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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
S. R. Jondhale et al., Received Signal Strength Based Target Localization
and Tracking Using Wireless Sensor Networks, EAI/Springer Innovations in
Communication and Computing, https://doi.org/10.1007/978-3-030-74061-0_1
Chapter 1
Fundamentals of Wireless Sensor
Networks
1.1
Introduction to Wireless Sensor Network
The WSN can be described as autonomous and self-organizing systems that consist
of a large number of tiny, low-cost, battery-operated sensor nodes (also called as
motes), which are generally randomly deployed either inside the phenomenon of
interest or very close to it [1, 2]. These motes are generally utilized to monitor envi-
ronmental and physical conditions, such as pressure, temperature, light, humidity,
fire detection, and chemical level [3–5]. These nodes can sense the environment
(data collection) and process and forward the processed data directly to the base
station (also called as sink) or via the other sensor nodes to the base station in order
to process it further as per application requirements. These sensor nodes in the WSN
are fitted with an onboard processor. Instead of forwarding the raw sensed data, sen-
sor nodes use their built-in processing capability to carry out simple computations
at local level and then transmit only the partially processed data to the nodes respon-
sible for the fusion with that obtained from other nodes. These computational capa-
bilities in WSN ensure a wide range of applications [3] [6–8]. For example, it is
possible to monitor the physiological data of a patient remotely by a doctor, which
saves a lot of time of patients as well as doctors. The WSN can also be used to locate
and/or detect pollution level as well as percent of toxic contents in the air and the
water. Thus, the WSN can provide the end user a better understanding of the envi-
ronment with intelligence. In 10–15 years it is not unreasonable to expect that the
large portion of the world will be covered with WSN with access to them via
Internet [3, 6].
A typical WSN model consisting of sensor nodes, sink, Internet connectivity, and
end user is shown in Fig. 1.1. Sensor field is nothing but an environment under con-
sideration, wherein the nodes are deployed to gather the information in it [5]. Each
of the nodes is capable of sensing, processing, and forwarding the sensed data to the](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-20-320.jpg){kind=tracking}
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requested nodes or to the sink. The sink and the sensor node may be static or mobile,
depending upon the application requirements. The sink can collect as well as pro-
cess the data from the sensor nodes. Generally, the sink is rich in memory, compu-
tational capacity, and energy as compared to the sensor nodes. The sink connects the
WSN to the end user with a terminal (such as computer) using an existing commu-
nication infrastructure such as Internet.
The concept of WSN can be described with the help of the following simple
equation [3, 5]:
Sensing CPU Radio Thousands of possible applications of WSN
+ + =
Thus, knowing the capabilities of the WSN, thousands of applications appear in
mind. Although it seems a straightforward combination of modern technologies,
combining sensors, processors, and radios into a coin-sized node requires a detailed
knowledge of both the capabilities and limitations of each of the underlying hard-
ware part, as well as fundamentals of distributed systems and modern networking
technologies [3, 5]. Each of the sensor nodes must be designed to encompass the set
of primitives required to formulate the network of nodes, while strictly attaining
requirements of cost, size, and power consumption. Thus, the major challenge is to
map the overall system requirements.
The WSN node may have different types of sensors interfaced, capable to moni-
tor a wide variety of ambient conditions. The type of sensor can be seismic, pres-
sure, thermal, magnetic, infrared, acoustic, and visual [3, 5]. The sensor nodes can
be deployed manually or randomly. Although each individual node has several
resource constraints in terms of memory, energy, computation, and communication
capabilities, their heavy deployment can collectively sense the surrounding environ-
ment, disseminate measurements, and process these experimental measurements.
That’s why the WSN applications range from environmental monitoring, real-time
tracking, to structural health of monitoring [3, 6–8]. Capability to real-time infor-
mation makes the WSN an ideal candidate for handling emergency, disaster relief
End user
Sink
Internet
Anchor Node
Sensor Field
Target
Wireless Sensor Networks
Sensor Node
Fig. 1.1 A typical WSN model
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-21-320.jpg){kind=tracking}
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operations, and military that need efficient coordination and planning. The WSN
can also be useful for instrumenting and controlling of offices, factories, vehicles,
homes, and cities. Any WSN-based application is useful only if the location of the
sensor node that provides the measurements is correctly known. In other words,
node localization is of prime importance to any WSN-based application [9]–[12]. In
order to get the node locations, an effective localization algorithm is needed. The
WSN-based system gets the updates of the location of node that provide useful
measurements; however, many times the estimated locations are not trustworthy
because of noisy measurements. Thus, in most of the situations, location estimates
are not accurate enough to claim that the underlying WSN-based application is
robust and reliable. That’s why the node localization has attracted tremendous atten-
tion of the researchers. The major objective of any localization algorithm is to
improve node localization accuracy (i.e., to reduce localization error). In recent
years, one of the major researches in WSN domain is on localization and tracking
(LT). Designing efficient LT algorithms becomes an important factor for the
success of any WSN-based application [9–12].
In this book we provide the fundamental aspects of WSN as well as a detailed
framework of WSN-based LT system right from concept to design. We cover
fundamentals of RSSI-based LT using WSN, simulated as well as real-time WSN-
based LT framework. A sincere attempt is made to provide the survey of the exist-
ing RSSI-based LT systems through a rigorous review of literature from the recent
papers of journals as well as conferences. This book is targeted to the managers,
communications developers, and practitioners, who wish to acquire the knowledge
of target LT and wish to implement WSN-based LT system to encompass broad
range of related applications.
1.2
WSN Versus Other Wireless Networks
The advancements in RF domain and rise in portable devices have accelerated the
use of mobile and wireless networking [13–15]. Because of wireless networking,
the users can electronically access data and services, irrespective of their physical
location [8]. Wireless technology-based networks are generally classified into two
categories, namely, infrastructure-based networks and infrastructure-less networks
(ad hoc networks). The former category has fixed the base station called access
points, which are connected by wires. The mobile node can communicate with the
base station via wireless link if it is inside the communication range of that base
station. If this mobile node travels out of the communication range of that base sta-
tion, then it tries to establish the connection with the other base station inside whose
communication range it currently is. Cellular phone system, paging systems, and
wireless local area networks (WLAN) are some of the examples of infrastructure-
based networks, whereas ad hoc networks do not have such predefined infrastruc-
ture and the nodes can move freely from one place to another, changing the network
topology continuously [3–5]. Mobile ad hoc network (MANET) and WSN are some
1.2 WSN Versus Other Wireless Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-22-320.jpg){kind=tracking}
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of the examples of ad hoc networks. These networks do not necessitate previous
setup or supporting infrastructure.
The MANET is a network of self-configurable, autonomous, self-organizing
nodes with wireless communication capabilities (especially multi-hop communica-
tion) [16]. It is generally adopted to meet the requirements of immediate communi-
cation need, where the deployment of wired infrastructure is not a feasible option.
For instance, MANET is used in situations such as battlefield, disaster relief opera-
tions, flood relief operations, and large construction sites. As compared to wired
infrastructure, the MANET can cover larger geographical areas. The WSN is a spe-
cial kind of ad hoc network, consisting of heavily deployed sensor nodes that can
cover a much wider geographical area as compared to the MANET [16]. As
described in the previous section, the sensor nodes in WSN are battery operated,
low cost, and small in size.
Some of the similarities between the WSN and MANET are:
• Both are distributed wireless networks with no requirement of previous infra-
structural setup.
• Nodes are deployed in an ad hoc manner in both.
• In most of the applications, the nodes communicate with each other using
multi-hop way.
• Both have concern over the minimization of power consumption due to use of
battery-powered nodes.
• Due to uses of unlicensed spectrum for operation, both are generally prone to
interference by other RF-based devices operating in the same frequency slot.
• Self-configuration is a must in both due to distributed nature.
In spite of many similarities between the WSN and the MANET, there are also
few key differences between them as listed below [3, 5, 6, 16]:
• The node in the WSN is generally of the order of several hundreds to thousands
as compared to the small number in the MANET. Thus, node deployment density
in the WSN is very high.
• Nodes in the WSN are prone to failure due to environmental and physical
conditions.
• Due to frequent node failures, WSN topology gets updated quite often.
• In most of the situations, the WSN use broadcast communication strategy,
whereas the MANET adopts point-to-point networking.
• The scarcity of resources is a common problem in the WSN (which means con-
straints of energy, computational abilities, and memory).
• The WSN nodes generally do not have global unique identification due to mass
(heavy) deployment.
• In majority of the applications, node mobility is comparatively low or nil in the
WSN as compared to that in the MANET.
• As compared to the MANET, the data rate in the WSN is very low.
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-23-320.jpg){kind=tracking}
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1.3 Sensor Node Architecture
Sensor nodes are designed and consisting of many components than just wireless
sensors. As mentioned earlier these sensor nodes can sense the physical parameter
of interests, process it, and dispatch the processed data to the base station. A sensor
node can be defined in the following way [3–5, 8]:
A sensor node is a type of transducer that senses one type of energy (field measurements)
and converts it into a suitable form (electrical form) for the purpose of data transfer to the
other sensor nodes. Furthermore, it possesses the ability to avoid the transmission of the
redundant data sensed from the surrounding environment (field measurements).
From a hardware perspective, the sensor nodes are small-scale processing units
with a variety of sensors interfaced to it. Typically the field measurements are tem-
perature, noise level, wind pressure, the presence of static or moving objects,
received signal strength indicators (RSSI), and so on [3–5, 8]. The type of sensors
interfaced with the node depends upon the underlying targeted application. Speaking
in more specific words, the sensor node typically has inbuilt processor (to process
the physical measurements received from the interfaced sensors), a battery (to
power it up), a memory (to store raw sensed or processed data), and a radio or com-
munication unit (for communication with the other nodes or external world). The
sensor network’s networking and communication abilities can be creatively
exploited to deal with specific underlying application. Sensor node architecture
with these four functional units is illustrated in Fig. 1.2 as shown below.
Fig. 1.2 Components of sensor node
1.3 Sensor Node Architecture](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-24-320.jpg){kind=tracking}
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1.3.1
The Power Supply
The power supply unit generally includes a nonrenewable coin-sized battery, whose
role is to supply power to all of the units of the sensor node [3–5, 8, 10]. Thus, bat-
teries are obviously energy storage devices, whose size ranges from small coin cell
to large lead-acid batteries of AA or AAA types. The rechargeable batteries are
generally not used in most of the WSN-based applications due to high cost, low
energy density, and impracticality of recharging option. If this battery is depleted,
the sensor node becomes nonfunctional. As in most of the WSN-based applications,
the sensor nodes are deployed in hostile environment and are generally inaccessible;
the sensor node lifetime mainly rely on the attached batteries.
In the sensor node, power is consumed for node activities, such as sensing, data
processing, and communication. Out of these node activities, the major part of
power consumption is observed for data communication. For instance, the power
consumption on transmitting 1 Kb data over a distance of say 100 m is approxi-
mately the same as that for executing approximately three million instructions by a
processor with a capability of 100 million instructions per second (MIPS) [3]. The
power consumption is a major design constraint of the WSN due to the limitation in
battery size. Thus, designing of power supply unit is a very crucial task in sensor
network design for an application. This design part may vary from application to
application. However, it is also possible to power the network and extend the WSN
lifetime by extracting energy from the environment by the usage of solar cells.
1.3.2
The Sensing Unit
This unit generally consists of physical sensors, which are capable of sensing the
physical parameter of interest [3]. It also contains an analog-to-digital converter
(ADC) to transform sensed data into digital form. Sensor is a transducer, which
converts a change in a physical phenomenon into a measurable electrical signal.
Sensors measure physical conditions such as temperature, humidity, light, pressure,
sound, chemical level, magnetic fields, and etc. The sensor converts analog signal
into digital signal using ADC, which is then fed to the processor for further required
processing. A sensor node is generally tiny in shape and requires low power con-
sumption and operates unattended. A sensor node may have several types of sensors
connected to the node.
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-25-320.jpg){kind=tracking}
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1.3.3
The Processor Unit
The processing unit in a WSN node consists of a suitable embedded processor for
processing the digital data obtained from ADC unit [3]. The processor can execute
various tasks, such as processing of input data and controlling the working of other
components of the node. The processing unit generally has a microcontroller to
execute all of the mentioned tasks; however, in some of the applications, it may
consists of digital signal processor (DSP) and field-programmable gate array
(FPGA). The microcontroller is a more preferred option due to low power consump-
tion and low cost involved as well as flexibility of interfacing with other devices and
ease in programming. The common microcontrollers that are used in sensor nodes
areAtmelATmega128 series controllers,ARM microcontrollers, Texas Instruments’
MSP 430, and Microchip’s PIC. The more complex the application, the more
advanced microcontroller is preferred in the sensor node to meet the application
requirements [3–5, 16].
The processing unit also contains a memory unit for storage of the processed
data and algorithms of the underlying application. The memory unit consists of on-
chip flash memory, internal RAM, and external flash memory. For instance, Mica2
mote is based on ATmega128L microcontroller, which has 4 Kb static RAM and
128 Kb flash program memory [3].
Though it is the era of modern powerful and tiny processors, the power (energy)
and memory of the sensor node are still considered as scarce resources. Some of the
typical tasks executed by the processing unit are:
• Control, signal processing, and actuation.
• Data aggregation.
• Compression, clustering, forward error correction, and encryption.
• Data fusion and data analysis.
1.3.4
The Communication Unit
The communication unit consists of a wireless radio transceiver. For collaborative
processing, the sensor nodes frequently need to exchange the data with the neigh-
boring nodes. The transceiver can convert the digital bit stream received from the
microcontroller into RF waves or RF waves into an equivalent digital bit stream [3,
16, 17]. Thus, the sensor node can communicate with the external world (other
nodes) through interfaced transceiver. The transmission media for communication
between nodes can be RF, optical, or infrared. Communications using lasers need
less energy; however, they require LOS for communication, and additionally they
are sensitive to atmospheric conditions. Like lasers the infrared does not need
antenna; however, its broadcasting capacity is limited. The RF communication gen-
erally involves various important operations, such as modulation and demodulation,
filtering, and multiplexing. These operations make sensor node communication
1.3 Sensor Node Architecture](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-26-320.jpg){kind=tracking}
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highly complex and expensive as compared to other operations of the sensor node.
Additionally, the signal path loss during the communication between two commu-
nicating sensor nodes has exponential relation with the distance between them, as
the sensor node antennas are usually close to the ground. In spite of the high com-
munication cost involved, the RF-based communication is widely preferred in the
WSN-based applications [9–12, 18, 19]. The reason behind this is that the data rates
are low and packets are small in RF communication. One more advantage with RF
communication is the possibility of frequency reuse due to shorter communication
lengths. The transceiver has four operational states, namely, receive, transmit, sleep,
and idle. The power consumption in idle mode is almost equal to that in the receive
mode. Therefore, if the transceiver is not transmitting or receiving, it’s better to shut
down it completely rather than leaving it in the idle mode. Another important aspect
to note down is that significant power consumption occurs during switching; there-
fore, unnecessary switching between states needs to avoided. The popular Mica2
mote uses two kinds of RF transceivers, namely, Chipcon CC1000 and RFM
TR1000. The transmission range of Mica2 is around 150 m [3]. Some of the domi-
nant wireless standards used for communication by the sensor nodes are:
• IEEE 802.15.1 PAN/Bluetooth
• IEEE 802.15.3/UWB
• IEEE 802.15.4/ZigBee
• IEEE Wi-Fi
1.3.5
Location Finding Unit
As discussed earlier the sensor node positioning is important in any WSN-based
application. In WSN locations of few nodes are prefixed (such nodes are called as
anchor nodes), whereas the remaining nodes are randomly deployed in the environ-
ment and are termed as non-anchor nodes [20–23]. That means locations of the
non-anchor nodes are unknown. Since sensor nodes are generally deployed ran-
domly and run unattended, they need to corporate with a location finding system.
The location finding unit in the sensor node architecture is optional. If it is present
in the sensor node, then it contains a Global Positioning System (GPS) to estimate
the location of the node. It is often assumed that each sensor node will have a GPS
unit that has approximately 5 m accuracy [24–27]. Equipping all sensor nodes with
a GPS is not a viable solution in the WSN due to the cost involved. The possible
solution to this is to interface GPS to anchor nodes and then locate the non-anchor
nodes with the help of anchor nodes by executing a suitable localization algorithm.
