QoS in WSN thesis

QUALITY OF SERVICE IMPROVEMENT IN WIRELESS
SENSOR NETWORKS
A PROJECT REPORT
Submitted by
LAKSHMI.C
SUJARITHA.S
EMAYAWATHY.R
DEEPIKA.D
in partial fulfillment for the award of the degree
of
BACHELOR OF ENGINEERING
In
ELECTRONICS AND COMMUNICATION ENGINEERING
COLLEGE OF ENGINEERING, GUINDY
ANNA UNIVERSITY :: CHENNAI 600 025
APRIL 2015

ANNA UNIVERSITY : CHENNAI 600 025
BONAFIDE CERTIFICATE
Certified that this project report “QUALITY OF SERVICE IMPROVEMENT
IN WIRELESS SENSOR NETWORKS” is the bonafide work of
“LAKSHMI.C, SUJARITHA.S , EMAYAWATHY.R and DEEPIKA.D”
who carried out the project work under my supervision.
SIGNATURE SIGNATURE
DR. S. MUTTAN DR. M.A. BHAGYAVENI
HEAD OF THE DEPARTMENT SUPERVISOR
PROFESSOR
DEPARTMENT OF ELECTRONICS AND DEPARTMENT OF ELECTRONICS AND
COMMUNICATION COMMUNICATION
COLLEGE OF ENGINEERING, GUINDY COLLEGE OF ENGINEERING, GUINDY
ANNA UNIVERSITY ANNA UNIVERSITY
CHENNAI – 25 CHENNAI-25

ABSTRACT
In our project, cluster based routing in wireless sensor networks is studied
precisely. Further, we modify one of the most prominent wireless sensor network’s
routing protocols “LEACH” as modified LEACH (MODLEACH) by introducing
efficient cluster head replacement scheme and dual transmitting power levels. Our
modified LEACH, in comparison with LEACH out performs it using metrics of
cluster head formation, throughput and network life. Afterwards, hard and soft
thresholds are implemented on modified LEACH (MODLEACH) that boosts the
performance even more. Finally a brief performance analysis of LEACH, Modified
LEACH (MODLEACH), MODLEACH with hard threshold (MODLEACHHT)
and MODLEACH with soft threshold (MODLEACHST) is undertaken considering
metrics of throughput, network life and cluster head replacements.

TABLE OF CONTENTS
`
CHAPTER NO. TITLE PAGE NO.
ABSTRACT
LIST OF TABLES
LIST OF FIGURES
LIST OF SYMBOLS AND ABBREVIATIONS
1 INTRODUCTION
1.1 Wireless Sensor Network
1.2 WSN Applications
1.3 WSN Architecture
1.4 WSN Challenges
1.5 QoS in WSN
2 LITERATURE REVIEW
2.1 Literature survey
2.2 Motivation
2.3 Proposed Idea
2.4 Organization of Thesis
3 HIERARCHICAL & ROUTING PROTOCOLS
3.1 Introduction to Routing Protocols

3.2 Analysis of existing protocols
3.3 Low Energy Adaptive Clustering Hierarchy
4 LEACH PROTOCOLOPTIMIZATION
4.1 Modified LEACH Protocol
4.2 Incorporation of Thresholding
5 SOFTWARE IMPLEMENTATION
5.1 Analysis in Qualnet
5.2 Implementation in Matlab
5.3 CodeSpecifications
6 RESULTS AND DISCUSSIONS
6.1 Network Life Time Analysis
6.2 Throughput Analysis
6.3 Cluster Head Formation
6.4 Comparative Analysis
7 CONCLUSION AND FUTURE WORK
7.1 Conclusion
7.2 Future Work
REFERENCES

