Exploring Wireless A Comprehensive Review on Sensor Node Integration and Energy Optimization Strategies for Enhanced Environmental Data Collection

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 8 Issue 1, January-February 2024 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD62401 | Volume – 8 | Issue – 1 | Jan-Feb 2024 Page 315
Exploring Wireless: A Comprehensive Review on Sensor Node
Integration and Energy Optimization Strategies for Enhanced
Environmental Data Collection
Vijay Malviya, Dr. Sachin Patel
Department of Computer Science & Engineering,
Sagar Institute of Research and Technology, Indore, Madhya Pradesh, India
ABSTRACT
WSNs encompass a multitude of spatially distributed sensor nodes or
devices employing radio signals for communication. Positioned
strategically in a geographical area, these sensor nodes operate
independently to collect information from their surroundings. Given
their often remote and inaccessible locations, human interaction with
deployed sensor nodes is limited. The core function of sensor nodes
in WSNs involves sensing environmental data and transmitting it to a
centralized base station or sink node. Subsequently, the collected data
undergoes analysis, demonstrating the vital role of WSNs in
facilitating data-driven insights within the realm of computer science.
In this paper review of different research paper on the based of
wireless sensor networks technology and different sensors for
optimization of energy dissipation.
KEYWORDS: Particle Swarm Optimization (PSO), Sensor Networks,
Wireless Sensor Networks, LEACH
How to cite this paper: Vijay Malviya |
Dr. Sachin Patel "Exploring Wireless: A
Comprehensive Review on Sensor Node
Integration and Energy Optimization
Strategies for Enhanced Environmental
Data Collection" Published in
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Scientific Research
and Development
(ijtsrd), ISSN:
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Volume-8 | Issue-1,
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1. INTRODUCTION
One issue that has arisen around the world is that of
energy efficiency, that is, it refers to the intelligent
consumption of energy because most of the energy
sources are finite, and what is sought is to have a
consumption of energy. responsible in the present so
that future generations can continue to enjoy them.
According to different studies carried out, a
considerable increase in the demand for residential
electricity is expected within the following decades,
so that our traditional electricity networks will not be
able to meet the requirements of the 21st
century [1-
3]. But there have been two major drawbacks for
these energy management systems: the large number
of residential homes without adequate automation
systems that are efficient and the high cost of
implementing them [4-7]. For this reason, for this
type of energy management, changes will have to be
made in terms of the way in which energy is supplied,
and the form of the energy market [8], which requires
different types of networks, such as they are wireless
sensor networks, as well as different energy
management systems within smart homes. This article
focuses on the implementation of these energy
management systems using wireless sensor networks,
which by maximizing coverage as a basis will allow
better and more extensive services to users. It should
be noted that this corresponds to the issue of smart
grids, which through the use of smart meters, sensors
and different actuators will allow obtaining more
detailed information on the consumption of each
residential area, and even obtain individual
consumption. of each of the electrical and electronic
devices within a specific dwelling, with which you
can have a remote control of them [9]. We can also
mention that wireless networks currently play a very
important role in the improvement of technology and
our quality of life, because they allow us to have a
IJTSRD62401

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
great freedom to communicate with the world at any
time and anywhere [10-12].
Although very advanced signal processing algorithms
exist and have been adopted by the wireless sensor
networks, most analytical studies on the coverage of
these networks are carried out in excessively
simplistic models, as is the case of the disk model,
which does not capture the stochastic nature of
detection [4]. This disk model has been analyzed
several times and its limitations in obtaining optimal
results has been understood and it has provided key
information for the design of wireless sensor
networks that in some cases have adopted algorithms
of data fusion [8]. While on the other hand, studies
have been carried out taking into account the problem
of coverage in the wireless networks of sensors,
which is similar to some computational geometry
problems. For this reason, due to its special
geometrical properties, the Voronoi algorithm has
been used in this field of wireless sensor network
research, especially in the problems related to the
coverage of the sensors. Some authors have come to
use this algorithm to propose repair methods based on
triangular mesh models [9]. In addition, with the
implementation of this algorithm some of the
problems related to coverage have been solved, that is
to say, it is possible to obtain a fairly good coverage
of the sensors. However, there is little research on
each Voronoi polygon, formed by the corresponding
points to reach maximum coverage [10-14].