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-27-320.jpg){kind=tracking}
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1.4
Sensor Network Communication Architecture
The overall working of the WSN can be explained using the protocol stack as elabo-
rated in Fig. 1.3 [3, 5]. The protocol stack includes five layers, namely, physical
layer, network layer, data link layer, transport layer, and application layer. Based on
the sensing tasks, a variety of application software may be built and run on the
application layer. The transport layer is responsible to maintain the data flow
between sensor nodes. The network layer monitors the routing of the data provided
by the transport layer. Minimizing the collision with neighbor nodes during broad-
cast is the main task of the data link layer. The physical layer deals with modulation,
data transmission, and data receiving techniques for the WSN.
Apart from these layers, the three management planes associated with the proto-
col stack are task plane, power plane, and mobility plane (see Fig. 1.3). These three
planes monitor the power, movement, and task distribution among the WSN nodes.
These three planes assists the sensor node in lowering the overall power consump-
tion and coordinating the sensing task. The power plane takes care of efficient and
effective utilization of power among sensor nodes during operation of the network
as a whole [3] [5]. For instance, the sensor node turns off its receiver in order to
avoid duplication of data. Let’s consider another case wherein the power level of a
sensor node is low. In such critical situation such sensor node may broadcast to its
neighboring nodes that it has low power and can’t participate in data routing. In
other words, this node will reserve the remaining power only for sensing. The
mobility plane is responsible for registering and detecting the movement of sensor
Fig. 1.3 Wireless sensor network protocol stack
1.4 Sensor Network Communication Architecture](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-28-320.jpg){kind=tracking}
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nodes. That’s why each sensor node can keep a track on the movement of its neigh-
boring nodes. By the knowledge of the neighboring nodes in advance, the sensor
nodes can maintain a balance between its task and power usage. The task plane is
responsible to balance and schedule the sensing tasks for a specific region in the
given monitoring environment. Thus, there is no need for the sensor nodes to sense
the environment at the same time. In other words, only those sensor nodes, which
have sufficient power level, will perform the sensing tasks. Thus, all of these man-
agement planes are essential for the sensor network to route the data effectively in
the network, to achieve power efficiency, and to marshal resources among the net-
work nodes [3, 5]. That means without these three management planes, a sensor
node could just work individually without a concern about the rest of the network.
For the sensor network as a whole, it will be highly advantageous if the sensor nodes
in the network can collaborate with each other in order to prolong the lifetime of the
sensor networks.
1.5
Design Constraints for WSN
The WSNs are characterized by a very powerful combination of distributed sensing,
computing, and communication. Despite the tiny size of an individual WSN node, it
faces numerous challenges such as stringent power constraints, limited communica-
tion range, computing power, and storage space of the sensor nodes [3–5, 10, 28].
The major reason for these constraints is the small physical size of the sensor nodes.
The primary objective of the WSN is to execute the task of data communication
(routing) while trying to extend the network lifetime as high as possible by employ-
ing energy-efficient techniques. Some other operating challenges include high error
rates, low bandwidth, noisy measurements, sleep scheduling of sensor node, scal-
ability to a huge amount of sensor nodes, survivability in dynamic environments,
breakdown of wireless communication link, and frequent node failure. The follow-
ing section discusses some of the important design issues and challenges that affect
data routing in WSNs.
1.5.1 Power Consumption
As discussed earlier the sensor nodes are generally battery powered and are gener-
ally deployed in remote or inaccessible environments [3, 6–8]. Replacing or recharg-
ing the batteries in such environment is almost impossible. The power is a mandatory
aspect for almost all of the operations in the WSN. In general, the power consump-
tion in sensor nodes is observed at three places: (a) power consumption by sensing
unit, (b) power consumption by communication unit, and (c) power consumption by
processing unit. Therefore, the power consumption is one of the major concerns in
the WSN-based applications [16, 20, 29, 30]. It is observed that a single bit
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-29-320.jpg){kind=tracking}
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transmission in the WSN consumes the same power as that for executing approxi-
mately 800–1000 instructions. Thus, the power consumption in radio is much higher
than that in sensing and computation.
From the architectural point of view, the use of low-power antenna circuitry must
be chosen to reduce power consumption. Low power consumption is a key to suc-
cess in any WSN-based application. That’s why a lot of research has been going on
in the WSN community to develop energy-efficient algorithms for routing, localiza-
tion, and other tasks, which will consume less power [16, 20, 29, 30]. Parallel to
this, continuous research is going on to extend sensor node lifetime despite its bat-
tery-dependent working. Power efficiency in the WSN can be accomplished in
three ways:
1. Low-duty-cycle operation.
2. Local/in-network processing to reduce data volume and in turn transmission time.
3. Multi-hop communication reduces the requirement for long-range transmission.
4. Each node in the WSN can act as a repeater, thereby reducing the communica-
tion link range coverage.
1.5.2 Memory
The sensor node generally has a very small amount of memory in the processing
unit for the storage of data and algorithm [3–5]. This memory is in the form of RAM
and ROM of processor of the sensor node. Due to the limited memory capacity of
the sensor node, there does not exist enough memory to execute complex algorithms
especially after loading the OS. For instance, consider the case of Smart Dust proj-
ect. In this project it is found that TinyOS consumes around 4 Kb for instructions,
leaving only 4.5 Kb for applications [3].
1.5.3
Deployment, Topology, and Coverage
Depending on the application requirement, the nodes in the WSN can be placed in
a planned fashion or in a random fashion [20, 31–33]. The node deployment in the
monitoring area can be a periodic or a one-time activity. Node deployment has
impact on important network parameters, such as coverage, node density, reliability,
sensing resolution, communications, and task allocations. The WSN generally oper-
ates in dynamic environment due to uncertainty in operating conditions, e.g., due to
abrupt changes in the environmental setup, node mobility, and node failures. Due to
such dynamicity in the operating environment, the communication links between
sensor nodes frequently break even when nodes are static. Another disadvantage of
this dynamicity is frequent changes in the WSN topology, which in turn affects
many network characteristics such as robustness, latency, and capacity. The level of
1.5 Design Constraints for WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-30-320.jpg){kind=tracking}
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complexity in data routing and processing also depends on the network topology.
Coverage is a measure of coverage area of a WSN. It can be sparse, i.e., only parts
of the environment fall under the sensing envelope, or dense, i.e., most parts of the
environment are covered. Coverage can also be redundant, i.e., the same physical
space is covered by multiple sensors. Coverage is mainly determined by the sensing
resolution demands of an application.
1.5.4 Communication and Routing
As the WSN generally has limited bandwidth, processing, and energy, it operates in
highly uncertain, remote, and hostile environments [18, 34]. Therefore, the network
continuously undergoes changes in its topology and coverage due to frequent node
failures and noisy measurements. Due to very heavy deployment, its nodes lack
global identification as well. Thus, data routing is a very critical issue in such condi-
tions. Therefore, designing appropriate routing scheme highly depends upon the
underlying application requirement. Popular WSN routing schemes are sensor pro-
tocols for information via negotiation (SPIN), constrained anisotropic diffusion
routing (CADR), active query forwarding in sensor networks (ACQUIRE), low-
energy adaptive clustering hierarchy (LEACH), power-efficient gathering in sensor
information systems (PEGASIS), and threshold-sensitive energy-efficient sensor
network protocol (TEEN) [3–5, 8].
1.5.5 Security
Sensor networks are vulnerable to several key attacks. Most popular are eavesdrop-
ping (adversary manages to listen data and communication), denial-of-service
attacks (a particular node denies to execute the network tasks), Sybil attack (mali-
cious nodes manage to get multiple identities to disrupt routing, resource allocation,
and data aggregation), physical attacks (adversary manages to sensor node tamper-
ing), and traffic analysis attacks (adversary manages to reconstruct network topolo-
gies) [7, 18, 35]. Therefore, network security is a very essential aspect in the WSN,
especially if it deployed in enemy prone or secret environment. Continuous research
is going on to propose appropriate defenses to protect the sensor networks against
attacks. Speaking in more technical words, the security in the WSN refers to ensure
three important data centric aspects:
1. Data confidentiality: It means an adversary must not be able to steal and inter-
pret data.
2. Data integrity: An adversary must not be able to alter or damage data.
3. Data availability: An adversary must not be able to disturb data communication
link between source nodes and sink node of the WSN.
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-31-320.jpg){kind=tracking}
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1.5.6 Production Costs
As we know the WSN generally consists of several hundreds or even thousands sen-
sor nodes [3, 4, 8]. Therefore, the cost of a single node is crucial to decide the over-
all cost of the WSN. If deploying the WSN is costlier than deploying traditional
sensors, then the WSN is not at all cost justified. Therefore, the cost of each sensor
node must be as low as possible for the sensor network to be feasible. Now a day,
due to advancement in Bluetooth technology, the cost of a sensor node is around
only 1–2$.
1.5.7 Fidelity and Scalability
Scalability broadly refers to how well all the operational specifications of a sensor
network are satisfied with a desired fidelity, as the number of nodes grows without
bound [3, 4, 8]. Based on the operating environment and the phenomenon to be
observed, fidelity can cover various performance parameters, such as spatial and
temporal resolution, misidentification probability, consistency in data transmission,
latency of event detection, and event detection accuracy. Depending on the measure
of fidelity, scalability can be formulated in terms of reliability, network capacity,
energy consumption, resource exhaustion, or any other operational parameter as the
number of nodes increases. Thus, there exists high level of trade-off between scal-
ability and fidelity. Therefore, one has to decide scalability and fidelity for the
designed sensor network, depending upon the application requirement.
1.6 Existing WSN Platforms
History of design and deployment of the WSN dates back to the World War II [3, 4,
8]. A platform of acoustic sensors was developed by the USA to detect and track
Soviet submarines for sound surveillance. It is currently used by the National
Oceanographic and Atmospheric Administration (NOAA) for detecting and moni-
toring events, such as seismic and animal activity in the ocean. In 1980, the research
on the WSN-entitled distributed sensor networks (DSN) was carried out at DARPA
(Defense Advanced Research Projects Agency). The network consisted of many
spatially distributed, low cost, autonomous sensing nodes that collaborate among
each other for data routing. A number of such research attempts on the design and
development of the WSN have been reported in the history. At present there is no
such common WSN platform to be used for a specific application. The platform of
Berkeley motes and their variants have wider user and developer communities. It is
quite less expensive to build our own WSN platform for intended application in
mind than to buy commercially available platforms. Therefore, a popular trend to
1.6 Existing WSN Platforms](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-32-320.jpg){kind=tracking}
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design and produce own WSN setup has been established for the last two decades
among many researchers, RD labs, and commercial companies prefer. Some of
these research attempts and related projects are explained in the following sections:
1.6.1 Wins
The University of California in association with the Rockwell Science Center devel-
oped wireless integrated network sensors (WINS) project, which was later on com-
mercialized with the Sensoria Corporation (San Diego, California) in 1998 [3, 4, 8].
This project covered almost all the aspects in the WSN design right from MEMS
sensor and transceiver integration at the circuit level, network protocol design, and
signal processing architectures to the fundamentals of its sensing and detection
theory. The project concluded that WINS would provide distributed networking and
Internet accessibility to sensor nodes, task controls, and adding embedded proces-
sors with the node.
1.6.2 Eyes
The Infineon developed energy-efficient sensor networks (EYES). This project was
funded by the European Union (EU) to design and develop the technology and
architecture of wireless sensors that can be networked with large number of mobile
nodes [3, 4, 8]. The project eyed at supporting devices such as PDAs, laptops, and
even mobile phones. The developed sensor nodes are equipped with a TI’s MSP430
processor, SAW filter, radio device TDA 5250, and transmission power control.
Each sensor node has a USB port for interfacing to a PC. These sensor nodes also
have provision to add extra sensors as well as actuators, depending upon the appli-
cation demand.
1.6.3 Pico-Radio
In 1999, the Pico-Radio project started at the University of California to support the
development of low-cost, low-energy sensor nodes with ad hoc capability. The pro-
posed for the Pico-Radio network has physical layer with direct sequence spread
spectrum and the MAC protocol with the application of carrier sense multiple access
(CSMA) and spread spectrum techniques [4–7]. The important findings of this proj-
ect are as follows: (1) The node can randomly select a channel and monitor the
network activity. (2) If the channel is currently engaged, the node can search for
another channel from the list of the remaining available channels. Once an idle
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-33-320.jpg){kind=tracking}
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channel is detected, the scanning is stopped. (3) In case the idle channel is not
found, the node would back off and set a random timeout timer for each channel. (4)
It can then use the channel which has first expired timer. Then, the timers for the
other channels are cleared off.
1.6.4
Mica Mote Family
The sensor nodes of Mica mote family are developed at the University of California,
Berkeley. This project started in partial collaboration with Intel in the late 1990s.
These sensor nodes are commonly referred as Mica motes, with different variants
such as Mica, MicaZ, Mica2, and Mica2Dot, which are commercially sold via the
Crossbow company [3, 4, 6, 7]. The OS in these products is TinyOS. The TinyOS is
coded in the nesC language with a component-based protocol. The Mica motes use
a processor from Atmel family (usually ATmega128L 8-bit processor running at
7 MHz) and a radio modem from RFM (usually it is TR 1000). In Mica motes sen-
sors are interfaced to the controller using I2C or SPI protocols. Power to Mica
motes is provided via two AA batteries of current capacity of 2000 mAh. The
Chipcon transceivers are generally employed in Mica motes. For instance, in Mica2
mote has Chipcon CC1000 transceiver, which operates on the 868/915 MHz band
with data rate of 38.4 kbps. In MicaZ the Chipcon CC2420 transceiver operating in
unlicensed 2.4 GHz band with data rate of 250 kbps is used. It uses offset quaternary
phase-shift keying (O-QPSK) as a modulation technique.
1.7 Applications of WSN
Due to the continuous development in the WSN technology, the assets of national
importance such as aircrafts, ships, and even buildings can detect structural faults on
time (this application area is popularly known as structural health monitoring) [7,
26, 36–38]. The WSN also has paved a way to design and develop systems that
provide useful prior alerts before earthquake and tsunami. The WSN also has exten-
sive applications in the battlefield for surveillance and reconnaissance. The WSNs
can be used in critical applications such as earthquake, tsunami, battlefield, and
flood and also in enemy intrusion detection, target tracking, forest fire detection,
industry monitoring, structural monitoring, and environmental and biological moni-
toring. Although it covers a broad range of diverse application areas, few of them
are described below:
1.7 Applications of WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-34-320.jpg){kind=tracking}
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1.7.1 Military Applications
The sensor network research was originally motivated from the military needs [3, 4,
6, 8]. The demand in military-based applications includes energy conservation,
rapid deployment of assets, as well as robust sensing along the rivers and in hostile
terrains. The typical military applications are listed below:
• Monitoring and tracking enemy forces and monitoring friendly forces
• Monitoring equipment and inventory
• Reconnaissance
• Surveillance of war area
• Assessment during war damage
• Nuclear, biological, and chemical attack detection
• National border monitoring
1.7.2 Environment Monitoring Applications
The WSN has been proved to be the ideal choice for many environment monitoring
applications due to its capability of unattended operation. The typical environment-
related applications are listed below:
• Weather sensing and monitoring stations
• Forest fire detection
• Habitat monitoring
• Monitoring pollution level of water, land, and air
• Flood detection
• Precision agriculture
• Endangered species population measurement
• Tracking migrations of bird and endangered wild animals
• Soil erosion detection
1.7.3 Health Applications
Many times humanly monitoring of patients or medical equipment during complex
surgery in big hospitals is impossible [39–41]. The wireless sensors in such situa-
tion can assist the doctors and hospital administration to execute various tasks accu-
rately and appropriately. The typical health systems-related applications wherein
the WSN is involved are listed below:
• Physiological data monitoring remotely
• Locating and tracking of patients and doctors inside a hospital
• Administrating drug remotely
1 Fundamentals of Wireless Sensor Networks](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-35-320.jpg){kind=tracking}
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• Assistance to elderly people
1.7.4 Home Applications
The improvement in the quality of life by creating secure and intelligent living
spaces for humans is the underlying idea behind smart homes [39, 42, 43]. The
WSN finds a huge potential for applications in the area of smart homes. The typical
home automation-related applications are listed below:
• Home automation
• Instrumented environment
• Automated meter reading
• Tracking system for child and elderly people
1.7.5 Other Commercial Applications
Sensor networks are also proved to be highly useful in some of the commercial
applications of national importance [39, 42, 43]. In commercially important appli-
cations, the WSN can not only provide reliable measurements using which localiza-
tion of important entities can be done efficiently.