LIST OF TABLES
CHAPTER 5
5.1 TABLE 1 Parameters in WSN

LIST OF FIGURES
CHAPTER 1
1.3 FIGURE 1 WSN Architecture
1.3 FIGURE 2 Sensing Node Architecture
CHAPTER 5
5.1 FIGURE 3 Throughput for various routing protocols
5.1 FIGURE 4 Average E-2-E delay for various routing protocols
5.1 FIGURE 5 No. of nodes Vs. Throughput for Hierarchical and Flat Protocol
5.1 FIGURE 6 No. of nodes Vs. PDR for Hierarchical and Flat Protocol
5.1 FIGURE 7 No. of nodes Vs. E-2-E delay for Hierarchical and Flat Protocol
CHAPTER 6
6.1 FIGURE 8 LEACH Rounds Vs. Alive nodes
6.1 FIGURE 9 MODLEACH Rounds Vs. Alive nodes
6.1 FIGURE 10 MODLEACH HT Rounds Vs. Alive nodes
6.1 FIGURE 11 MODLEACH ST Rounds Vs. Alive nodes
6.2 FIGURE 12 Throughput analysis of LEACH
6.2 FIGURE 13 Throughput analysis of MODLEACH
6.2 FIGURE 14 Throughput analysis of MODLEACH HT
6.2 FIGURE 15 Throughput analysis of MODLEACH ST
6.3 FIGURE 16 LEACH Rounds Vs. No. of Cluster Heads
6.3 FIGURE 17 MODLEACH Rounds Vs. No. of Cluster Heads
6.3 FIGURE 18 MODLEACH HT Rounds Vs. No. of Cluster Heads
6.3 FIGURE 19 MODLEACH ST Rounds Vs. No. of Cluster Heads

LIST OF SYMBOLS AND ABBREVIATIONS
1. WSN - Wireless Sensor Networks
2. QoS – Quality of Service
3. AODV – Ad-hoc On demand Distance Vector routing
4. DSR – Dynamic Source Routing
5. MANET- Mobile Ad-hoc Network
6. DYMO – Dynamic MANET On demand routing
7. DD- Directed Diffusion
8. CBR – Constant Bit Rate
9. PDR – Packet Delivery Ratio
10.E-2-E – End To End
11.AOMDV - Ad-hoc On demand Multipath Distance Vector routing
12.DSDV – Destination Sequenced Distance Vector Routing
13.M-DART – Multipath Dynamic Address Routing
14.OLSR- Optimized Link State Routing
15.WRP-Wireless Routing Protocol
16.LEACH – Low Energy Adaptive Clustering Hierarchy
17.MODLEACH – Modified Low Energy Adaptive Clustering Hierarchy
18.MODLEACH HT – MODLEACH Hard Thresholding
19.MODLEACH ST – MODLEACH Soft Thresholding
20.CH-Cluster Head
21.BS-Base Station
22.TEEN – Threshold sensitive Energy Efficient sensor Network protocol

CHAPTER 1: INTRODUCTION
1.1 WIRELESS SENSOR NETWORKS
A Wireless Sensor Network (WSN) sometimes called a Wireless Sensor and
Actor Network (WSAN) are spatially
distributed autonomous sensors to monitor physical or environmental conditions,
such as temperature, sound, pressure, etc. and to cooperatively pass their data
through the network to a main location. The more modern networks are bi-
directional, also enabling control of sensor activity. The development of wireless
sensor networks was motivated by military applications such as battlefield
surveillance; today such networks are used in many industrial and consumer
applications, such as industrial process monitoring and control, machine health
monitoring, and so on.
The WSN is built of “nodes” – from a few to several hundreds or even thousands,
where each node is connected to one (or sometimes several) sensors. Each such
sensor network node has typically several parts: a radio transceiver with an
internal antenna or connection to an external antenna, a microcontroller, an
electronic circuit for interfacing with the sensors and an energy source, usually
a battery or an embedded form of energy harvesting. A sensor node might vary in
size from that of a shoebox down to the size of a grain of dust, although
functioning “motes” of genuine microscopic dimensions have yet to be created.
The cost of sensor nodes is similarly variable, ranging from a few to hundreds of
dollars, depending on the complexity of the individual sensor nodes. Size and cost
constraints on sensor nodes result in corresponding constraints on resources such

as energy, memory, computational speed and communications bandwidth. The
topology of the WSNs can vary from a simple star network to an advanced multi-
hop wireless mesh network. The propagation technique between the hops of the
network can be routing or flooding. In computer science and telecommunications,
wireless sensor networks are an active research area.
The main characteristics of a WSN include:
 Power consumption constraints for nodes using batteries or energy
harvesting
 Ability to copewith node failures (resilience)
 Mobility of nodes
 Heterogeneity of nodes
 Scalability to large scale of deployment
 Ability to withstand harsh environmental conditions
 Ease of use
1.2 WSN APPLICATIONS
Wireless Sensor Networks (WSN) is an ad- hoc network , consisting of several
mobile devices with wireless transceiver , which has routing and transmission
functions. It has a wide range of applications, few of which are discussed below.
Area monitoring:

In area monitoring, the WSN is deployed over a region where some phenomenon
is to be monitored. Forexample in military, the use of sensors is to detect enemy
intrusion.
Health care monitoring:
The medical applications include body-area networks that can collect information
about an individual's health, fitness, and energy expenditure.
Air pollution monitoring:
Wireless sensornetworks have been deployed in several cities to monitor the
concentration of dangerous gases for citizens. These can take advantage of the ad
hoc wireless links rather than wired installations, which also make them more
mobile for testing readings in different areas.
Forestfire detection:
A network of Sensor Nodes can be installed in a forest to detect when a fire has
started. The nodes can be equipped with sensors to measure temperature, humidity
and gases which are produced byfire in the trees or vegetation.
Natural disaster prevention:
Wireless sensornetworks can effectively act to prevent the consequences of natural
disasters, like floods. Wireless nodes have successfully been deployed in rivers
where changes of the water levels have to be monitored in real time.
Data logging:
Wireless sensornetworks are also used for the collection of data for monitoring of
environmental information, this can be as simple as the monitoring of the

temperature in a fridge to the level of water in overflow tanks in nuclear power
plants.
Water/Waste water monitoring:
Monitoring the quality and level of water includes many activities such as
checking the quality of underground or surface water and ensuring a country’s
water infrastructure for the benefit of both human and animal. It may be used to
protect the wastage of water.
1.3 WSN ARCHITECTURE
The basic wireless sensor network architecture can be represented by the following
figure. The topology of WSNs can vary from a simple star network to an advanced
multi-hop mesh network.
Figure 1 WSN Architecture

The sensing node will have the following architecture.
Figure 2 Sensing node architecture
1.4 WSN CHALLENGES
The major features of WSNs that challenge QoS provisioning is discussed below.
1. ResourceConstraints
In WSNs, sensor nodes are usually low-cost, low-power, small devices that are
equipped with only limited data processing capability, transmission rate, battery
energy, and memory. Due to the limitation on transmission power, the available

bandwidth and the radio range of the wireless channel are often limited. In
particular, energy conservation is critically important for extending the lifetime of
the network, because it is often infeasible or undesirable to recharge or replace the
batteries attached to sensornodes once they are deployed. Resourceconstraints
apply to sensors. In the presence of resource constraints, the network QoS may
suffer from the unavailability of computing and/or communication resources. As a
consequence, some data transmissions will possibly experience large delays,
resulting in low level of QoS. Due to the limited memory size, data packets may be
dropped beforethe nodes successfully send them to the destination. Therefore, it is
of critical importance to use the available resources in WSNs in a very efficient
way.
2. Platform Heterogeneity
In a large-scale system of systems, the hardware and networking technologies used
in the underlying WSNs may differ from one subsystem to another. This platform
heterogeneity makes it very difficult to make full use of the resources available in
the integrated system. Consequently, resource efficiency cannot be maximized in
many situations. In addition, the platform heterogeneity also makes it challenging
to achieve real-time and reliable communication between different nodes.
3. Dynamic Network Topology
Node mobility is an intrinsic nature of many applications such as, among others,
intelligent transportation, assisted living, urban warfare, planetary exploration, and
animal control. During runtime, new sensornodes may be added; the state of a
node is possibly changed to or from sleeping mode by the employed power
management mechanism; some nodes may even die due to exhausted battery
energy. All of these factors may potentially cause the network topologies of WSNs

to change dynamically. Dealing with the inherent dynamics of WSNs requires QoS
mechanisms to work in dynamic and even unpredictable environments. In this
context, QoS adaptation becomes necessary; that is, WSNs must be adaptive and
flexible at runtime with respect to changes in available resources. For example,
when an intermediate node dies, the network should still be able to guarantee real-
time and reliable communication by exploiting appropriate protocols and
algorithms.
4. Mixed Traffic
Diverse applications may need to share the same WSN, inducing both periodic and
aperiodic data. This feature will becomeincreasingly evident as the scale of WSNs
grows. Some sensors may be used to create the measurements of certain physical
variables in a periodic manner for the purposeof monitoring and control.
Meanwhile, some others may be deployed to detect critical events. Furthermore,
disparate sensors for different kinds of physical variables, e.g., temperature,
humidity, location, and speed, generate traffic flows with different characteristics.
This feature of WSNs necessitates the supportof service differentiation in QoS
management.
1.5 QoS IN WSN
Quality of Service (QoS)routing is one of the key factors for WSN applications to
get controllable differentiated service and achieve fully efficient path to transfer