It is a method that can be widely used in the wireless
sensor networks in order to solve the problems of
coverage and low accuracy in locating the centroids
[15]. A coverage strategy based on regions and
different parameters is proposed, such as distance of
coverage, capacity and percentage of coverage. It
should also be noted that in comparison with
traditional centroid algorithms, the proposed strategy
offers some advantages, such as less localization
error, less time consumption, greater optimization and
better stability with respect to location.
Figure 1.1: Wireless sensor network for indoor infrastructure
2. LITERATURE REVIEW
SonamMaurya, in paper “Hybrid Routing Approach
for Heterogeneous Wireless Sensor Networks using
Fuzzy Logic Technique”, proposed a fuzzy logic
technique. The proposed fuzzy logic technique is used
with region-based clustering technique for cluster
head selection. The technique reduces the overall
consumption of energy in route selection process by
implementing the fuzzy information.
Wendi Rabiner Heinzelman, in paper “Energy-
Efficient Communication Protocol for Wireless
Microsensor Networks,” proposed a protocol with
rotation of cluster base station. Communication
protocols have significant impact on the overall
energy dissipation of these networks. Based on our
findings that the conventional protocols of direct
transmission, minimum-transmission-energy,
multihop routing, and static clustering may not be

optimal for sensor networks, we propose LEACH
(Low-Energy Adaptive Clustering Hierarchy), a
clustering-based protocol that utilizes randomized
rotation of local cluster base stations (cluster-heads)
to evenly distribute the energy load among the
sensors in the network. LEACH uses localized
coordination to enable scalability and robustness for
dynamic networks, and incorporates data fusion into
the routing protocol to reduce the amount of
information that must be transmitted to the base
station. Simulations show that LEACH can achieve as
much as a factor of 8 reduction in energy dissipation
compared with conventional routing protocols.
LEACH is a clustering-based routing protocol that
minimizes global energy usage by distributing the
load to all the nodes at different points in time.
LEACH outperforms static clustering algorithms by
requiring nodes to volunteer to be high-energy
cluster-heads and adapting the corresponding clusters
based on the nodes that choose to be cluster-heads at
a given time. At different times, each node has the
burden of acquiring data from the nodes in the cluster,
fusing the data to obtain an aggregate signal, and
transmitting this aggregate signal to the base station.
LEACH is completely distributed, requiring no
control information from the base station, and the
nodes do not require knowledge of the global network
in order for LEACH to operate. Distributing the
energy among the nodes in the network is effective in
reducing energy dissipation from a global perspective
and enhancing system lifetime. Specifically,
simulations show that LEACH reduces
communication energy by as much as 8x compared
with direct transmission and minimum transmission-
energy routing. The first node death in LEACH
occurs over 8 times later than the first node death in
direct transmission, minimum-transmission-energy
routing, and a static clustering protocol, and the last
node death in LEACH occurs over 3 times later than
the last node death in the other protocols.