• Monitoring nation’s critical resources such as power industrial plants, tunnels,
communication grids, and parks
• Ambient temperature control in office and industrial buildings
• Inventory management and control
• Landslide detection systems
• Vehicle tracking and detection
• Traffic flow surveillance on highways
• Air traffic control stations
References
1. Y. Zhang, L. Sun, H. Song, X. Cao, Ubiquitous WSN for healthcare: Recent advances and
future prospects. IEEE Internet Things J. (2014). https://doi.org/10.1109/JIOT.2014.2329462
2. S. Gezici et al., Localization via ultra-wideband radios: A look at positioning aspects
of future sensor networks. IEEE Signal Process. Mag. (2005). https://doi.org/10.1109/
MSP.2005.1458289
3. I. F. Akyildiz, T. Melodia, K. R. Chowdhury, A survey on wireless multimedia sensor net-
works. Comput. Netw. 51(4) (2007). https://doi.org/10.1016/j.comnet.2006.10.002
References](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-36-320.jpg){kind=tracking}
![21
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
S. R. Jondhale et al., Received Signal Strength Based Target Localization
and Tracking Using Wireless Sensor Networks, EAI/Springer Innovations in
Communication and Computing, https://doi.org/10.1007/978-3-030-74061-0_2
Chapter 2
Target Localization and Tracking Using
WSN
2.1
Introduction to WSN-Based LT
Indoor LT of target is useful for many applications in several sectors, such as the
manufacturing, sports, healthcare, and construction [1–6]. For instance, in the
healthcare sector, locating and tracking the locations of objects can be very crucial
whenever and wherever there is high emergency to respond to. For instance, in hos-
pitals many employees need to share the same hospital assets during work. These
items many times are moved from their regular location and not returned to the
original location after the work is finished. In the manufacturing sector, the knowl-
edge of location of the finished products and other items in a warehouse can help in
asset management by keeping a track on the inventory and lowering the searching
time to find them. The person unfamiliar with the given built environment (e.g., a
large building) can be provided with location map of the area in order to search out
the routes toward intended destination. Critical information objects (e.g., objects in
museums or computer hard-drives) or high-valued assets can also be exposed to
possible thefts. In other words, location knowledge can provide theft detection as
well as prevention by giving useful alerts about whenever they are shifted outside
from the predefined boundaries by some unauthorized persons.
Target LT is one of fundamental applications of the WSN, wherein the main
objective is to detect and locate the mobile target and also to keep a track on its
movement path (trajectory) continuously with the help of field measurements from
sensor nodes [7–10]. This is termed a single target LT problem; although if the
problem involves LT of multiple mobile targets with the help of WSN-based
setup, then it is termed as a multi-target LT problem. The low maintenance cost,
simple and random deployment procedure, ad hoc nature, and the possibility of
unattended operation make WSN a vital option for various indoor LT applica-
tions. The WSN can easily locate and track the trajectory of the moving target by](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-39-320.jpg){kind=tracking}
![22
simply exploiting field measurements once deployed randomly. In these problems,
the sensor nodes are deployed at random or predefined locations in the sensing
environment.
Consider a general target LT scenario using WSN as shown in Fig. 2.1, wherein
the target is moving inside the WSN monitored area along a predefined or unknown
path. The sensor nodes in the WSN based LT system are designated as detecting
nodes (the nodes which are in the vicinity of the mobile target and able to detect the
target), vigilant nodes (the nodes which are likely to detect the target in the future),
and inactive nodes (which are not at all utilized in the LT process). The mobile
target can be any object, such as asset, an animal, an intruder, a vehicle, or a person
[7–10]. Figure 2.2 shows the basic procedural steps executed in target LT mecha-
nism. It consists of detection of a target during its motion in the WSN monitored
area, localization to locate the mobile target, and tracking to trace the route of
mobile target.
The WSN-based LT may also be classified as single target vs multiple target
LT, active vs passive LT, indoor vs outdoor LT, and two-dimensional (2-D) vs
three-dimensional (3-D) LT [7, 11–14]. If the target cooperates in localization,
then it is termed as active LT; otherwise, it is called as passive LT. In the former
case, the target is with a sensor node, and the rest of the WSN nodes can detect and
locate the target. In the latter case, the target is “device-free,” wherein the target is
not equipped with a WSN node. This book is intended to discuss the design and
development of LT algorithms to efficiently track a single mobile target in an
indoor environmental setup by exploiting field measurements.
Fig. 2.1 General target LT scenario using WSN
Target
Field Measurements
Detection of
Target
Localization
of Target
Tracking of
Target
Fig. 2.2 General mechanism of target LT
2 Target Localization and Tracking Using WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-40-320.jpg){kind=tracking}
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The dramatic technological revolution in smartphones, wearable wireless
devices, and WSN in the last decade has come up with a wide variety of useful
applications, including indoor LT applications [7, 11–14]. Indoor LT is the pro-
cess of achieving user location, which can be utilized in a wide range of applications
in health sector, disaster management, smart home, and surveillance. It is also
proved to be beneficial in many important areas, such as smart cities, smart struc-
tures, and smart grids. In the context of the WSN-based LT for the indoor setup,
there are two types of sensor nodes, namely, anchor nodes (also called as reference
nodes) and non-anchor nodes [7]. Generally, the anchor nodes are deployed at
known locations, whereas locations of non-anchors nodes are unknown. The mov-
ing target is assumed to carry one non-anchor node. The target locations during its
movement are estimated with the help of anchor nodes through internode commu-
nications. However, environmental issues, such as signal fading, multipath propaga-
tion, and non-line of sight (NLOS), pose the major challenges in achieving high
tracking accuracy. The WSN-based tracking systems must also be robust enough to
deal with abrupt variations in target velocity as well as variation in target mobility
pattern. Therefore, the researchers from academia and industry need to propose
efficient LT algorithms with reference to the challenges mentioned above.
2.1.1
Typical LT Scenario in Wireless Sensor Networks
The target LT in an indoor environment using WSN enables a wide variety of
applications [1–4]. As discussed earlier, the mobile target can be any object, such as
an asset, an animal, an intruder, a vehicle, or a person. Sometimes the mobile target
moves along a predefined path, and sometimes the target path is unknown. A typical
scenario of target LT using WSN is shown in Fig. 2.3. The target state at any time
instance k can be given by the state vector X x y x y
k k k k k
= ( )
, , ,
’
, wherein xk and yk are
the target locations and
xk and
yk are the target velocities in x and y directions, respec-
tively. One may augment acceleration parameters xk
¨
and yk
¨
along x and y directions,
respectively, in the above the state vector. During the target motion in the WSN, the
state vector changes. The objective of the deployed WSN is to estimate continuously
the state vector using field measurements from the environment with the help of a
suitable LT algorithm [11, 15–18]. Thus, for the case of mere target localization, it
is one time estimation problem, whereas for the case of target tracking, it is a sequen-
tial state estimation problem. That means the algorithms that are used for target
tracking problem are the same as that for the target localization problem. At the end
of a state vector estimation, we are interested to know about how the LT algorithm
performed in the context of target LT for the considered system design and assump-
tions. The performance evaluation parameters that are generally used for target LT
are localization error, RMSE, or both. The lower the values of these performance
evaluation parameters, the higher will be the target LT accuracy.
As discussed in the last paragraph, the target tracking being a sequential localiza-
tion problem, it needs the location estimation algorithm of recursive nature [16–19].
2.1 Introduction to WSN-Based LT](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-41-320.jpg){kind=tracking}
![24
One may call this recursive location estimation algorithm as the target tracking algo-
rithm. Several factors that impact the performance of the target tracking algorithm
are the following: the type of environment (indoor/outdoor), type of field measure-
ment involved, density of obstacles in the environment, algorithmic design, and den-
sity of anchor and non-anchor nodes.Apart from these system design issues, the field
measurement also faces the problem of signal propagation issues, such as signal
fading, reflections, NLOS conditions, and multipath propagation. Therefore, to
develop a robust and high precision target LT system for an indoor environment is
a highly challenging task. Due to such environmental dynamicity, the existing target
LT systems suffer with a low LT accuracy (i.e., if localization error is higher than
1 m, then it can be considered as low LT accuracy). In addition to issues of system
design and environmental dynamicity, some other issues, such as abrupt changes in
target velocity, during motion and availability of less field measurements can also
deteriorate the performance of the LT algorithm further. Therefore, research has
been continuously going on to design and develop robust and accurate target LT
systems, which can offer higher target localization accuracy (i.e., localization error
lower than 1 m), real-time performance, and lower computational simplicity.
2.1.2
Classification of Target LT Techniques
As discussed several times previously, the WSN utilizes the field measurements to
locate the mobile target. Based on the involvement of distance of the target from the
anchor nodes in the computation or estimation of the unknown locations of the
mobile target during its motion, the LT algorithms can be divided into two major
classes: range-based LT and range-free LT as shown in Fig. 2.4 [9, 11, 20]. If
the target LT algorithm depends upon the distance (range) between target and
Fig. 2.3 Typical target tracking scenario using WSN
2 Target Localization and Tracking Using WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-42-320.jpg){kind=tracking}
![25
anchor nodes during estimation, then it is termed as range-based algorithms; other-
wise, it is termed as range-free algorithms. Unlike range-based approach, in the
range-free approach the connectivity of sensor nodes is utilized to locate the moving
target rather than the distance between the target and anchor node. The target LT
accuracy of range-based algorithms is generally high as compared to its counterpart
range-free algorithms. However, looking from hardware perspectives, the range-
free algorithms require additional hardware when compared with range-free algo-
rithms. The overall comparison between range-free and range-based approaches is
presented in Table 2.1.
The range-based approach utilizes field measurements, such as the time of arrival
(ToA), angle of arrival (AoA), received signal strength indicator (RSSI), and time
difference of arrival (TDoA) [7, 21]. In the AoA, the angles of arrival of signals
between target and anchor nodes are utilized to locate the moving target. Although
the AoA technique does not need clock synchronizations between transmitters and
receivers, the need of an array of directional antennas is its main limitation. In the
ToA-based LT approach, the signal propagation velocity and the time of arrival of
the transmitted signal are exploited to calculate the distances from transmitter to
receiver, whereas in the TDoA-based approach, the time difference of arrival of sig-
nals coming from the transmitter and receiver is utilized. The major drawback of
TDoA and ToA techniques is the need of exact time synchronization between the
transmitter and receiver clocks, the susceptibility to NLOS conditions, interferences,
and measurement noise. The additional hardware requirements in AoA, ToA, and
TDoA range-based techniques make the LT system expensive and bit complex.
Unlike AoA, ToA, and TDoA range-based LT approaches, in the RSSI-
based LT
approach, there is no such requirement of additional hardware for the target LT. In
the RSSI LT approach, the distance between the target and anchor nodes using a
suitable path loss model is utilized to locate the target. The prerequisites for the path
loss model are knowledge of the transmitted and received signal powers, transmitting
Fig. 2.4 Classification of WSN-based LT
2.1 Introduction to WSN-Based LT](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-43-320.jpg){kind=tracking}
![26
and receiving antenna gains, and operating frequency. The pros and cons associated
with these field measurements are given in detail in Table 2.2.
The range-free techniques are classified as hop count-based technique (e.g., DV
Hop) and pattern matching-based technique (e.g., approximate point in triangle
(APIT)) [11, 20, 22, 23]. Basically, these both approaches are area-based methods.
In DV-Hop-based LT approach, the unknown location of node (or target) is com-
puted by counting the number of hops the RF signal takes to reach the destination.
In APIT-based LT approach, the information such as whether the node (or target)
is within a predefined area or not is utilized. These both approaches do not provide
the exact location of the target; instead of that, they provide the area in which the
target is. An artificial neural network (ANN) can be used in both range-based and
range-free methods. As this book is intended to discuss the fundamentals of the
RSSI-based target LT approach only, the rest of the other approaches are out of
the scope of this book. The detailed discussion of the RSSI-based target LT
approach is discussed in detail in the next section.
Table 2.1 Comparison between range-based LT and range-free LT
Parameters Range-based approach Range-free approach
Additional hardware Required Not required
Localization accuracy Approximately 80–90% Approximately 60–75%
Power consumption High Low
Robustness High Low
Deployment Generally hard Generally easy
Table 2.2 Types of measurements involved in WSN-based target tracking
Measurement
type Procedure Pros Cons
ToA Distance-
based
Moderate accuracy Need for transmitter and receiver clocks
and their perfect synchronization; errors
due to NLOS conditions, signal noise,
and interferences
TDoA Distance-
based
High accuracy Need for transmitter and receiver clocks
and their perfect synchronization; errors
due to NLOS conditions, signal noise,
and interferences
AoA Angle-
based
High accuracy Requirement of directional antenna array
RSSI Distance-
based
No need for additional
hardware, low cost, and
low power consumption
RSSI measurements are susceptible to
environmental dynamicity and moderate
accuracy
2 Target Localization and Tracking Using WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-44-320.jpg){kind=tracking}
![27
2.2 RSSI-Based Target LT Approach
The RSSI is basically the measure of the magnitude of power received at the receiver
terminal. The RSSI measurements during RF communication are obtained very eas-
ily at the receiver during normal communication [11, 15, 16, 24, 25]. As discussed
in the previous section, the LT system based on the RSSI measurements neither
needs an array of directional antennas nor needs synchronization between the
receiver and transmitter clocks. Each wireless sensor node is with on-chip RSSI
circuit, which can give the values of RSSI measurements. Thus, there is no need for
additional hardware in the RSSI-based target LT approach. Hence, the RSSI met-
ric has been dominantly used in the WSN-based target LT systems. Compared to
other counterparts, few other important advantages associated with the RSSI-based
LT approaches are as follows: simpler procedural aspects and lower power
consumption.
Theoretically speaking, the RSSI is a function of distance between the receiver
and transmitter and the RF environment, in which the WSN or other wireless system
is deployed. Therefore, due to the dependence on the RF channel, the RSSI-based
LT algorithms are generally affected by changes in the environmental setup [11,
15, 16, 24, 25]. In fact, in the RSSI-based approach, the distance between the
receiver and the transmitter is computed using the difference between magnitudes
of transmitted power and that of received power. This power difference is termed as
signal attenuation or path loss. Therefore, the utmost care is to be taken to choose
an appropriate path loss model to characterize the given RF channel. Speaking in
more technical words, the RSSI is a part of the IEEE 802.11 protocol family. The
RSSI values are measured in dBm unit. The RSSI values generally fall between
0 dBm (excellent signal) and −110 dBm (very poor signal) and are negative [26,
27]. In the indoor LT applications with WSN, the RSSI measurement-based
approach is generally used as compared to the rest of the alternatives. In these appli-
cations for indoor environmental setup, the aspects that are of prime importance to
the success of the underlying application are the following: selections of path loss
model, density and locations of non-anchor and anchor nodes, selection of suitable
transmission power level algorithmic design, and issues related with signal propa-
gation, such as fading, reflections, NLOS conditions, and multipath propagation
[15, 16, 19, 28, 29].
In most of the RSSI-based indoor LT applications, it is assumed that the target
carries one sensor node, which is configured in the transmitter mode, whereas the
rest of the sensor nodes of the WSN are configured in the transceiver mode. All the
RSSI-based target LT algorithms discussed in this book from Chap. 4 onward are
based on this assumption, although some applications in the literature also assumes
the target configured in the receiver mode to collect the measurements from the sur-
rounding sensor nodes. In the first case, the target broadcasts RF signal in the sur-
rounding WSN environment, while the rest of the sensor nodes in the network
collect the RSSI measurements of this broadcasted signals. Using the collected
RSSI measurements, the distance between the target and the sensor node can be
2.2 RSSI-Based Target LT Approach](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-45-320.jpg){kind=tracking}
![28
computed using a suitable signal path loss model. Let’s consider a typical scenario
showing the use of RSSI measurements to obtain the unknown location of the target
as shown in Fig. 2.5. If the target is in the communication range of the three trans-
mitting nodes (anchor nodes), then at the target (which carries a sensor node config-
ured in the receiver mode) three RSSI measurements are received. Then, by using a
suitable signal path loss model, one can very easily get three distances of the target
from these three anchor nodes. Using these coordinates of the three anchor nodes
and three computed distances, the unknown location of the target can be computed
very easily. The lower the actual distance between the anchor node and the target
node, the higher will be the value of the RSSI measurement and vice versa [11, 30,
31]. The received RSSI measurement is found to have a highly nonlinear relation-
ship with the distance as shown in Fig. 2.6.