information which can balance and extend power utilization. QoS routing has two
aspects:
1. Energy cost:the network lifetime.
2. Service availability: to ensure QoS requirements such as minimum delay,
throughput and bandwidth.
It is envisioned that WSNs will becomepervasive in our daily lives, for example,
in our homes, offices, and cars. They promise to revolutionize the way we
understand and manage the physical world, just as Internet transformed how we
interact with one another. Ultimately, they will be connected to the Internet in
order to achieve global information sharing. This technical trend is driving WSNs
to provide QoS supportbecausethey have to satisfy the service requirements of
various applications. From an end user’s perspective, real-world WSN applications
have their specific requirements on the QoS of the underlying network
infrastructure. For instance, in a fire handling system, sensors need to report the
occurrence of a fire to actuators in a timely and reliable fashion then, the actuators
equipped with water sprinklers will react by a certain deadline so that the situation
will not become uncontrollable.
Conceptually, QoS can be regarded as the capability to provide assurance that the
service requirements of applications can be satisfied. Depending on the type of
target application, QoS in WSNs can be characterized by reliability, timeliness,
robustness, availability, and security, among others. Some QoS parameters may be
used to measure the degree of satisfaction of these services, such as throughput,
network lifetime, delay, jitter, and packet delivery ratio.

Throughput is the effective number of data flow transported within a certain period
of time, also specified as bandwidth in some situations. In general, the bigger the
throughput of the network, the better is the performance of the system.
Network lifetime can be defined as the time until the first node dies. The easiest to
capture indicator of this metric is the maximum per-node load, where a node’s load
correspondsto the number of packets sent from or routed through the given node.
Clearly, the network setup that minimizes the maximum node load is the one that
will ensure the maximum network lifetime.
Packet Delivery Ratio (PDR) can be described as the ratio of the packets received
to the packets sent. The performance of the protocolhaving greater value of packet
delivery ratio is said to be better.
Thus, provision of QoS in WSNs is very challenging due to two main problems:
(1) the usually severe limitations of WSN nodes, such as the ones related to their
energy, computational and communication capabilities, in addition to the large-
scale nature of WSNs; (2) most QoS properties are interdependent, in a way that
improving one of them may degrade others. These negative facts force system
designers to try to achieve the best trade-offs between QoS metrics. In this project,
a mechanism that enables to improve a few QoS parameters of a WSN system at
the same time is proposed.

CHAPTER 2: LITERATURE REVIEW
2.1 LITERATURE SURVEY
Jayadip Sen presented a query-based routing protocolfor a WSN that provides
different levels of Quality of Service (QoS): energy efficiency, reliability, low
latency and fault tolerance-under different application scenarios. For ensuring path
reliability, the algorithm used multiple paths from sourcenodes to the sink nodes
and for guaranteeing data reliability, it sent multiple copies of the same message.
The latency was minimized by enabling the sensor nodes to transmit with more
power.
Goran Horvat, Drago Zagar and Davor Vinko presented a simulation to show that
deployment parameters (coverage area, number of nodes and TX power)
substantially affected the QoS of a network. Thus, by choosing optimal
deployment parameters it was possible to maximize QoS in a network.
Bhupinder Kaur and Sakshi Kaushal analysed the existing routing protocols
namely AODV, AOMDV, DSDV and M-DART by using different metrics like
packet delivery ratio, throughput, end to end delay and energy efficiency to ensure
which routing protocolprovided a better QoS guarantee. It was inferred that
AODV protocoloutperformed all the other three protocols in terms of energy
efficiency because in AODV only a few nodes are active during the transmission
due to the reactive and multi-path nature of the protocol.
Junguo Zhang and Wenbin LI had simulated the LEACH protocolusing NS2
network simulation and they identified the drawbacks of that protocolin clustering.

They deduced that if the number of cluster-heads were too small, the meaning of
layering would be lost and if the number was too large, the cluster heads would
directly communicate with the distal sink nodes. This would lead to higher
transmission power which could lead to higher energy consumption by the entire
network.
Jing Feng, Xiaoxing YU, Zijun LIU and Cuihan WANG aimed to improve the
power efficiency and QoS performance of WSN. To solve the high availability of
WSN, the idea of gradient and hierarchy in DD and LEACH protocols respectively
were combined. The combined protocolcould achieve significant power utilization
and time performance in sensornetworks.
2.2 MOTIVATION
Low Energy Adaptive Clustering Hierarchy (LEACH) is a TDMA
based MAC protocol which is integrated with clustering and a simple routing
protocol in wireless sensor networks. The procedure of this protocol is compact
and well coped with homogeneous sensor environment. According to this protocol,
for every round, new cluster head is elected and hence new cluster formation is
required. This leads to unnecessary routing overhead resulting in excessive use of
limited energy. If a cluster head has not utilized much of its energy during previous
round, than there is probability that some low energy node may replace it as a
cluster head in next cluster head election process. There is a need to limit the
change of cluster heads at every round considering the residual energy of existing
cluster head. Hence an efficient cluster head replacement algorithm is required to
conserve energy.