Wendi B. Heinzelman, in paper “An Application-
Specific Protocol Architecture for Wireless
Microsensor Networks,” proposed an architecture
protocol with microsensors. Networking together
hundreds or thousands of cheap microsensor nodes
allows users to accurately monitor a remote
environment by intelligently combining the data from
the individual nodes. These networks require robust
wireless communication protocols that are energy
efficient and provide low latency. Low-energy
adaptive clustering hierarchy (LEACH), a protocol
architecture for microsensor networks that combines
the ideas of energy-efficient cluster-based routing and
media access together with application-specific data
aggregation to achieve good performance in terms of
system lifetime, latency, and application-perceived
quality is developed and analysed. LEACH includes a
new, distributed cluster formation technique that
enables self-organization of large numbers of nodes,
algorithms for adapting clusters and rotating cluster
head positions to evenly distribute the energy load
among all the nodes, and techniques to enable
distributed signal processing to save communication
resources. Research show that LEACH can improve
system lifetime by an order of magnitude compared
with general-purpose multihop approaches. While
designing a protocol, it is important to consider the
function of the application, the need for ease of
deployment, and the severe energy constraints of the
nodes. These features led designing of LEACH, a
protocol architecture where computation is performed
locally to reduce the amount of transmitted data,
network configuration and operation is done using
local control, and media access control (MAC) and
routing protocols enable low-energy networking.
Arati Manjeshwar and Dharma P. Agrawal, in
paper “APTEEN: A Hybrid Protocol for Efficient
Routing and Comprehensive Information Retrieval in
Wireless Sensor Networks,” proposed a hybrid
protocol for enhancing the efficiency of network.
Wireless sensor networks with thousands of tiny
sensor nodes, are expected to find wide applicability
and increasing deployment in coming years, as they
enable reliable monitoring and analysis of the
environment. A hybrid routing protocol (APTEEN) is
proposed which allows for comprehensive
information retrieval. The nodes in such a network
not only react to time-critical situations, but also give
an overall picture of the network at periodic intervals
in a very energy efficient manner. Such a network
enables the user to request past, present and future
data from the network in the form of historical, one-
time and persistent queries respectively. The
performance of these protocols is evaluated and these
protocols are observed to out perform existing
protocols in terms of energy consumption and
longevity of the network. Hybrid protocol APTEEN
combines the best features of both proactive and
reactive networks and provide periodic data collection
as well as near real-time warnings about critical
events. Though, our query model is suitable for a
network with evenly distributed nodes, it can be
extended further to sensor networks with uneven node
distributions. We believe we have taken first step in
defining an appropriate protocol for upcoming field
of wireless sensor networks.
Mao YE, in paper “An Energy Efficient Clustering
Scheme in Wireless Sensor Networks,” proposed a

new clustering scheme. Data gathering is a common
but critical operation in many applications of wireless
sensor networks. Innovative techniques that improve
energy efficiency to prolong the network lifetime are
highly required. Clustering is an effective topology
control approach in wireless sensor networks, which
can increase network scalability and lifetime. Single-
hop wireless sensor networks, which better suits the
periodical data gathering applications. Though
approach elects cluster heads with more residual
energy in an autonomous manner through local radio
communication with no iteration while achieving
good cluster head distribution; further more, it
introduces a novel distance-based method to balance
the load among the cluster heads. Simulation results
show that EECS prolongs the network lifetime
significantly against the other clustering protocols
such as LEACH and HEED.
Vivek P. Mhatre, in paper “A Minimum Cost
Heterogeneous Sensor Network with a Lifetime
Constraint,” proposed a method for reducing cost in
heterogeneous networks. A heterogeneous sensor
network is considered in which nodes are to be
deployed over a unit area for the purpose of
surveillance. If an aircraft visits the area periodically
and gathers data about the activity in the area from
the sensor nodes. Nodes act as the cluster heads
besides doing the sensing. Nodes use multihopping to
communicate with their closest cluster heads. The
optimum node intensities and node energies guarantee
a lifetime of at least T units, while ensuring
connectivity and coverage of the surveillance area
with a high probability. The overall cost of the
network is minimized under these constraints.
Lifetime is defined as the number of successful data
gathering trips (or cycles) that are possible until
connectivity and/or coverage are lost. Conditions for
a sharp cutoff are also taken into account, i.e., we
ensure that almost all the nodes run out of energy at
about the same time so that there is very little energy
waste due to residual energy. Vivek Mhatre considers
two types of hierarchical sensor networks: one that
uses random uniform deployment and the other that
uses grid deployment. This approach involves using
two types of nodes: type 0 nodes which do the
sensing and relaying on the ground. He also ensures
conditions for connectivity and coverage of the area
during the lifetime of the network. The fact is that the
cluster heads as well as the nodes within one hop of
the cluster heads, i.e., the critical nodes have the
maximum relaying burden and, therefore, these nodes
are likely to run out of battery before other nodes.