The RSSI measurements are generally erroneous due to the issues related with
signal propagation, such as attenuation, reflections, fading, NLOS conditions, and
multipath propagation [11, 30, 31]. In fact, the RF wave reaches the destination
along the different paths of varying length (multipath propagation), and thereby it
takes different travel times along these paths. Thus, these components of the same
RF signal reach the destination at different times with varying amplitudes. The
interaction of these RF components with each other causes multipath fading. That
means, these components interfere with each other. These interferences at the
receiver can be destructive or constructive. The major reason of multipath propaga-
tion and fading is the varying amount of obstacles in the given environment along
different paths, and thereby, the RF signal components, along each path, experience
varying amount of reflections. The NLOS is the condition wherein the antennas of
the transmitting and the receiving nodes are not along a LOS. Therefore, the received
RSSI measurements are not reliable, though environment is kept unchanged.
The slight changes in location of experiment can also cause variations in the
amount of attenuation, reflections, fading, multipath propagation, and NLOS. Even
with the same environmental setup, the same RSSI measurements are not guaran-
teed. In other words, there exists less possibility of repeatability and regularity in
the RSSI measurements. Thus, the RSSI measurements are highly notorious and
Anchor
Node 1 Anchor
Node 3
Anchor
Node 2
RSSI3
RSSI1
RSSI2
Fig. 2.5 RSSI
measurements for
target LT
2 Target Localization and Tracking Using WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-46-320.jpg){kind=tracking}
![29
dependent on the environment setup. Due to all these characteristic features and
limitations in the RSSI measurements as discussed above, the RSSI-based LT
system is generally associated with low localization accuracy and low stability [11,
30–33]. In order to avoid this problem, some of the precautionary measures reported
in the literature are as follows:
• Take the RSSI measurements at several frequency.
• Take the average RSSI measurements over a suitable time period to smooth vari-
ations in the RSSI measurements.
• Calibrate WSN transceivers to get a comparable reception sensitivity and emis-
sion power.
• Use high-quality antennas.
• Try to minimize changes in the environment setup and signal interference from
the surrounding electronic gadgets, rain, and mobile objects.
2.3
Environmental Characterization Through Path
Loss Models
As discussed earlier to locate a target using RSSI-based technique, characterization
of the given RF channel is a must, and for its characterization, the selection of a
suitable path loss model is highly essential. The path loss model translates the RSSI
0 10 20 30 40 50 60 70 80 90 100
-40
-30
-20
-10
0
10
20
30
40
50
Distance, [m]
]
m
b
d
[
t
n
e
m
e
r
u
s
a
e
M
I
S
S
R
¬ RSSI Curve
RSSI versus Distance Curve
Fig. 2.6 Nonlinear relationship between RSSI and distance
2.3 Environmental Characterization Through Path Loss Models](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-47-320.jpg){kind=tracking}
![30
measurements into distances. Therefore, selecting the appropriate model for the
target localization and tracking is the key to success. A correct understanding and
modeling of the RF propagation channel is a vital prerequisite for improving the
target LT accuracy.
Basically, a path loss model is a set of mathematical expressions, algorithms, and
diagrams, which represents the radio characteristics of the considered RF environ-
ment in which the target resides [9, 14, 26, 33]. The empirical models of a path loss
model are based on the actual RSSI measurements, whereas the theoretical models
of a path loss model are based on the fundamental principles of RF communication.
Popular RSSI path loss models for RF environment characterization are the follow-
ing: free space propagation model, log normal shadowing model (LNSM), and two-
ray ground model. The modified versions of these basic models have also been
reported in the literature. Few researchers in LT domain have also designed their
own path loss models to characterize the given wireless environment. For instance,
the authors in [9] have presented the optimal fitting parametric exponential decay
model (OFPEDM). The OFPEDM is developed for large-scale wheat field. The
author claim that the OFPEDM has less susceptibility to variations in the RF envi-
ronment and higher distance estimation accuracy. The free space propagation model
and two-ray ground model have specific requirements for the underlying applica-
tion environment, whereas the LNSM model is more general in nature. The LNSM
is sometimes also called as log normal shadow fading model. Out of all of these, the
LNSM is more suitable in RSSI-based LT applications for indoor as well as out-
door environmental setup. It presents a number of configurable parameters using
which the given RF environment can be artificially simulated. Let’s discuss mathe-
matics of all of these path loss models in detail.
2.3.1
Free Space Path Loss Model
The free space path loss model provides the RSSI measurement if the transmitter
and the receiver are along a LOS without any obstacle in between [9, 26, 34, 35].
This model is basically based on a well-known Friis transmission formula. It relates
the antenna gains, free space path loss, and wavelength to the transmitted and
received powers. This equation is one of the fundamental equations in RF commu-
nication and antenna theory. In this mathematical equation, if d is the distance
between the receiver and the transmitter, then the RSSI measurement at the receiver
is denoted as Pr(d). According to this model, the ratio of received power to transmit-
ter power is given as the following:
P
P
G G
d
P d
PG G
d
r
t
t r
r
t t r
=
×
→ ( ) =
( )
λ
π
λ
π
4 4
2
2
2 2
(2.1)
2 Target Localization and Tracking Using WSN](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-48-320.jpg){kind=tracking}
![31
where Pt and Pr(d) are the transmitted power and the received power, respectively;
Gt and Gr are the transmitter antenna gain and the receiver antenna gain, respec-
tively; and λ is the signal wavelength in meters. By rearranging the above equation,
one can easily obtain the distance between the transmitter and the receiver.
The Friis equation states that more power is lost at higher frequencies, which is
a fundamental result of this equation. In other words, it can be stated that for anten-
nas with some specified gains, the power transfer will be highest at lower frequen-
cies. That means the higher the frequencies, the higher would be the path loss
associated. As accurate LOS between the transmitter and the receiver is not always
the reality in most of the cases, the estimated RSSI measurements with the help of
this model are not reliable, and thus it generally leads to high localization error in
the target LT applications.
2.3.2
Two-Ray Ground Model
The major drawback of free space path loss model is the dependence on the LOS
between the receiver and the transmitter [9, 26, 34, 35]. The two-ray ground model
does not necessitate the requirement of the LOS. It is basically based on geometry
of the given RF environment and pays attention to the direct path as well as the
ground reflected path between the receiver and the transmitter (see Fig. 2.7). The
estimated RSSI using the two-ray ground model is fairly accurate as compared to
that using the free space path loss model. According to the two-ray ground model,
the RSSI (received power) is given as [34, 35]:
P d PG G
h h
d
r t a b
t r
( ) =
2 2
4
(2.2)
where Ga and Gb are the receiver and the transmitter antenna gain, respectively, and
hr and ht are the heights of receiver antenna and transmitter antenna, respectively.
By rearranging the above equation, one can easily obtain the distance between the
transmitter and the receiver.
Fig. 2.7 Two-ray ground model
2.3 Environmental Characterization Through Path Loss Models](https://image.slidesharecdn.com/15287-250529171346-92e5037f/85/Received-Signal-Strength-Based-Target-Localization-and-Tracking-Using-Wireless-Sensor-Networks-Satish-R-Jondhale-R-Maheswar-Jaime-Lloret-49-320.jpg){kind=tracking}
Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks Satish R Jondhale R Maheswar Jaime Lloret
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- 5. EAI/Springer Innovations in Communication and Computing Satish R. Jondhale R. Maheswar Jaime Lloret Received Signal Strength Based Target Localization andTracking Using Wireless Sensor Networks
- 6. EAI/Springer Innovations in Communication and Computing Series editor Imrich Chlamtac, European Alliance for Innovation, Ghent, Belgium
- 7. Editor’s Note The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineeringinclude,butarenotlimitedto,wirelessnetworks;mobilecommunication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution. Indexing: This series is indexed in Scopus, Ei Compendex, and zbMATH. About EAI EAI is a grassroots member organization initiated through cooperation between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellence by community. More information about this series at http://www.springer.com/series/15427
- 8. Satish R. Jondhale • R. Maheswar • Jaime Lloret Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks
- 9. ISSN 2522-8595 ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-030-74060-3 ISBN 978-3-030-74061-0 (eBook) https://doi.org/10.1007/978-3-030-74061-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Satish R. Jondhale Department of Electronics and Telecommunication Amrutvahini College of Engineering Sangamner, Maharashtra, India Jaime Lloret Instituto de Investigación para la gestión Integrada de Zonas Costeras Universitat Politecnica de Valencia Valencia, Valencia, Spain R. Maheswar Research VIT Bhopal University Bhopal, Madhya Pradesh, India
- 10. v Preface Location awareness is a key component in many industrial, scientific, and military indoor and outdoor applications as well as a wide variety of present location-based services (LBS). Although GPS is a more popular technique to get location updates very easily, limited accessibility GPS signals in most of the indoor as well as out- door environmental setup motivate researchers to design GPS-less localization sys- tem. Being one of the key technologies of twenty-first century, the low powered and low cost wireless sensor network (WSN) paved the way for the design and develop- ment of GPS-less system for indoor as well as outdoor localization and tracking (L&T) applications. The merits of the WSN technology over the rest of the other technological alternatives are: easy deployment, small size, low cost, low power consumption, and ad hoc nature. Due to no additional hardware requirement and simplicity in the usage, the received signal strength indicator (RSSI) is the most widely used metric of field measurement in WSN-based L&T systems as compared with other possible metrics. However, the existing RSSI-based target tracking sys- tems generally suffer with low tracking accuracy because of signal propagation issues such as reflection, refraction, multipath propagation, and non-line of sight (NLOS). Apart from signal propagation issues, environmental dynamicity aspects such as abrupt variations in target velocity during motion, nonavailability of all RSSI measurements, variations in target mobility patterns also make RSSI-based target L&T highly challenging. Although much research has already been done in WSN-based L&T, most of these existing systems are not robust and efficient in terms of tracking accuracy and computational complexity. The present focus of all the researchers working in RSSI and WSN-based L&T domain is the development of efficient, robust, and accurate L&T system. The research in WSN- and RSSI- based L&T domain is blooming with very high pace that it is very difficult to encompass all the new developments in it; however, we tried our best to provide a detailed review of recent and relevant information of existing RSSI- and WSN- based L&T systems. The main focus of writing this book is to give a systematic approach of learning fundamentals of WSN and its capability to build L&T applica- tions. The sincere attempt is made in this book to answer about how to design novel- efficient RSSI-based tracking system which can track single mobile target and yield
- 11. vi high tracking accuracy irrespective of its motion. Several artificial neural network (ANN)-based implementations dealing with tracking of single mobile target with environmental dynamicity are presented in this book and are validated through extensive MATLAB-based simulation experiments. We believe that this book can provide an effective way to design or program customized solution tailored to meet the underlying WSN-based L&T applications with the help of RSSI measurements. Thus through this book, we not only present the fundamentals of RF communi- cation, WSN-based target L&T, hardware, protocols architectures, and pros-cons in the existing RSSI- and WSN-based systems, but we also present system-level implementation through MATLAB-based building blocks of subsystems of L&T system. One can use these ready-to-use building blocks to understand and build their WSN-based L&T applications or pursue further research to customize their underlying application as per the actual requirement. Any undergraduate student of physics, mathematics, computer science, or electronics disciplines might feel com- fortable to follow this book material. Sangamner, India Satish R. Jondhale Bhopal, India R. Maheswar Valencia, Spain Jaime Lloret Preface
- 12. vii Acknowledgment I would like to express my sincere thanks to Prof. Chlamtac (President, European Alliance for Innovation (EAI)) and Eliška Vlčková (Managing Editor, EAI) for pro- viding the opportunity to write the book entitled, Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Network. As a correspond- ing author for this book, I would like to give special thanks to the book co-authors Dr. R. Maheswar (School of EEE, VIT Bhopal University, Bhopal) and Dr. Jaime Lloret (Polytechnic University of Valencia, Spain) for being selfless mentors, bril- liant research partners, and precious friends during the overall accomplishment of this book. I would like to thank Dr. Rajkumar S. Deshpande (my Ph.D. guide), Dr. D. N. Kyatanvar (Principal, Sanjivani COE, Kopargaon), and Dr. B. S. Agarkar (Sanjivani COE, Kopargaon) for motivating me to extend my Ph.D. work in the context of writing this book. I also thank the Management, Dr. R. P. Labade (Head, ETC department), and my colleagues from Amrutvahini COE, Sangamner, Maharashtra, India for giving me all kind of support and facilities to complete this book successfully. I also thank all the reviewers for giving their precious feedback to improve the work further. I would also like to thank all the supporting staff from Springer who really helped a lot and their extended support with quick and efficient efforts made the book finally a successful one. Sincere thanks to my wife Prof. Amruta, my daughters Aarohi and Rajlaxmi, and my parents for wholeheartedly excusing my absence during precious life moments when I was writing this book. I feel that without the support of my family members, this book writing would not at all be possible. At the end, I must extend a huge expression of gratitude to Lord Shri Krishna for offering me enough energy and knowledge during the making of this book.