In clustering protocols as LEACH, nodes use same amplification energy to
transmit data regardless of distance between transmitter and receiver. To preserve
energy, there should also be a transmission mechanism that specifies required
amplification energy for communicating with cluster head or base station. For
example, transmitting a packet to a cluster head with same amplification power
level as required by a node located at the farthest end of the network to the base
station results in wastage of energy .One solution can be having global knowledge
of network and then nodes decide how much they need to amplify signal. Locating
and calculating distances with in full network topology needs lot of routing and so,
this approach does not work for saving energy. To solve the above mentioned
problems, we propose two mechanisms i.e. efficient cluster head replacement and
dual transmitting power levels.
2.3 PROPOSED IDEA
In the proposedprotocol, LEACH is improved as modified LEACH
(MODLEACH) by introducing efficient cluster head replacement scheme and dual
transmitting power levels. Then, hard and soft thresholds are implemented on
MODLEACH that boosts the performance even more. Finally a brief performance
analysis of LEACH, MODLEACH, MODLEACH with hard threshold
(MODLEACHHT) and MODLEACH with soft threshold (MODLEACHST) is
undertaken considering metrics of network lifetime, cluster head replacements and
throughput. The project is implemented using Matlab software.

In MODLEACH, it is a threshold in cluster head formation for every next round. If
existing cluster has not spent much energy during its tenure and has more energy
than required threshold, it will remain cluster head for the next round as well. This
is how, energy wasted in routing packets for new cluster head and cluster
formation can be saved.
Minimum amplification energy required for inter cluster or cluster head to BS
communication and amplification energy required for intra cluster communication
cannot be same. Using low energy level for intra cluster transmissions with respect
to cluster head to BS transmission leads in saving much amount of energy.
Moreover, multi power levels also reduce the packet drop ratio, collisions and
interference for other signals.
2.4 ORGANIZATION OF THESIS
The thesis is divided into seven chapters. The first chapter presents the introduction
to Wireless Sensor Networks (WSN) and QoS in WSNs. Chapter 2 is the literature
survey that was done prior to the start of the project. Chapter 3 gives an insight into
the hierarchical and routing protocols. Chapter4 is the detailed description of our
proposedMODLEACH protocol. Chapter 5 discusses the challenges, approaches,
specifications and the platform used for implementing the project. Chapter 6
contains the comparative performance analysis of the modified protocols and the
result of our project. Chapter 7 provides the conclusion and suggests ideas for
related future work. Following these chapters are the references.

CHAPTER 3: HIERARCHICAL AND ROUTING PROTOCOLS
3.1 INTRODUCTION TO ROUTING PROTOCOLS
Routing protocols define a set of rules which governs the journey of message
packets from sourceto destination in a network. In MANET, there are different
types of routing protocols each of them is applied according to the network
circumstances.
Proactive routing protocols are also called as table driven routing protocols. In this
every node maintain routing table which contains information about the network
topology even without requiring it. This feature although useful for datagram
traffic, incurs substantial signaling traffic and power consumption. The routing
tables are updated periodically whenever the network topology changes. Proactive
protocols are not suitable for large networks as they need to maintain node entries
for each and every node in the routing table of every node. These protocols
maintain different number of routing tables varying from protocolto protocol.
There are various well known proactive routing protocols. Example: DSDV,
OLSR, WRP etc.
Reactive routing protocolis also known as on demand routing protocol. In this
protocolroute is discovered whenever it is needed Nodes initiate route discovery
on demand basis. Sourcenode sees its route cache for the available route from
sourceto destination if the route is not available then it initiates route discovery
process. Theon- demand routing protocols have two major components:

Route discovery: In this phase source node initiates route discovery on demand
basis. Sourcenodes consults its route cache for the available route from sourceto
destination otherwise if the route is not present it initiates route discovery. The
sourcenode, in the packet, includes the destination address of the node as well
address of the intermediate nodes to the destination.
Route maintenance: Due to dynamic topology of the network cases of the route
failure between the nodes arises due to link breakage etc, so route maintenance is
done. Reactive protocols have acknowledgement mechanism due to which route
maintenance is possible.
3.2 ANALYSIS OF EXISTING ROUTING PROTOCOLS
Ad Hoc On-Demand Distance Vector Routing (AODV) is basically an
improvement of DSDV. But, AODV is a reactive routing protocolinstead of
proactive. It minimizes the number of broadcasts bycreating routes based on
demand, which is not the case for DSDV. When any source node wants to send a
packet to a destination, it broadcasts aroute request (RREQ) packet. The
neighboring nodes in turn broadcastthe packet to their neighbors and the process
continues until the packet reaches the destination. During the process of
forwarding the route request, intermediate nodes record the address ofthe neighbor
from which the first copyof the broadcastpacket is received. This record is stored
in their route tables, which helps for establishing a reverse path. If additional
copies of the same RREQ are later received, these packets are discarded. The reply
is sent using the reverse path. Forroute maintenance, when a sourcenode moves, it
can reinitiate a route discovery process. Ifany intermediate node moves within a