The overall cost of the network is minimized
satisfying these constraints.
3. COMPARISON WITH TRADITIONAL
NETWORK
Routing is one of the most important and challenging
task of a network and so is for wireless sensor
network. Routing in wireless sensor network is very
challenging and different than from other traditional
networks due to the following reasons listed below
[15-20]:
 Global Addressing Scheme: Since the number of
nodes in sensor network is much higher than other
ad-hoc networks, it is not possible to build global
addresses to sensor nodes. The classical IP-
addressing mechanism cannot be applied to sensor
network; hence the routing protocols that work
based on IP-addressing cannot be used with sensor
network.
 Multi-Point Communication: Most of the
application in sensor network requires the sensed
data from multiple sensor nodes to a sink, which
is in contradiction to our traditional networks
which require point to point communication.
 Data Redundancy: A sensing region consists of
number of nodes and many a time’s multiple
sensors generate similar data which has significant
amount of redundancy in it. This redundant data
can cause power exploitation, which is a valuable
resource of sensor network. Hence, this data
redundancy is needed to be reduced for improving
the efficiency of the network.
 Constrained Resources: Sensor networks are
very much constrained in terms of energy
resources, computation capacity and memory
capacity hence requires careful resource
management.
4. APPLICATIONS OF WSN
Wireless Sensor Networks are formed by tiny sensing
devices for wireless communication, actuation,
control and monitoring. Given the potential benefits
offered by these networks like simple deployment,
low cost, lack of cabling and mobility they providing
numerous applications among which some are
categorized below:
Disaster Relief Operations: The WSN framework
structural planning for flood forecasting comprises of
sensors (which sense and gather the information
applicable for counts), a few nodes alluded to as
computational nods and a manned focal checking
office (which checks the results with the accessible
online data, executes an incorporated rendition of the
forecast calculation as an excess system, issues
cautions and starts departure strategies). Diverse sorts
of sensors are obliged to sense water release from
dam, precipitation, stickiness, temperature, and so on.

The information gathered by these sensors are utilized
within the flood prediction calculation. The
computational nodes have compelling CPUs needed
to execute the appropriated expectation model. The
computational nodes should impart the forecast
results to the observing node. They additionally have
correspondence between themselves for discovering
breaking down of nodes [21].
Intelligent Buildings/Bridges: To reduce the energy
use of buildings, WSNs could be deployed to measure
temperature, humidity and air flow, which then could
be used to adapt the temperature within the building
automatically. Also sensors could be used to monitor
the mechanical stress level of buildings, such as
bridges, to find out the likelihood of a collapse [22].
Biodiversity Mapping: WSNs can be used, for
example, to monitor the erosion processes on the
ground of the ocean. Closely related is biodiversity
mapping in which a number of plants or animals in a
certain region are monitored [83].
5. CONCLUSION
In the conclusion of this study, several critical points
were considered. Clustering emerged as a pivotal
technique for mitigating energy dissipation in the
network and augmenting its stability. Given that
nodes may be located far from the Base Station (BS),
direct communication becomes impractical due to
limited battery capacity, necessitating energy-
efficient alternatives. Numerous clustering protocols,
such as LEACH, have been designed to address this
issue. LEACH, as a fundamental algorithm, employs
a procedure of clusterhead election, where each
sensor node generates a random number in each
round. If this generated random number falls below a
predefined threshold, the respective node is elected as
the clusterhead for the current round. This approach
offers a dynamic and distributed means of forming
clusters, thereby optimizing energy consumption and
enhancing network performance.
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