- 13. ix Contents 1 Fundamentals of Wireless Sensor Networks ���������������������������������������� 1 1.1 Introduction to Wireless Sensor Network���������������������������������������� 1 1.2 WSN Versus Other Wireless Networks�������������������������������������������� 3 1.3 Sensor Node Architecture ���������������������������������������������������������������� 5 1.3.1 The Power Supply���������������������������������������������������������������� 6 1.3.2 The Sensing Unit������������������������������������������������������������������ 6 1.3.3 The Processor Unit���������������������������������������������������������������� 7 1.3.4 The Communication Unit ���������������������������������������������������� 7 1.3.5 Location Finding Unit���������������������������������������������������������� 8 1.4 Sensor Network Communication Architecture �������������������������������� 9 1.5 Design Constraints for WSN������������������������������������������������������������ 10 1.5.1 Power Consumption�������������������������������������������������������������� 10 1.5.2 Memory�������������������������������������������������������������������������������� 11 1.5.3 Deployment, Topology, and Coverage���������������������������������� 11 1.5.4 Communication and Routing������������������������������������������������ 12 1.5.5 Security �������������������������������������������������������������������������������� 12 1.5.6 Production Costs������������������������������������������������������������������ 13 1.5.7 Fidelity and Scalability �������������������������������������������������������� 13 1.6 Existing WSN Platforms������������������������������������������������������������������ 13 1.6.1 Wins�������������������������������������������������������������������������������������� 14 1.6.2 Eyes�������������������������������������������������������������������������������������� 14 1.6.3 Pico-Radio���������������������������������������������������������������������������� 14 1.6.4 Mica Mote Family���������������������������������������������������������������� 15 1.7 Applications of WSN������������������������������������������������������������������������ 15 1.7.1 Military Applications������������������������������������������������������������ 16 1.7.2 Environment Monitoring Applications �������������������������������� 16 1.7.3 Health Applications�������������������������������������������������������������� 16 1.7.4 Home Applications��������������������������������������������������������������� 17 1.7.5 Other Commercial Applications ������������������������������������������ 17 References�������������������������������������������������������������������������������������������������� 17
- 14. x 2 Target Localization and Tracking Using WSN�������������������������������������� 21 2.1 Introduction to WSN-Based LT���������������������������������������������������� 21 2.1.1 Typical LT Scenario in Wireless Sensor Networks ���������� 23 2.1.2 Classification of Target LT Techniques ���������������������������� 24 2.2 RSSI-Based Target LT Approach�������������������������������������������������� 26 2.3 Environmental Characterization Through Path Loss Models ���������� 29 2.3.1 Free Space Path Loss Model������������������������������������������������ 30 2.3.2 Two-Ray Ground Model������������������������������������������������������ 31 2.3.3 Log Normal Shadow Fading Model (LNSM)���������������������� 32 2.3.4 OFPEDM������������������������������������������������������������������������������ 32 2.4 Technologies for RSSI-Based LT�������������������������������������������������� 33 2.4.1 RFID ������������������������������������������������������������������������������������ 33 2.4.2 Wi-Fi������������������������������������������������������������������������������������ 34 2.4.3 Bluetooth������������������������������������������������������������������������������ 34 2.4.4 Zigbee ���������������������������������������������������������������������������������� 35 2.5 Traditional Techniques for Target Localization�������������������������������� 35 2.5.1 Trilateration�������������������������������������������������������������������������� 36 2.5.2 Triangulation������������������������������������������������������������������������ 37 2.5.3 Fingerprinting ���������������������������������������������������������������������� 37 2.6 Mobility Models for Target Tracking������������������������������������������������ 38 2.6.1 Constant Velocity (CV) Model �������������������������������������������� 38 2.6.2 Constant Acceleration (CA) Model�������������������������������������� 39 2.7 State Estimation Techniques for Target Tracking ���������������������������� 39 2.7.1 Standard Kalman Filter (KF)������������������������������������������������ 40 2.7.2 UKF�������������������������������������������������������������������������������������� 41 2.8 Challenges Associated with RSSI-Based Indoor LT�������������������� 43 References�������������������������������������������������������������������������������������������������� 45 3 Survey of Existing RSSI-Based LT Systems�������������������������������������� 49 3.1 Survey of Application of Various Wireless Technologies for Indoor Tracking�������������������������������������������������������������������������� 49 3.2 Survey of Application of Bayesian Filtering in RSSI- Based Target Tracking �������������������������������������������������������������������������������� 51 3.3 Survey of Application of ANN in RSSI-Based Target Tracking�������������������������������������������������������������������������������������������� 54 3.4 Survey of Application of BLE Technology in RSSI-Based Target Tracking �������������������������������������������������������������������������������� 58 3.5 Limitations in the Existing RSSI-Based LT Systems�������������������� 60 References�������������������������������������������������������������������������������������������������� 62 4 Trilateration-Based Target LT Using RSSI���������������������������������������� 65 4.1 System Assumptions and Design for Trilateration-Based LT�������������������������������������������������������������������������������������������������� 65 4.2 Flow of Trilateration-Based LT Algorithm������������������������������������ 68 4.3 Performance Metrics for Assessment of LT Performance������������ 69 4.4 Discussion on Results ���������������������������������������������������������������������� 69 Contents
- 15. xi 4.4.1 Case I Results: Testing the Impact of Environmental Dynamicity on LT (Variation in RSSI Measurement Noise)������������������������������������������������������������������������������������ 70 4.4.2 Case II Results: Testing the Impact of Anchor Density on LT�������������������������������������������������������������������� 83 4.5 Conclusions�������������������������������������������������������������������������������������� 88 MATLAB Code for Trilateration-Based Target LT�������������������������������� 89 References�������������������������������������������������������������������������������������������������� 96 5 KF-Based Target LT Using RSSI�������������������������������������������������������� 97 5.1 System Assumptions and Design of KF-Based LT ���������������������� 97 5.2 Flow of Trilateration+KF and Trilateration+UKF-Based LT Algorithms���������������������������������������������������������������������������������������� 103 5.3 Performance Metrics for Assessment of LT Performance������������ 104 5.4 Discussion on Results ���������������������������������������������������������������������� 105 5.4.1 Case I Results������������������������������������������������������������������������ 105 5.4.2 Case II Results���������������������������������������������������������������������� 106 5.4.3 Case III Results�������������������������������������������������������������������� 111 5.5 Conclusions�������������������������������������������������������������������������������������� 114 MATLAB Code for KF-Based Target LT���������������������������������������������� 115 References�������������������������������������������������������������������������������������������������� 131 6 GRNN-Based Target LT Using RSSI�������������������������������������������������� 133 6.1 GRNN Architecture for Target LT Applications���������������������������� 133 6.2 System Assumption and Design������������������������������������������������������� 134 6.3 Flow of Trilateration+KF- and Trilateration+UKF-Based LT Algorithms���������������������������������������������������������������������������������������� 138 6.4 Performance Metrics������������������������������������������������������������������������ 138 6.5 Discussion on Results ���������������������������������������������������������������������� 139 6.5.1 Case I Results������������������������������������������������������������������������ 139 6.5.2 Case II Results���������������������������������������������������������������������� 141 6.5.3 Case III Results�������������������������������������������������������������������� 142 6.6 Conclusions�������������������������������������������������������������������������������������� 147 MATLAB Codes for GRNN and KF Framework-Based Target LT�������������������������������������������������������������������������������������������������� 148 References�������������������������������������������������������������������������������������������������� 169 7 Supervised Learning Architecture-Based LT Using RSSI���������������� 171 7.1 Supervised Learning Architectures for LT������������������������������������ 171 7.1.1 FFNT������������������������������������������������������������������������������������ 171 7.1.2 Radial Basis Function Neural Network (RBFN or RBFNN)�������������������������������������������������������������������������������� 171 7.1.3 Multilayer Perceptron (MLP) ���������������������������������������������� 173 7.2 Training Functions in ANN�������������������������������������������������������������� 174 7.3 Application of Supervised Learning Architectures for LT������������ 174 7.3.1 System Assumptions and Design������������������������������������������ 175 Contents
- 16. xii 7.3.2 Evaluation Parameters���������������������������������������������������������� 177 7.3.3 Algorithmic Flow of Proposed ANN Architectures�������������� 177 7.3.4 Discussion on Results ���������������������������������������������������������� 177 7.4 Conclusion���������������������������������������������������������������������������������������� 187 MATLAB Code for Cases I and II������������������������������������������������������������ 188 References�������������������������������������������������������������������������������������������������� 201 Index������������������������������������������������������������������������������������������������������������������ 203 Contents
- 17. xiii About the Editors Satish R. Jondhale received his B.E. in Electronics and Telecommunication in 2006, his M.E. in Electronics and Telecommunication in 2012, and his Ph.D. in Electronics and Telecommunication in 2019 from Savitribai Phule Pune University, Pune, India. He has been working as an Assistant Professor in Electronics and Telecommunication Department at Amrutvahini College of Engineering, Sangamner, Maharashtra, India for more than a decade now. His research interests are Signal Processing, Target Localization and Tracking, Wireless Sensor Networks, Artificial Neural Networks and Applications, Image Processing and Embedded System Design. He has sev- eral research publications in reputed journals such as IEEE Sensors Journal, Ad Hoc Networks (Elsevier), Ad Hoc Sensor Wireless Networks, and International Journal of Communication Systems (Wiley). He has published two book chapters in Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario, Springer, 2019. He is also a member of professional societies such as IEEE and ISTE. He has been a reviewer for peer-reviewed journals such as IEEE Transactions on Industrial Informatics, IEEE Sensors, Signal Processing (Elsevier), IEEE Access, IEEE Signal Processing Letters, and Ad Hoc Sensor Wireless Networks, and so on. He has received review recognition appreciation from Mississippi State University, USA for valuable review work. He had served as a TPC member for Sixth International conference on Internet of Things: Systems, Management and Security (IOTSMS, 2019) held at Granada, Spain from 22 to 25 October, 2019 (Technically Co-Sponsored by IEEE Spain Section). He has been appointed as “Bentham Brand Ambassador” for 2019–20.
- 18. xiv R. Maheswar has completed his B.E (ECE) from Madras University in the year 1999, M.E (Applied Electronics) from Bharathiar University in the year 2002 and Ph.D. in the field of Wireless Sensor Network from Anna University in the year 2012. He has about 19 years of teaching experience at various levels and presently working as Dean–Research (Assistant) and Dean In-Charge for the School of EEE, VIT Bhopal University, Bhopal. He has published around 70 papers at International Journals and International Conferences and published 4 patents. His research interest includes Wireless Sensor Network, IoT, Queueing theory, and Performance Evaluation. He has served as guest editor for Wireless Networks Journal, Springer and is serving as editorial review board member for peer-reviewed journals, and also edited four books supported by EAI/ Springer Innovations in Communications and Computing book series. He is pres- ently an associate editor in Wireless Networks Journal, Springer, Alexandria Engineering Journal, Elsevier and Ad Hoc Sensor Wireless Networks Journal, Old City Publishing. Jaime Lloret received his B.Sc.+M.Sc. in Physics in 1997, his B.Sc.+M.Sc. in electronic Engineering in 2003 and his Ph.D. in telecommunication engineering (Dr. Ing.) in 2006. He is a Cisco Certified Network Professional Instructor. He is IEEE Senior, ACM Senior, and IARIA Fellow. He is Chair of the Integrated Management Coastal Research Institute (IGIC), IEEE Spain Section Officer, Chair of the Internet Technical Committee (IEEE Communications Society Internet Society) (Term 2014–2015), Head of the Innovation Group “Active and collaborative techniques and use of technologic resources in the education (EITACURTE)” as well as Chair IEEE 1907.1 WG (till 2018). He is currently Associate Professor in the Polytechnic University of Valencia. He is the Chair of the Integrated Management Coastal Research Institute (IGIC) and he is the head of the “Active and collaborative tech- niques and use of technologic resources in the education (EITACURTE)” Innovation Group. He is the director of the University Diploma “Redes y Comunicaciones de Ordenadores” and he has been the director of the University Master “Digital Post Production” for the term 2012–2016. He was Vice-chair for the Europe/Africa Region of Cognitive Networks Technical Committee (IEEE Communications Society) for the term 2010–2012 andVice-chair of the Internet Technical Committee (IEEE Communications Society and Internet society) for the term 2011–2013. He has been Internet Technical Committee chair (IEEE Communications Society and Internet society) for the term 2013–2015. He has authored 22 book chapters and has About the Editors
- 19. xv more than 480 research papers published in national and international conferences, international journals (more than 230 with ISI Thomson JCR). He has been the co- editor of 40 conference proceedings and guest editor of several international books and journals. He is editor-in-chief of Ad Hoc and Sensor Wireless Networks (with ISI Thomson Impact Factor), the international journal Networks Protocols and Algorithms, and the International Journal of Multimedia Communications. Moreover, he is Associate Editor-in-Chief of Sensors in the Section Sensor Networks, he is advisory board member of the International Journal of Distributed Sensor Networks (both with ISI Thomson Impact Factor), and he is IARIA Journals Board Chair (8 Journals). Furthermore, he is (or has been) associate editor of 46 international journals (16 of them with ISI Thomson Impact Factor). He has been involved in more than 450 Program committees of international conferences and more than 150 organization and steering committees. He has led many local, regional, national, and European projects. He is currently the chair of the Working Group of the Standard IEEE 1907.1. Since 2016, he is the Spanish researcher with highest h-index in the TELECOMMUNICATIONS journal list according to Clarivate Analytics Ranking. He has been general chair (or co-chair) of 52 International workshops and conferences (chairman of SENSORCOMM 2007, UBICOMM 2008, ICNS 2009, ICWMC 2010, eKNOW 2012, SERVICE COMPUTATION 2013, COGNITIVE 2013, ADAPTIVE 2013, 12th AICT 2016, 11th ICIMP 2016, 3rd GREENETS 2016, 13th IWCMC 2017, 10th WMNC 2017, 18th ICN 2019, 14th ICDT 2019, 12th CTRQ 2019, 12th ICSNC 2019, 8th INNOV 2019, 14th ICDS 2020, 5th ALLSENSORS 2020, Industrial IoT 2020 and GC-ElecEng 2020, and co-chairman of ICAS 2009, INTERNET 2010, MARSS 2011, IEEE MASS 2011, SCPA 2011, ICDS 2012, 2nd IEEE SCPA 2012, GreeNets 2012, 3rd IEEE SCPA 2013, SSPA 2013, AdHocNow 2014, MARSS 2014, SSPA 2014, IEEE CCAN 2015, 4th IEEE SCPA 2015, IEEE SCAN 2015, ICACCI 2015, SDRANCAN 2015, FMEC 2016, 2nd FMEC 2017, 5th SCPA 2017, XIII JITEL 2017, 3rd SDS 2018, 5th IoTSMS 2018, 4th FMEC 2019, 10th International Symposium on Ambient Intelligence 2019, 6th SNAMS 2019, and ACN 2019, and local chair of MIC-WCMC 2013 and IEEE Sensors 2014). About the Editors
- 20. 1 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. R. Jondhale et al., Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-74061-0_1 Chapter 1 Fundamentals of Wireless Sensor Networks 1.1 Introduction to Wireless Sensor Network The WSN can be described as autonomous and self-organizing systems that consist of a large number of tiny, low-cost, battery-operated sensor nodes (also called as motes), which are generally randomly deployed either inside the phenomenon of interest or very close to it [1, 2]. These motes are generally utilized to monitor envi- ronmental and physical conditions, such as pressure, temperature, light, humidity, fire detection, and chemical level [3–5]. These nodes can sense the environment (data collection) and process and forward the processed data directly to the base station (also called as sink) or via the other sensor nodes to the base station in order to process it further as per application requirements. These sensor nodes in the WSN are fitted with an onboard processor. Instead of forwarding the raw sensed data, sen- sor nodes use their built-in processing capability to carry out simple computations at local level and then transmit only the partially processed data to the nodes respon- sible for the fusion with that obtained from other nodes. These computational capa- bilities in WSN ensure a wide range of applications [3] [6–8]. For example, it is possible to monitor the physiological data of a patient remotely by a doctor, which saves a lot of time of patients as well as doctors. The WSN can also be used to locate and/or detect pollution level as well as percent of toxic contents in the air and the water. Thus, the WSN can provide the end user a better understanding of the envi- ronment with intelligence. In 10–15 years it is not unreasonable to expect that the large portion of the world will be covered with WSN with access to them via Internet [3, 6]. A typical WSN model consisting of sensor nodes, sink, Internet connectivity, and end user is shown in Fig. 1.1. Sensor field is nothing but an environment under con- sideration, wherein the nodes are deployed to gather the information in it [5]. Each of the nodes is capable of sensing, processing, and forwarding the sensed data to the
- 21. 2 requested nodes or to the sink. The sink and the sensor node may be static or mobile, depending upon the application requirements. The sink can collect as well as pro- cess the data from the sensor nodes. Generally, the sink is rich in memory, compu- tational capacity, and energy as compared to the sensor nodes. The sink connects the WSN to the end user with a terminal (such as computer) using an existing commu- nication infrastructure such as Internet. The concept of WSN can be described with the help of the following simple equation [3, 5]: Sensing CPU Radio Thousands of possible applications of WSN + + = Thus, knowing the capabilities of the WSN, thousands of applications appear in mind. Although it seems a straightforward combination of modern technologies, combining sensors, processors, and radios into a coin-sized node requires a detailed knowledge of both the capabilities and limitations of each of the underlying hard- ware part, as well as fundamentals of distributed systems and modern networking technologies [3, 5]. Each of the sensor nodes must be designed to encompass the set of primitives required to formulate the network of nodes, while strictly attaining requirements of cost, size, and power consumption. Thus, the major challenge is to map the overall system requirements. The WSN node may have different types of sensors interfaced, capable to moni- tor a wide variety of ambient conditions. The type of sensor can be seismic, pres- sure, thermal, magnetic, infrared, acoustic, and visual [3, 5]. The sensor nodes can be deployed manually or randomly. Although each individual node has several resource constraints in terms of memory, energy, computation, and communication capabilities, their heavy deployment can collectively sense the surrounding environ- ment, disseminate measurements, and process these experimental measurements. That’s why the WSN applications range from environmental monitoring, real-time tracking, to structural health of monitoring [3, 6–8]. Capability to real-time infor- mation makes the WSN an ideal candidate for handling emergency, disaster relief End user Sink Internet Anchor Node Sensor Field Target Wireless Sensor Networks Sensor Node Fig. 1.1 A typical WSN model 1 Fundamentals of Wireless Sensor Networks