particular route, the neighbor of the drifted node can detect the link failure and
sends a link failure notification to its upstream neighbor. This process continues
until the failure notification reaches the sourcenode. Based on the received
information, the sourcemight decide to re-initiate the route discovery phase.
3.3 LOW ENERGY ADAPTIVE CLUSTERING HIERARCHY
One of the first and most popular clustering protocols proposed forWSNs was
LEACH (Low Energy Adaptive Clustering Hierarchy) . It is probably the first
dynamic clustering protocolwhich addressed specifically the WSNs needs, using
homogeneous stationary sensornodes randomly deployed, and it still serves as the
basis for other improved clustering protocols for WSNs. It’s an hierarchical,
probabilistic, distributed, one-hop protocol, with main objectives (a) to improve the
lifetime of WSNs by trying to evenly distribute the energy consumption among all
the nodes of the network and (b) to reduce the energy consumption in the network
nodes (by performing data aggregation and thus reducing the number of
communication messages). It forms clusters based on the received signal strength
and also uses the CH nodes as routers to the BS. All the data processing suchas
data fusion and aggregation are local to the cluster. LEACH forms clusters by
using a distributed algorithm, where nodes make autonomous decisions without
any centralized control. All nodes have a chance to become CHs to balance the
energy spent per round by each sensornode. Initially a node decides to be a CH
with a probability “p” and broadcastsits decision. Specifically, after its election,
each CH broadcasts an advertisement message to the other nodes and each one of
the other (non-CH) nodes determines a cluster to belong to, by choosing the CH

that can be reached using the least communication energy (based on the signal
strength of each CH message).
The role of being a CH is rotated periodically among the nodes of the cluster to
balance the load. The rotation is performed by getting each node to choosea
random number “T” between 0 and 1. The clusters are formed dynamically in each
round and the time to perform the rounds are also selected randomly. Generally,
LEACH can provide a quite uniform load distribution in one-hop sensornetworks.
Moreover, it provides a good balancing of energy consumption by random rotation
of CHs. Furthermore, the localized coordination scheme used in LEACH provides
better scalability for cluster formation, whereas the better load balancing enhances
the network lifetime. However, despite the generally good performance, LEACH
has also some clear drawbacks. Because the decision on CH election and rotation
is probabilistic, there is still a good chance that a node with very low energy gets
selected as a CH. Due to the same reason, it is possible that the elected CHs will be
concentrated in one part of the network (good CHs distribution cannot be
guaranteed) and some nodes will not have any CH in their range. Also, the CHs are
assumed to have a long communication range so that the data can reach the BS
directly. This is not always a realistic assumption because the CHs are usually
regular sensors and the BS is often not directly reachable to all nodes. Moreover,
LEACH forms in general one-hop intracluster and intercluster topology where
each node should transmit directly to the CHs and thereafter to the BS, thus
normally it cannot be used effectively on networks deployed in large regions.

CHAPTER 4: LEACH PROTOCOL OPTIMIZATION
4.1 MODIFIED LEACH PROTOCOL
Modified Leach is nothing but an improved version of LEACH (Low Energy
Adaptive Clustering Hierarchy) protocol. There are two main disadvantages of
LEACH. They are
1. All nodes are assigned the same amplification energy. Amplification energy
is the amount of energy required for a transmission to occur between the
sender and the receiver. Thus the cluster heads are also given the same
energy. As the cluster heads have to perform data aggregation and transmit
all the information sent from the various nodes to the base station it requires
higher energy when compared to the node. But since same energy is given
for all nodes the cluster heads tend to lose their energy faster. Thus the life
of the network decreases.
2. Secondly in the case of Leach the cluster head is changed for every round.
But a node that is a cluster head cannot become the cluster head for the next
1/p rounds where p is the probability. Thus there are chances for a node with
lesser energy compared to the present cluster head to become the new cluster
head. This reduces the efficiency of the network.
So in order to overcome these disadvantages of LEACH we have
incorporated modified LEACH. Basically there can be three modes of
transmission in modified leach.
1) Intra Cluster Transmission

2) Inter Cluster Transmission
3) Cluster Head to Base Station Transmission
Intra Cluster Transmission deals with all the communications within
a cluster i.e. cluster members sense data and report sensed data to cluster head.
The transmission/ reception between two cluster heads can be termed as inter
cluster transmission while a cluster head transmitting its data straight to base
station lies under the caption of cluster head to base station transmission. The
two main features in mod leach are
Dual transmitting power levels:
Here two different levels of energy are assigned to the nodes. All the
nodes are assigned a particular energy level and all the cluster heads are
assigned a higher energy. This is because intra cluster communication requires
a lower energy for communication. Whereas for the communication between
the base station and the cluster head, the cluster head requires a higher energy.
Thus the routing overhead decreases and the number of packets transmitted
also increases. This in turn, increases the packet delivery ratio and also the
throughput which in turn increases the performance of the network.
Efficient Cluster head selection:
The cluster head selection is made efficient by first comparing the
energy of the existing cluster head with a particular threshold in every round. If the
energy is greater than the threshold then the same node exists as the cluster head
for the next rounds. Suppose if the cluster head has a lower energy compared to the
threshold then a new cluster head is appointed. So in this way of cluster head
replacement the life time of the network increases. In our project we have

compared the energy with both soft and hard threshold. The concept of hard and
soft threshold is obtained from the concept of TEEN.
4.2 INCORPORATION OF THRESHOLDING
Reactive Network Protocol:TEEN
In our project the conceptof thresholding is obtained from the TEEN (Threshold
sensitive Energy Efficient sensorNetwork protocol)which is a new reactive
network protocol. It is targeted at reactive networks and is the first protocol
developed for reactive networks.
Functioning of TEEN
In this scheme, at every cluster change time, the cluster-head broadcasts to its
members at a particular time which is determined by two attributes hard threshold
and softthreshold.
Hard Threshold (HT ): This is a threshold value for the sensed attribute. It is the
absolute value of the attribute beyond which, the node sensing this value must
switch on its transmitter and report to its cluster head.
Soft Threshold (ST ): This is a small change in the value of the sensed attribute
which triggers the nodeto switch on its transmitter and transmit.
The nodes sense their environment continuously. The first time a parameter from
the attribute set reaches its hard threshold value, the node switches on its
transmitter and sends the sensed data. The sensed value is stored in an internal

variable in the node, called the sensed value (SV). The nodes will next transmit
data in the current cluster period, only when both the following conditions are true:
1. The current value of the sensed attribute is greater than the hard threshold.
2. The current value of the sensed attribute differs from SV by an amount equal to
or greater than the soft threshold.
Whenever a node transmits data, SV is set equal to the current value of the sensed
attribute. Thus, the hard threshold tries to reduce the number of transmissions by
allowing the nodes to transmit only when the sensed attribute is in the range of
interest. The soft threshold further reduces the number of transmissions by
eliminating all the transmissions which might have otherwise occurred when
there is little or no change in the sensed attribute once the hard threshold.
Important Features
The main features of this scheme are as follows:
1. Time critical data reaches the user almost instantaneously. So, this scheme is
eminently suited for time- critical data sensing applications.
2. Message transmission consumes much more energy than data sensing. So, even
though the nodes sense continuously, the energy consumption in this scheme can
potentially be much less than in the proactive network, because data transmission
is done less frequently.
3. The soft threshold can be varied, depending on the criticality of the sensed
attribute and the target application.

4. A smaller value of the soft threshold gives a more accurate picture of the
network, at the expense of in- creased energy consumption. Thus, the user can
control the trade-off between energy efﬁciency and accuracy.
5. At every cluster change time, the attributes are broad-cast afresh and so, the
user can change them as required.
This protocolis best suited for time critical applications such as intrusion detection
and explosion detection.