- 22. 3 operations, and military that need efficient coordination and planning. The WSN can also be useful for instrumenting and controlling of offices, factories, vehicles, homes, and cities. Any WSN-based application is useful only if the location of the sensor node that provides the measurements is correctly known. In other words, node localization is of prime importance to any WSN-based application [9]–[12]. In order to get the node locations, an effective localization algorithm is needed. The WSN-based system gets the updates of the location of node that provide useful measurements; however, many times the estimated locations are not trustworthy because of noisy measurements. Thus, in most of the situations, location estimates are not accurate enough to claim that the underlying WSN-based application is robust and reliable. That’s why the node localization has attracted tremendous atten- tion of the researchers. The major objective of any localization algorithm is to improve node localization accuracy (i.e., to reduce localization error). In recent years, one of the major researches in WSN domain is on localization and tracking (LT). Designing efficient LT algorithms becomes an important factor for the success of any WSN-based application [9–12]. In this book we provide the fundamental aspects of WSN as well as a detailed framework of WSN-based LT system right from concept to design. We cover fundamentals of RSSI-based LT using WSN, simulated as well as real-time WSN- based LT framework. A sincere attempt is made to provide the survey of the exist- ing RSSI-based LT systems through a rigorous review of literature from the recent papers of journals as well as conferences. This book is targeted to the managers, communications developers, and practitioners, who wish to acquire the knowledge of target LT and wish to implement WSN-based LT system to encompass broad range of related applications. 1.2 WSN Versus Other Wireless Networks The advancements in RF domain and rise in portable devices have accelerated the use of mobile and wireless networking [13–15]. Because of wireless networking, the users can electronically access data and services, irrespective of their physical location [8]. Wireless technology-based networks are generally classified into two categories, namely, infrastructure-based networks and infrastructure-less networks (ad hoc networks). The former category has fixed the base station called access points, which are connected by wires. The mobile node can communicate with the base station via wireless link if it is inside the communication range of that base station. If this mobile node travels out of the communication range of that base sta- tion, then it tries to establish the connection with the other base station inside whose communication range it currently is. Cellular phone system, paging systems, and wireless local area networks (WLAN) are some of the examples of infrastructure- based networks, whereas ad hoc networks do not have such predefined infrastruc- ture and the nodes can move freely from one place to another, changing the network topology continuously [3–5]. Mobile ad hoc network (MANET) and WSN are some 1.2 WSN Versus Other Wireless Networks
- 23. 4 of the examples of ad hoc networks. These networks do not necessitate previous setup or supporting infrastructure. The MANET is a network of self-configurable, autonomous, self-organizing nodes with wireless communication capabilities (especially multi-hop communica- tion) [16]. It is generally adopted to meet the requirements of immediate communi- cation need, where the deployment of wired infrastructure is not a feasible option. For instance, MANET is used in situations such as battlefield, disaster relief opera- tions, flood relief operations, and large construction sites. As compared to wired infrastructure, the MANET can cover larger geographical areas. The WSN is a spe- cial kind of ad hoc network, consisting of heavily deployed sensor nodes that can cover a much wider geographical area as compared to the MANET [16]. As described in the previous section, the sensor nodes in WSN are battery operated, low cost, and small in size. Some of the similarities between the WSN and MANET are: • Both are distributed wireless networks with no requirement of previous infra- structural setup. • Nodes are deployed in an ad hoc manner in both. • In most of the applications, the nodes communicate with each other using multi-hop way. • Both have concern over the minimization of power consumption due to use of battery-powered nodes. • Due to uses of unlicensed spectrum for operation, both are generally prone to interference by other RF-based devices operating in the same frequency slot. • Self-configuration is a must in both due to distributed nature. In spite of many similarities between the WSN and the MANET, there are also few key differences between them as listed below [3, 5, 6, 16]: • The node in the WSN is generally of the order of several hundreds to thousands as compared to the small number in the MANET. Thus, node deployment density in the WSN is very high. • Nodes in the WSN are prone to failure due to environmental and physical conditions. • Due to frequent node failures, WSN topology gets updated quite often. • In most of the situations, the WSN use broadcast communication strategy, whereas the MANET adopts point-to-point networking. • The scarcity of resources is a common problem in the WSN (which means con- straints of energy, computational abilities, and memory). • The WSN nodes generally do not have global unique identification due to mass (heavy) deployment. • In majority of the applications, node mobility is comparatively low or nil in the WSN as compared to that in the MANET. • As compared to the MANET, the data rate in the WSN is very low. 1 Fundamentals of Wireless Sensor Networks
- 24. 5 1.3 Sensor Node Architecture Sensor nodes are designed and consisting of many components than just wireless sensors. As mentioned earlier these sensor nodes can sense the physical parameter of interests, process it, and dispatch the processed data to the base station. A sensor node can be defined in the following way [3–5, 8]: A sensor node is a type of transducer that senses one type of energy (field measurements) and converts it into a suitable form (electrical form) for the purpose of data transfer to the other sensor nodes. Furthermore, it possesses the ability to avoid the transmission of the redundant data sensed from the surrounding environment (field measurements). From a hardware perspective, the sensor nodes are small-scale processing units with a variety of sensors interfaced to it. Typically the field measurements are tem- perature, noise level, wind pressure, the presence of static or moving objects, received signal strength indicators (RSSI), and so on [3–5, 8]. The type of sensors interfaced with the node depends upon the underlying targeted application. Speaking in more specific words, the sensor node typically has inbuilt processor (to process the physical measurements received from the interfaced sensors), a battery (to power it up), a memory (to store raw sensed or processed data), and a radio or com- munication unit (for communication with the other nodes or external world). The sensor network’s networking and communication abilities can be creatively exploited to deal with specific underlying application. Sensor node architecture with these four functional units is illustrated in Fig. 1.2 as shown below. Fig. 1.2 Components of sensor node 1.3 Sensor Node Architecture
- 25. 6 1.3.1 The Power Supply The power supply unit generally includes a nonrenewable coin-sized battery, whose role is to supply power to all of the units of the sensor node [3–5, 8, 10]. Thus, bat- teries are obviously energy storage devices, whose size ranges from small coin cell to large lead-acid batteries of AA or AAA types. The rechargeable batteries are generally not used in most of the WSN-based applications due to high cost, low energy density, and impracticality of recharging option. If this battery is depleted, the sensor node becomes nonfunctional. As in most of the WSN-based applications, the sensor nodes are deployed in hostile environment and are generally inaccessible; the sensor node lifetime mainly rely on the attached batteries. In the sensor node, power is consumed for node activities, such as sensing, data processing, and communication. Out of these node activities, the major part of power consumption is observed for data communication. For instance, the power consumption on transmitting 1 Kb data over a distance of say 100 m is approxi- mately the same as that for executing approximately three million instructions by a processor with a capability of 100 million instructions per second (MIPS) [3]. The power consumption is a major design constraint of the WSN due to the limitation in battery size. Thus, designing of power supply unit is a very crucial task in sensor network design for an application. This design part may vary from application to application. However, it is also possible to power the network and extend the WSN lifetime by extracting energy from the environment by the usage of solar cells. 1.3.2 The Sensing Unit This unit generally consists of physical sensors, which are capable of sensing the physical parameter of interest [3]. It also contains an analog-to-digital converter (ADC) to transform sensed data into digital form. Sensor is a transducer, which converts a change in a physical phenomenon into a measurable electrical signal. Sensors measure physical conditions such as temperature, humidity, light, pressure, sound, chemical level, magnetic fields, and etc. The sensor converts analog signal into digital signal using ADC, which is then fed to the processor for further required processing. A sensor node is generally tiny in shape and requires low power con- sumption and operates unattended. A sensor node may have several types of sensors connected to the node. 1 Fundamentals of Wireless Sensor Networks
- 26. 7 1.3.3 The Processor Unit The processing unit in a WSN node consists of a suitable embedded processor for processing the digital data obtained from ADC unit [3]. The processor can execute various tasks, such as processing of input data and controlling the working of other components of the node. The processing unit generally has a microcontroller to execute all of the mentioned tasks; however, in some of the applications, it may consists of digital signal processor (DSP) and field-programmable gate array (FPGA). The microcontroller is a more preferred option due to low power consump- tion and low cost involved as well as flexibility of interfacing with other devices and ease in programming. The common microcontrollers that are used in sensor nodes areAtmelATmega128 series controllers,ARM microcontrollers, Texas Instruments’ MSP 430, and Microchip’s PIC. The more complex the application, the more advanced microcontroller is preferred in the sensor node to meet the application requirements [3–5, 16]. The processing unit also contains a memory unit for storage of the processed data and algorithms of the underlying application. The memory unit consists of on- chip flash memory, internal RAM, and external flash memory. For instance, Mica2 mote is based on ATmega128L microcontroller, which has 4 Kb static RAM and 128 Kb flash program memory [3]. Though it is the era of modern powerful and tiny processors, the power (energy) and memory of the sensor node are still considered as scarce resources. Some of the typical tasks executed by the processing unit are: • Control, signal processing, and actuation. • Data aggregation. • Compression, clustering, forward error correction, and encryption. • Data fusion and data analysis. 1.3.4 The Communication Unit The communication unit consists of a wireless radio transceiver. For collaborative processing, the sensor nodes frequently need to exchange the data with the neigh- boring nodes. The transceiver can convert the digital bit stream received from the microcontroller into RF waves or RF waves into an equivalent digital bit stream [3, 16, 17]. Thus, the sensor node can communicate with the external world (other nodes) through interfaced transceiver. The transmission media for communication between nodes can be RF, optical, or infrared. Communications using lasers need less energy; however, they require LOS for communication, and additionally they are sensitive to atmospheric conditions. Like lasers the infrared does not need antenna; however, its broadcasting capacity is limited. The RF communication gen- erally involves various important operations, such as modulation and demodulation, filtering, and multiplexing. These operations make sensor node communication 1.3 Sensor Node Architecture
- 27. 8 highly complex and expensive as compared to other operations of the sensor node. Additionally, the signal path loss during the communication between two commu- nicating sensor nodes has exponential relation with the distance between them, as the sensor node antennas are usually close to the ground. In spite of the high com- munication cost involved, the RF-based communication is widely preferred in the WSN-based applications [9–12, 18, 19]. The reason behind this is that the data rates are low and packets are small in RF communication. One more advantage with RF communication is the possibility of frequency reuse due to shorter communication lengths. The transceiver has four operational states, namely, receive, transmit, sleep, and idle. The power consumption in idle mode is almost equal to that in the receive mode. Therefore, if the transceiver is not transmitting or receiving, it’s better to shut down it completely rather than leaving it in the idle mode. Another important aspect to note down is that significant power consumption occurs during switching; there- fore, unnecessary switching between states needs to avoided. The popular Mica2 mote uses two kinds of RF transceivers, namely, Chipcon CC1000 and RFM TR1000. The transmission range of Mica2 is around 150 m [3]. Some of the domi- nant wireless standards used for communication by the sensor nodes are: • IEEE 802.15.1 PAN/Bluetooth • IEEE 802.15.3/UWB • IEEE 802.15.4/ZigBee • IEEE Wi-Fi 1.3.5 Location Finding Unit As discussed earlier the sensor node positioning is important in any WSN-based application. In WSN locations of few nodes are prefixed (such nodes are called as anchor nodes), whereas the remaining nodes are randomly deployed in the environ- ment and are termed as non-anchor nodes [20–23]. That means locations of the non-anchor nodes are unknown. Since sensor nodes are generally deployed ran- domly and run unattended, they need to corporate with a location finding system. The location finding unit in the sensor node architecture is optional. If it is present in the sensor node, then it contains a Global Positioning System (GPS) to estimate the location of the node. It is often assumed that each sensor node will have a GPS unit that has approximately 5 m accuracy [24–27]. Equipping all sensor nodes with a GPS is not a viable solution in the WSN due to the cost involved. The possible solution to this is to interface GPS to anchor nodes and then locate the non-anchor nodes with the help of anchor nodes by executing a suitable localization algorithm. 1 Fundamentals of Wireless Sensor Networks
- 28. 9 1.4 Sensor Network Communication Architecture The overall working of the WSN can be explained using the protocol stack as elabo- rated in Fig. 1.3 [3, 5]. The protocol stack includes five layers, namely, physical layer, network layer, data link layer, transport layer, and application layer. Based on the sensing tasks, a variety of application software may be built and run on the application layer. The transport layer is responsible to maintain the data flow between sensor nodes. The network layer monitors the routing of the data provided by the transport layer. Minimizing the collision with neighbor nodes during broad- cast is the main task of the data link layer. The physical layer deals with modulation, data transmission, and data receiving techniques for the WSN. Apart from these layers, the three management planes associated with the proto- col stack are task plane, power plane, and mobility plane (see Fig. 1.3). These three planes monitor the power, movement, and task distribution among the WSN nodes. These three planes assists the sensor node in lowering the overall power consump- tion and coordinating the sensing task. The power plane takes care of efficient and effective utilization of power among sensor nodes during operation of the network as a whole [3] [5]. For instance, the sensor node turns off its receiver in order to avoid duplication of data. Let’s consider another case wherein the power level of a sensor node is low. In such critical situation such sensor node may broadcast to its neighboring nodes that it has low power and can’t participate in data routing. In other words, this node will reserve the remaining power only for sensing. The mobility plane is responsible for registering and detecting the movement of sensor Fig. 1.3 Wireless sensor network protocol stack 1.4 Sensor Network Communication Architecture
- 29. 10 nodes. That’s why each sensor node can keep a track on the movement of its neigh- boring nodes. By the knowledge of the neighboring nodes in advance, the sensor nodes can maintain a balance between its task and power usage. The task plane is responsible to balance and schedule the sensing tasks for a specific region in the given monitoring environment. Thus, there is no need for the sensor nodes to sense the environment at the same time. In other words, only those sensor nodes, which have sufficient power level, will perform the sensing tasks. Thus, all of these man- agement planes are essential for the sensor network to route the data effectively in the network, to achieve power efficiency, and to marshal resources among the net- work nodes [3, 5]. That means without these three management planes, a sensor node could just work individually without a concern about the rest of the network. For the sensor network as a whole, it will be highly advantageous if the sensor nodes in the network can collaborate with each other in order to prolong the lifetime of the sensor networks. 1.5 Design Constraints for WSN The WSNs are characterized by a very powerful combination of distributed sensing, computing, and communication. Despite the tiny size of an individual WSN node, it faces numerous challenges such as stringent power constraints, limited communica- tion range, computing power, and storage space of the sensor nodes [3–5, 10, 28]. The major reason for these constraints is the small physical size of the sensor nodes. The primary objective of the WSN is to execute the task of data communication (routing) while trying to extend the network lifetime as high as possible by employ- ing energy-efficient techniques. Some other operating challenges include high error rates, low bandwidth, noisy measurements, sleep scheduling of sensor node, scal- ability to a huge amount of sensor nodes, survivability in dynamic environments, breakdown of wireless communication link, and frequent node failure. The follow- ing section discusses some of the important design issues and challenges that affect data routing in WSNs. 1.5.1 Power Consumption As discussed earlier the sensor nodes are generally battery powered and are gener- ally deployed in remote or inaccessible environments [3, 6–8]. Replacing or recharg- ing the batteries in such environment is almost impossible. The power is a mandatory aspect for almost all of the operations in the WSN. In general, the power consump- tion in sensor nodes is observed at three places: (a) power consumption by sensing unit, (b) power consumption by communication unit, and (c) power consumption by processing unit. Therefore, the power consumption is one of the major concerns in the WSN-based applications [16, 20, 29, 30]. It is observed that a single bit 1 Fundamentals of Wireless Sensor Networks
- 30. 11 transmission in the WSN consumes the same power as that for executing approxi- mately 800–1000 instructions. Thus, the power consumption in radio is much higher than that in sensing and computation. From the architectural point of view, the use of low-power antenna circuitry must be chosen to reduce power consumption. Low power consumption is a key to suc- cess in any WSN-based application. That’s why a lot of research has been going on in the WSN community to develop energy-efficient algorithms for routing, localiza- tion, and other tasks, which will consume less power [16, 20, 29, 30]. Parallel to this, continuous research is going on to extend sensor node lifetime despite its bat- tery-dependent working. Power efficiency in the WSN can be accomplished in three ways: 1. Low-duty-cycle operation. 2. Local/in-network processing to reduce data volume and in turn transmission time. 3. Multi-hop communication reduces the requirement for long-range transmission. 4. Each node in the WSN can act as a repeater, thereby reducing the communica- tion link range coverage. 1.5.2 Memory The sensor node generally has a very small amount of memory in the processing unit for the storage of data and algorithm [3–5]. This memory is in the form of RAM and ROM of processor of the sensor node. Due to the limited memory capacity of the sensor node, there does not exist enough memory to execute complex algorithms especially after loading the OS. For instance, consider the case of Smart Dust proj- ect. In this project it is found that TinyOS consumes around 4 Kb for instructions, leaving only 4.5 Kb for applications [3]. 1.5.3 Deployment, Topology, and Coverage Depending on the application requirement, the nodes in the WSN can be placed in a planned fashion or in a random fashion [20, 31–33]. The node deployment in the monitoring area can be a periodic or a one-time activity. Node deployment has impact on important network parameters, such as coverage, node density, reliability, sensing resolution, communications, and task allocations. The WSN generally oper- ates in dynamic environment due to uncertainty in operating conditions, e.g., due to abrupt changes in the environmental setup, node mobility, and node failures. Due to such dynamicity in the operating environment, the communication links between sensor nodes frequently break even when nodes are static. Another disadvantage of this dynamicity is frequent changes in the WSN topology, which in turn affects many network characteristics such as robustness, latency, and capacity. The level of 1.5 Design Constraints for WSN
- 31. 12 complexity in data routing and processing also depends on the network topology. Coverage is a measure of coverage area of a WSN. It can be sparse, i.e., only parts of the environment fall under the sensing envelope, or dense, i.e., most parts of the environment are covered. Coverage can also be redundant, i.e., the same physical space is covered by multiple sensors. Coverage is mainly determined by the sensing resolution demands of an application. 1.5.4 Communication and Routing As the WSN generally has limited bandwidth, processing, and energy, it operates in highly uncertain, remote, and hostile environments [18, 34]. Therefore, the network continuously undergoes changes in its topology and coverage due to frequent node failures and noisy measurements. Due to very heavy deployment, its nodes lack global identification as well. Thus, data routing is a very critical issue in such condi- tions. Therefore, designing appropriate routing scheme highly depends upon the underlying application requirement. Popular WSN routing schemes are sensor pro- tocols for information via negotiation (SPIN), constrained anisotropic diffusion routing (CADR), active query forwarding in sensor networks (ACQUIRE), low- energy adaptive clustering hierarchy (LEACH), power-efficient gathering in sensor information systems (PEGASIS), and threshold-sensitive energy-efficient sensor network protocol (TEEN) [3–5, 8]. 1.5.5 Security Sensor networks are vulnerable to several key attacks. Most popular are eavesdrop- ping (adversary manages to listen data and communication), denial-of-service attacks (a particular node denies to execute the network tasks), Sybil attack (mali- cious nodes manage to get multiple identities to disrupt routing, resource allocation, and data aggregation), physical attacks (adversary manages to sensor node tamper- ing), and traffic analysis attacks (adversary manages to reconstruct network topolo- gies) [7, 18, 35]. Therefore, network security is a very essential aspect in the WSN, especially if it deployed in enemy prone or secret environment. Continuous research is going on to propose appropriate defenses to protect the sensor networks against attacks. Speaking in more technical words, the security in the WSN refers to ensure three important data centric aspects: 1. Data confidentiality: It means an adversary must not be able to steal and inter- pret data. 2. Data integrity: An adversary must not be able to alter or damage data. 3. Data availability: An adversary must not be able to disturb data communication link between source nodes and sink node of the WSN. 1 Fundamentals of Wireless Sensor Networks
- 32. 13 1.5.6 Production Costs As we know the WSN generally consists of several hundreds or even thousands sen- sor nodes [3, 4, 8]. Therefore, the cost of a single node is crucial to decide the over- all cost of the WSN. If deploying the WSN is costlier than deploying traditional sensors, then the WSN is not at all cost justified. Therefore, the cost of each sensor node must be as low as possible for the sensor network to be feasible. Now a day, due to advancement in Bluetooth technology, the cost of a sensor node is around only 1–2$. 1.5.7 Fidelity and Scalability Scalability broadly refers to how well all the operational specifications of a sensor network are satisfied with a desired fidelity, as the number of nodes grows without bound [3, 4, 8]. Based on the operating environment and the phenomenon to be observed, fidelity can cover various performance parameters, such as spatial and temporal resolution, misidentification probability, consistency in data transmission, latency of event detection, and event detection accuracy. Depending on the measure of fidelity, scalability can be formulated in terms of reliability, network capacity, energy consumption, resource exhaustion, or any other operational parameter as the number of nodes increases. Thus, there exists high level of trade-off between scal- ability and fidelity. Therefore, one has to decide scalability and fidelity for the designed sensor network, depending upon the application requirement. 1.6 Existing WSN Platforms History of design and deployment of the WSN dates back to the World War II [3, 4, 8]. A platform of acoustic sensors was developed by the USA to detect and track Soviet submarines for sound surveillance. It is currently used by the National Oceanographic and Atmospheric Administration (NOAA) for detecting and moni- toring events, such as seismic and animal activity in the ocean. In 1980, the research on the WSN-entitled distributed sensor networks (DSN) was carried out at DARPA (Defense Advanced Research Projects Agency). The network consisted of many spatially distributed, low cost, autonomous sensing nodes that collaborate among each other for data routing. A number of such research attempts on the design and development of the WSN have been reported in the history. At present there is no such common WSN platform to be used for a specific application. The platform of Berkeley motes and their variants have wider user and developer communities. It is quite less expensive to build our own WSN platform for intended application in mind than to buy commercially available platforms. Therefore, a popular trend to 1.6 Existing WSN Platforms
- 33. 14 design and produce own WSN setup has been established for the last two decades among many researchers, RD labs, and commercial companies prefer. Some of these research attempts and related projects are explained in the following sections: 1.6.1 Wins The University of California in association with the Rockwell Science Center devel- oped wireless integrated network sensors (WINS) project, which was later on com- mercialized with the Sensoria Corporation (San Diego, California) in 1998 [3, 4, 8]. This project covered almost all the aspects in the WSN design right from MEMS sensor and transceiver integration at the circuit level, network protocol design, and signal processing architectures to the fundamentals of its sensing and detection theory. The project concluded that WINS would provide distributed networking and Internet accessibility to sensor nodes, task controls, and adding embedded proces- sors with the node. 1.6.2 Eyes The Infineon developed energy-efficient sensor networks (EYES). This project was funded by the European Union (EU) to design and develop the technology and architecture of wireless sensors that can be networked with large number of mobile nodes [3, 4, 8]. The project eyed at supporting devices such as PDAs, laptops, and even mobile phones. The developed sensor nodes are equipped with a TI’s MSP430 processor, SAW filter, radio device TDA 5250, and transmission power control. Each sensor node has a USB port for interfacing to a PC. These sensor nodes also have provision to add extra sensors as well as actuators, depending upon the appli- cation demand. 1.6.3 Pico-Radio In 1999, the Pico-Radio project started at the University of California to support the development of low-cost, low-energy sensor nodes with ad hoc capability. The pro- posed for the Pico-Radio network has physical layer with direct sequence spread spectrum and the MAC protocol with the application of carrier sense multiple access (CSMA) and spread spectrum techniques [4–7]. The important findings of this proj- ect are as follows: (1) The node can randomly select a channel and monitor the network activity. (2) If the channel is currently engaged, the node can search for another channel from the list of the remaining available channels. Once an idle 1 Fundamentals of Wireless Sensor Networks
- 34. 15 channel is detected, the scanning is stopped. (3) In case the idle channel is not found, the node would back off and set a random timeout timer for each channel. (4) It can then use the channel which has first expired timer. Then, the timers for the other channels are cleared off. 1.6.4 Mica Mote Family The sensor nodes of Mica mote family are developed at the University of California, Berkeley. This project started in partial collaboration with Intel in the late 1990s. These sensor nodes are commonly referred as Mica motes, with different variants such as Mica, MicaZ, Mica2, and Mica2Dot, which are commercially sold via the Crossbow company [3, 4, 6, 7]. The OS in these products is TinyOS. The TinyOS is coded in the nesC language with a component-based protocol. The Mica motes use a processor from Atmel family (usually ATmega128L 8-bit processor running at 7 MHz) and a radio modem from RFM (usually it is TR 1000). In Mica motes sen- sors are interfaced to the controller using I2C or SPI protocols. Power to Mica motes is provided via two AA batteries of current capacity of 2000 mAh. The Chipcon transceivers are generally employed in Mica motes. For instance, in Mica2 mote has Chipcon CC1000 transceiver, which operates on the 868/915 MHz band with data rate of 38.4 kbps. In MicaZ the Chipcon CC2420 transceiver operating in unlicensed 2.4 GHz band with data rate of 250 kbps is used. It uses offset quaternary phase-shift keying (O-QPSK) as a modulation technique. 1.7 Applications of WSN Due to the continuous development in the WSN technology, the assets of national importance such as aircrafts, ships, and even buildings can detect structural faults on time (this application area is popularly known as structural health monitoring) [7, 26, 36–38]. The WSN also has paved a way to design and develop systems that provide useful prior alerts before earthquake and tsunami. The WSN also has exten- sive applications in the battlefield for surveillance and reconnaissance. The WSNs can be used in critical applications such as earthquake, tsunami, battlefield, and flood and also in enemy intrusion detection, target tracking, forest fire detection, industry monitoring, structural monitoring, and environmental and biological moni- toring. Although it covers a broad range of diverse application areas, few of them are described below: 1.7 Applications of WSN
- 35. 16 1.7.1 Military Applications The sensor network research was originally motivated from the military needs [3, 4, 6, 8]. The demand in military-based applications includes energy conservation, rapid deployment of assets, as well as robust sensing along the rivers and in hostile terrains. The typical military applications are listed below: • Monitoring and tracking enemy forces and monitoring friendly forces • Monitoring equipment and inventory • Reconnaissance • Surveillance of war area • Assessment during war damage • Nuclear, biological, and chemical attack detection • National border monitoring 1.7.2 Environment Monitoring Applications The WSN has been proved to be the ideal choice for many environment monitoring applications due to its capability of unattended operation. The typical environment- related applications are listed below: • Weather sensing and monitoring stations • Forest fire detection • Habitat monitoring • Monitoring pollution level of water, land, and air • Flood detection • Precision agriculture • Endangered species population measurement • Tracking migrations of bird and endangered wild animals • Soil erosion detection 1.7.3 Health Applications Many times humanly monitoring of patients or medical equipment during complex surgery in big hospitals is impossible [39–41]. The wireless sensors in such situa- tion can assist the doctors and hospital administration to execute various tasks accu- rately and appropriately. The typical health systems-related applications wherein the WSN is involved are listed below: • Physiological data monitoring remotely • Locating and tracking of patients and doctors inside a hospital • Administrating drug remotely 1 Fundamentals of Wireless Sensor Networks
- 36. 17 • Assistance to elderly people 1.7.4 Home Applications The improvement in the quality of life by creating secure and intelligent living spaces for humans is the underlying idea behind smart homes [39, 42, 43]. The WSN finds a huge potential for applications in the area of smart homes. The typical home automation-related applications are listed below: • Home automation • Instrumented environment • Automated meter reading • Tracking system for child and elderly people 1.7.5 Other Commercial Applications Sensor networks are also proved to be highly useful in some of the commercial applications of national importance [39, 42, 43]. In commercially important appli- cations, the WSN can not only provide reliable measurements using which localiza- tion of important entities can be done efficiently. • Monitoring nation’s critical resources such as power industrial plants, tunnels, communication grids, and parks • Ambient temperature control in office and industrial buildings • Inventory management and control • Landslide detection systems • Vehicle tracking and detection • Traffic flow surveillance on highways • Air traffic control stations References 1. Y. Zhang, L. Sun, H. Song, X. Cao, Ubiquitous WSN for healthcare: Recent advances and future prospects. IEEE Internet Things J. (2014). https://doi.org/10.1109/JIOT.2014.2329462 2. S. Gezici et al., Localization via ultra-wideband radios: A look at positioning aspects of future sensor networks. IEEE Signal Process. Mag. (2005). https://doi.org/10.1109/ MSP.2005.1458289 3. I. F. Akyildiz, T. Melodia, K. R. Chowdhury, A survey on wireless multimedia sensor net- works. Comput. Netw. 51(4) (2007). https://doi.org/10.1016/j.comnet.2006.10.002 References
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- 39. 21 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. R. Jondhale et al., Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-74061-0_2 Chapter 2 Target Localization and Tracking Using WSN 2.1 Introduction to WSN-Based LT Indoor LT of target is useful for many applications in several sectors, such as the manufacturing, sports, healthcare, and construction [1–6]. For instance, in the healthcare sector, locating and tracking the locations of objects can be very crucial whenever and wherever there is high emergency to respond to. For instance, in hos- pitals many employees need to share the same hospital assets during work. These items many times are moved from their regular location and not returned to the original location after the work is finished. In the manufacturing sector, the knowl- edge of location of the finished products and other items in a warehouse can help in asset management by keeping a track on the inventory and lowering the searching time to find them. The person unfamiliar with the given built environment (e.g., a large building) can be provided with location map of the area in order to search out the routes toward intended destination. Critical information objects (e.g., objects in museums or computer hard-drives) or high-valued assets can also be exposed to possible thefts. In other words, location knowledge can provide theft detection as well as prevention by giving useful alerts about whenever they are shifted outside from the predefined boundaries by some unauthorized persons. Target LT is one of fundamental applications of the WSN, wherein the main objective is to detect and locate the mobile target and also to keep a track on its movement path (trajectory) continuously with the help of field measurements from sensor nodes [7–10]. This is termed a single target LT problem; although if the problem involves LT of multiple mobile targets with the help of WSN-based setup, then it is termed as a multi-target LT problem. The low maintenance cost, simple and random deployment procedure, ad hoc nature, and the possibility of unattended operation make WSN a vital option for various indoor LT applica- tions. The WSN can easily locate and track the trajectory of the moving target by
- 40. 22 simply exploiting field measurements once deployed randomly. In these problems, the sensor nodes are deployed at random or predefined locations in the sensing environment. Consider a general target LT scenario using WSN as shown in Fig. 2.1, wherein the target is moving inside the WSN monitored area along a predefined or unknown path. The sensor nodes in the WSN based LT system are designated as detecting nodes (the nodes which are in the vicinity of the mobile target and able to detect the target), vigilant nodes (the nodes which are likely to detect the target in the future), and inactive nodes (which are not at all utilized in the LT process). The mobile target can be any object, such as asset, an animal, an intruder, a vehicle, or a person [7–10]. Figure 2.2 shows the basic procedural steps executed in target LT mecha- nism. It consists of detection of a target during its motion in the WSN monitored area, localization to locate the mobile target, and tracking to trace the route of mobile target. The WSN-based LT may also be classified as single target vs multiple target LT, active vs passive LT, indoor vs outdoor LT, and two-dimensional (2-D) vs three-dimensional (3-D) LT [7, 11–14]. If the target cooperates in localization, then it is termed as active LT; otherwise, it is called as passive LT. In the former case, the target is with a sensor node, and the rest of the WSN nodes can detect and locate the target. In the latter case, the target is “device-free,” wherein the target is not equipped with a WSN node. This book is intended to discuss the design and development of LT algorithms to efficiently track a single mobile target in an indoor environmental setup by exploiting field measurements. Fig. 2.1 General target LT scenario using WSN Target Field Measurements Detection of Target Localization of Target Tracking of Target Fig. 2.2 General mechanism of target LT 2 Target Localization and Tracking Using WSN
- 41. 23 The dramatic technological revolution in smartphones, wearable wireless devices, and WSN in the last decade has come up with a wide variety of useful applications, including indoor LT applications [7, 11–14]. Indoor LT is the pro- cess of achieving user location, which can be utilized in a wide range of applications in health sector, disaster management, smart home, and surveillance. It is also proved to be beneficial in many important areas, such as smart cities, smart struc- tures, and smart grids. In the context of the WSN-based LT for the indoor setup, there are two types of sensor nodes, namely, anchor nodes (also called as reference nodes) and non-anchor nodes [7]. Generally, the anchor nodes are deployed at known locations, whereas locations of non-anchors nodes are unknown. The mov- ing target is assumed to carry one non-anchor node. The target locations during its movement are estimated with the help of anchor nodes through internode commu- nications. However, environmental issues, such as signal fading, multipath propaga- tion, and non-line of sight (NLOS), pose the major challenges in achieving high tracking accuracy. The WSN-based tracking systems must also be robust enough to deal with abrupt variations in target velocity as well as variation in target mobility pattern. Therefore, the researchers from academia and industry need to propose efficient LT algorithms with reference to the challenges mentioned above. 2.1.1 Typical LT Scenario in Wireless Sensor Networks The target LT in an indoor environment using WSN enables a wide variety of applications [1–4]. As discussed earlier, the mobile target can be any object, such as an asset, an animal, an intruder, a vehicle, or a person. Sometimes the mobile target moves along a predefined path, and sometimes the target path is unknown. A typical scenario of target LT using WSN is shown in Fig. 2.3. The target state at any time instance k can be given by the state vector X x y x y k k k k k = ( ) , , , ’ , wherein xk and yk are the target locations and xk and yk are the target velocities in x and y directions, respec- tively. One may augment acceleration parameters xk ¨ and yk ¨ along x and y directions, respectively, in the above the state vector. During the target motion in the WSN, the state vector changes. The objective of the deployed WSN is to estimate continuously the state vector using field measurements from the environment with the help of a suitable LT algorithm [11, 15–18]. Thus, for the case of mere target localization, it is one time estimation problem, whereas for the case of target tracking, it is a sequen- tial state estimation problem. That means the algorithms that are used for target tracking problem are the same as that for the target localization problem. At the end of a state vector estimation, we are interested to know about how the LT algorithm performed in the context of target LT for the considered system design and assump- tions. The performance evaluation parameters that are generally used for target LT are localization error, RMSE, or both. The lower the values of these performance evaluation parameters, the higher will be the target LT accuracy. As discussed in the last paragraph, the target tracking being a sequential localiza- tion problem, it needs the location estimation algorithm of recursive nature [16–19]. 2.1 Introduction to WSN-Based LT
- 42. 24 One may call this recursive location estimation algorithm as the target tracking algo- rithm. Several factors that impact the performance of the target tracking algorithm are the following: the type of environment (indoor/outdoor), type of field measure- ment involved, density of obstacles in the environment, algorithmic design, and den- sity of anchor and non-anchor nodes.Apart from these system design issues, the field measurement also faces the problem of signal propagation issues, such as signal fading, reflections, NLOS conditions, and multipath propagation. Therefore, to develop a robust and high precision target LT system for an indoor environment is a highly challenging task. Due to such environmental dynamicity, the existing target LT systems suffer with a low LT accuracy (i.e., if localization error is higher than 1 m, then it can be considered as low LT accuracy). In addition to issues of system design and environmental dynamicity, some other issues, such as abrupt changes in target velocity, during motion and availability of less field measurements can also deteriorate the performance of the LT algorithm further. Therefore, research has been continuously going on to design and develop robust and accurate target LT systems, which can offer higher target localization accuracy (i.e., localization error lower than 1 m), real-time performance, and lower computational simplicity. 2.1.2 Classification of Target LT Techniques As discussed several times previously, the WSN utilizes the field measurements to locate the mobile target. Based on the involvement of distance of the target from the anchor nodes in the computation or estimation of the unknown locations of the mobile target during its motion, the LT algorithms can be divided into two major classes: range-based LT and range-free LT as shown in Fig. 2.4 [9, 11, 20]. If the target LT algorithm depends upon the distance (range) between target and Fig. 2.3 Typical target tracking scenario using WSN 2 Target Localization and Tracking Using WSN
- 43. 25 anchor nodes during estimation, then it is termed as range-based algorithms; other- wise, it is termed as range-free algorithms. Unlike range-based approach, in the range-free approach the connectivity of sensor nodes is utilized to locate the moving target rather than the distance between the target and anchor node. The target LT accuracy of range-based algorithms is generally high as compared to its counterpart range-free algorithms. However, looking from hardware perspectives, the range- free algorithms require additional hardware when compared with range-free algo- rithms. The overall comparison between range-free and range-based approaches is presented in Table 2.1. The range-based approach utilizes field measurements, such as the time of arrival (ToA), angle of arrival (AoA), received signal strength indicator (RSSI), and time difference of arrival (TDoA) [7, 21]. In the AoA, the angles of arrival of signals between target and anchor nodes are utilized to locate the moving target. Although the AoA technique does not need clock synchronizations between transmitters and receivers, the need of an array of directional antennas is its main limitation. In the ToA-based LT approach, the signal propagation velocity and the time of arrival of the transmitted signal are exploited to calculate the distances from transmitter to receiver, whereas in the TDoA-based approach, the time difference of arrival of sig- nals coming from the transmitter and receiver is utilized. The major drawback of TDoA and ToA techniques is the need of exact time synchronization between the transmitter and receiver clocks, the susceptibility to NLOS conditions, interferences, and measurement noise. The additional hardware requirements in AoA, ToA, and TDoA range-based techniques make the LT system expensive and bit complex. Unlike AoA, ToA, and TDoA range-based LT approaches, in the RSSI- based LT approach, there is no such requirement of additional hardware for the target LT. In the RSSI LT approach, the distance between the target and anchor nodes using a suitable path loss model is utilized to locate the target. The prerequisites for the path loss model are knowledge of the transmitted and received signal powers, transmitting Fig. 2.4 Classification of WSN-based LT 2.1 Introduction to WSN-Based LT
- 44. 26 and receiving antenna gains, and operating frequency. The pros and cons associated with these field measurements are given in detail in Table 2.2. The range-free techniques are classified as hop count-based technique (e.g., DV Hop) and pattern matching-based technique (e.g., approximate point in triangle (APIT)) [11, 20, 22, 23]. Basically, these both approaches are area-based methods. In DV-Hop-based LT approach, the unknown location of node (or target) is com- puted by counting the number of hops the RF signal takes to reach the destination. In APIT-based LT approach, the information such as whether the node (or target) is within a predefined area or not is utilized. These both approaches do not provide the exact location of the target; instead of that, they provide the area in which the target is. An artificial neural network (ANN) can be used in both range-based and range-free methods. As this book is intended to discuss the fundamentals of the RSSI-based target LT approach only, the rest of the other approaches are out of the scope of this book. The detailed discussion of the RSSI-based target LT approach is discussed in detail in the next section. Table 2.1 Comparison between range-based LT and range-free LT Parameters Range-based approach Range-free approach Additional hardware Required Not required Localization accuracy Approximately 80–90% Approximately 60–75% Power consumption High Low Robustness High Low Deployment Generally hard Generally easy Table 2.2 Types of measurements involved in WSN-based target tracking Measurement type Procedure Pros Cons ToA Distance- based Moderate accuracy Need for transmitter and receiver clocks and their perfect synchronization; errors due to NLOS conditions, signal noise, and interferences TDoA Distance- based High accuracy Need for transmitter and receiver clocks and their perfect synchronization; errors due to NLOS conditions, signal noise, and interferences AoA Angle- based High accuracy Requirement of directional antenna array RSSI Distance- based No need for additional hardware, low cost, and low power consumption RSSI measurements are susceptible to environmental dynamicity and moderate accuracy 2 Target Localization and Tracking Using WSN
- 45. 27 2.2 RSSI-Based Target LT Approach The RSSI is basically the measure of the magnitude of power received at the receiver terminal. The RSSI measurements during RF communication are obtained very eas- ily at the receiver during normal communication [11, 15, 16, 24, 25]. As discussed in the previous section, the LT system based on the RSSI measurements neither needs an array of directional antennas nor needs synchronization between the receiver and transmitter clocks. Each wireless sensor node is with on-chip RSSI circuit, which can give the values of RSSI measurements. Thus, there is no need for additional hardware in the RSSI-based target LT approach. Hence, the RSSI met- ric has been dominantly used in the WSN-based target LT systems. Compared to other counterparts, few other important advantages associated with the RSSI-based LT approaches are as follows: simpler procedural aspects and lower power consumption. Theoretically speaking, the RSSI is a function of distance between the receiver and transmitter and the RF environment, in which the WSN or other wireless system is deployed. Therefore, due to the dependence on the RF channel, the RSSI-based LT algorithms are generally affected by changes in the environmental setup [11, 15, 16, 24, 25]. In fact, in the RSSI-based approach, the distance between the receiver and the transmitter is computed using the difference between magnitudes of transmitted power and that of received power. This power difference is termed as signal attenuation or path loss. Therefore, the utmost care is to be taken to choose an appropriate path loss model to characterize the given RF channel. Speaking in more technical words, the RSSI is a part of the IEEE 802.11 protocol family. The RSSI values are measured in dBm unit. The RSSI values generally fall between 0 dBm (excellent signal) and −110 dBm (very poor signal) and are negative [26, 27]. In the indoor LT applications with WSN, the RSSI measurement-based approach is generally used as compared to the rest of the alternatives. In these appli- cations for indoor environmental setup, the aspects that are of prime importance to the success of the underlying application are the following: selections of path loss model, density and locations of non-anchor and anchor nodes, selection of suitable transmission power level algorithmic design, and issues related with signal propa- gation, such as fading, reflections, NLOS conditions, and multipath propagation [15, 16, 19, 28, 29]. In most of the RSSI-based indoor LT applications, it is assumed that the target carries one sensor node, which is configured in the transmitter mode, whereas the rest of the sensor nodes of the WSN are configured in the transceiver mode. All the RSSI-based target LT algorithms discussed in this book from Chap. 4 onward are based on this assumption, although some applications in the literature also assumes the target configured in the receiver mode to collect the measurements from the sur- rounding sensor nodes. In the first case, the target broadcasts RF signal in the sur- rounding WSN environment, while the rest of the sensor nodes in the network collect the RSSI measurements of this broadcasted signals. Using the collected RSSI measurements, the distance between the target and the sensor node can be 2.2 RSSI-Based Target LT Approach
- 46. 28 computed using a suitable signal path loss model. Let’s consider a typical scenario showing the use of RSSI measurements to obtain the unknown location of the target as shown in Fig. 2.5. If the target is in the communication range of the three trans- mitting nodes (anchor nodes), then at the target (which carries a sensor node config- ured in the receiver mode) three RSSI measurements are received. Then, by using a suitable signal path loss model, one can very easily get three distances of the target from these three anchor nodes. Using these coordinates of the three anchor nodes and three computed distances, the unknown location of the target can be computed very easily. The lower the actual distance between the anchor node and the target node, the higher will be the value of the RSSI measurement and vice versa [11, 30, 31]. The received RSSI measurement is found to have a highly nonlinear relation- ship with the distance as shown in Fig. 2.6. The RSSI measurements are generally erroneous due to the issues related with signal propagation, such as attenuation, reflections, fading, NLOS conditions, and multipath propagation [11, 30, 31]. In fact, the RF wave reaches the destination along the different paths of varying length (multipath propagation), and thereby it takes different travel times along these paths. Thus, these components of the same RF signal reach the destination at different times with varying amplitudes. The interaction of these RF components with each other causes multipath fading. That means, these components interfere with each other. These interferences at the receiver can be destructive or constructive. The major reason of multipath propaga- tion and fading is the varying amount of obstacles in the given environment along different paths, and thereby, the RF signal components, along each path, experience varying amount of reflections. The NLOS is the condition wherein the antennas of the transmitting and the receiving nodes are not along a LOS. Therefore, the received RSSI measurements are not reliable, though environment is kept unchanged. The slight changes in location of experiment can also cause variations in the amount of attenuation, reflections, fading, multipath propagation, and NLOS. Even with the same environmental setup, the same RSSI measurements are not guaran- teed. In other words, there exists less possibility of repeatability and regularity in the RSSI measurements. Thus, the RSSI measurements are highly notorious and Anchor Node 1 Anchor Node 3 Anchor Node 2 RSSI3 RSSI1 RSSI2 Fig. 2.5 RSSI measurements for target LT 2 Target Localization and Tracking Using WSN
- 47. 29 dependent on the environment setup. Due to all these characteristic features and limitations in the RSSI measurements as discussed above, the RSSI-based LT system is generally associated with low localization accuracy and low stability [11, 30–33]. In order to avoid this problem, some of the precautionary measures reported in the literature are as follows: • Take the RSSI measurements at several frequency. • Take the average RSSI measurements over a suitable time period to smooth vari- ations in the RSSI measurements. • Calibrate WSN transceivers to get a comparable reception sensitivity and emis- sion power. • Use high-quality antennas. • Try to minimize changes in the environment setup and signal interference from the surrounding electronic gadgets, rain, and mobile objects. 2.3 Environmental Characterization Through Path Loss Models As discussed earlier to locate a target using RSSI-based technique, characterization of the given RF channel is a must, and for its characterization, the selection of a suitable path loss model is highly essential. The path loss model translates the RSSI 0 10 20 30 40 50 60 70 80 90 100 -40 -30 -20 -10 0 10 20 30 40 50 Distance, [m] ] m b d [ t n e m e r u s a e M I S S R ¬ RSSI Curve RSSI versus Distance Curve Fig. 2.6 Nonlinear relationship between RSSI and distance 2.3 Environmental Characterization Through Path Loss Models
- 48. 30 measurements into distances. Therefore, selecting the appropriate model for the target localization and tracking is the key to success. A correct understanding and modeling of the RF propagation channel is a vital prerequisite for improving the target LT accuracy. Basically, a path loss model is a set of mathematical expressions, algorithms, and diagrams, which represents the radio characteristics of the considered RF environ- ment in which the target resides [9, 14, 26, 33]. The empirical models of a path loss model are based on the actual RSSI measurements, whereas the theoretical models of a path loss model are based on the fundamental principles of RF communication. Popular RSSI path loss models for RF environment characterization are the follow- ing: free space propagation model, log normal shadowing model (LNSM), and two- ray ground model. The modified versions of these basic models have also been reported in the literature. Few researchers in LT domain have also designed their own path loss models to characterize the given wireless environment. For instance, the authors in [9] have presented the optimal fitting parametric exponential decay model (OFPEDM). The OFPEDM is developed for large-scale wheat field. The author claim that the OFPEDM has less susceptibility to variations in the RF envi- ronment and higher distance estimation accuracy. The free space propagation model and two-ray ground model have specific requirements for the underlying applica- tion environment, whereas the LNSM model is more general in nature. The LNSM is sometimes also called as log normal shadow fading model. Out of all of these, the LNSM is more suitable in RSSI-based LT applications for indoor as well as out- door environmental setup. It presents a number of configurable parameters using which the given RF environment can be artificially simulated. Let’s discuss mathe- matics of all of these path loss models in detail. 2.3.1 Free Space Path Loss Model The free space path loss model provides the RSSI measurement if the transmitter and the receiver are along a LOS without any obstacle in between [9, 26, 34, 35]. This model is basically based on a well-known Friis transmission formula. It relates the antenna gains, free space path loss, and wavelength to the transmitted and received powers. This equation is one of the fundamental equations in RF commu- nication and antenna theory. In this mathematical equation, if d is the distance between the receiver and the transmitter, then the RSSI measurement at the receiver is denoted as Pr(d). According to this model, the ratio of received power to transmit- ter power is given as the following: P P G G d P d PG G d r t t r r t t r = × → ( ) = ( ) λ π λ π 4 4 2 2 2 2 (2.1) 2 Target Localization and Tracking Using WSN
- 49. 31 where Pt and Pr(d) are the transmitted power and the received power, respectively; Gt and Gr are the transmitter antenna gain and the receiver antenna gain, respec- tively; and λ is the signal wavelength in meters. By rearranging the above equation, one can easily obtain the distance between the transmitter and the receiver. The Friis equation states that more power is lost at higher frequencies, which is a fundamental result of this equation. In other words, it can be stated that for anten- nas with some specified gains, the power transfer will be highest at lower frequen- cies. That means the higher the frequencies, the higher would be the path loss associated. As accurate LOS between the transmitter and the receiver is not always the reality in most of the cases, the estimated RSSI measurements with the help of this model are not reliable, and thus it generally leads to high localization error in the target LT applications. 2.3.2 Two-Ray Ground Model The major drawback of free space path loss model is the dependence on the LOS between the receiver and the transmitter [9, 26, 34, 35]. The two-ray ground model does not necessitate the requirement of the LOS. It is basically based on geometry of the given RF environment and pays attention to the direct path as well as the ground reflected path between the receiver and the transmitter (see Fig. 2.7). The estimated RSSI using the two-ray ground model is fairly accurate as compared to that using the free space path loss model. According to the two-ray ground model, the RSSI (received power) is given as [34, 35]: P d PG G h h d r t a b t r ( ) = 2 2 4 (2.2) where Ga and Gb are the receiver and the transmitter antenna gain, respectively, and hr and ht are the heights of receiver antenna and transmitter antenna, respectively. By rearranging the above equation, one can easily obtain the distance between the transmitter and the receiver. Fig. 2.7 Two-ray ground model 2.3 Environmental Characterization Through Path Loss Models
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