CHAPTER 5: SOFTWARE IMPLEMENTATION
5.1 ANALYSIS IN QUALNET
The implementation of the LEACH and Modified LEACH protocol in a
virtual scenario required the utilization of two separate simulation platforms
namely Qualnet and MATLAB. Though these two simulators are used for the
analysis and implementation purposes respectively the inferences obtained using
the former directly influences the results to be obtained using the latter.
In this project the Qualnet simulator is mainly used for analyzing the
considered routing protocols that can be used for the development of an improved
LEACH protocol. The few routing protocols that are being analyzed here include
Bellman Ford, DSR, AODV and DYMO since these protocols have been shown to
give promising results considering the large scale deployment area pertaining to a
WSN scenario.
The simulation scenario is set in a 1500mx1500m terrain that is 1500m
above the sea level. Though a real-time WSN scenario consists of no less than
1000 sensors deployed in a given area for simplification of analyses we opt for a
different numbers of sensors nodes ranging from 50 to 200 in steps of 50 deployed
randomly with a maximum of 10m distance in between adjacent nodes.
Traffic is introduced in the WSN by directing a CBR (Constant Bit Rate)
between two sensor nodes that are preferably far apart with as many nodes in
between them as possible. A WSN is characterized by the 802.15.4 Network layer
MAC protocol. Hence the necessary parameter specifications are set as follows.

PARAMETER VALUE
MAC Protocol 802.15.4
No. of Packets 100
No. of Bits per packet 512
Fragmentation units 78
End Time 20 seconds
Simulation Time 30 seconds
TABLE 1 PARAMETERS IN WSN
After setting the required parameters the Routing Protocol is varied from
DSR to AODV to DYMO and the simulation is ‘Run’ for different scenarios
containing varied number of sensor nodes (50, 100, …,etc.). In each case the
values of three main parameters named Throughput, End-to-End Delay and PDR
(Packet Delivery Ratio) are noted down.
FIGURE 3 THROUGHPUT FOR VARIOUS ROUTING PROTOCOLS

FIGURE 4 AVERAGE E-2-E DELAY FOR VARIOUS ROUTING PROTOCOLS
From the results it was found that AODV and DYMO comparatively have higher
throughput and lesser end-to-end delay compared to the other two routing
protocols. Hence these two routing protocols are finalized for incorporation into
the LEACH protocol.
After including the routing block into the hierarchical protocol results were
obtained for the parameters PDR, Throughput and End-to-End delay and the
results are compared with corresponding parameters obtained for conventional
hierarchical protocol. And graphical plots were generated.

FIGURE 5 ‘No of Nodes Vs Throughput’ for Hierarchical and Flat protocol
FIGURE 6 ‘No of Nodes Vs PDR’ for Hierarchical and Flat protocol

FIGURE 7 ‘No of Nodes Vs End-to-End Delay’ for Hierarchical and Flat protocol
The above results clearly suggests that hierarchical protocol with incorporated
AODV and incidentally DYMO has better performance than the Flat protocol.

5.2 MATLAB IMPLEMENTATION
The results obtained in Qualnet helped in deciding which routing protocol
would be more suited for the performance optimization of the LEACH protocol.
Initially both AODV and DYMO were combined with a basic hierarchical protocol
wherein the required hierarchical node-to-cluster head-to-base station routing was
configured using the Statics.in file available within the Qualnet software’s source
file. So the primitive design was necessarily a hierarchical routing achieved via
static routing. Proceeding to the next level in the protocol development involved
incorporating both AODV and DYMO protocols into the LEACH protocol
individually. Firstly this required developing a code for LEACH that could be
implemented in Qualnet and secondly to alter the ‘back-end’ coding of AODV and
DYMO available in the Qualnet source code file to combine them individually
with the LEACH protocol.
Though the code for LEACH was written in the back-end of Qualnet and the
combining of AODV & LEACH and DYMO & LEACH were completed, every
time the simulation was ‘run’ the software kept crashing. To sort out this mishap
the developers of the Qualnet software were contacted and asked for their views on
the project and their solution to the continued software crash. Their opinion was
that the implementation of the proposed combined protocol requires higher coding
capabilities and tampering with the original source could lead to eventual system
crash.
Hence to avoid the continued un-fruitful attempts at implementing the
proposed protocol in Qualnet the same code was modified to suite a different
software: Matlab. Here instead of incorporating AODV and DYMO individually
into the LEACH protocol only the main idea behind the functionality of both
AODV and DYMO (broadcasting a request to determine the path to the destination

and finding the minimum path between the two transmitting nodes and then
establishing the connection) is used for coding the routing section of the Modified
LEACH protocol.
5.3 CODE SPECIFICATIONS
In the Matlab coding scenario the node deployment terrain is taken to be of
400x400 dimensions. Within this terrain the base station or the sink node is
assigned to occupy a 200x200 space. Also to analyze the improvement in QoS a
very small deployment scenario is considered wherein a mere 100 nodes are
deployed randomly in this terrain. For setting up ‘thresholding’ within the LEACH
protocol two thresholds namely hard and soft thresholds are set as 2 and 100
respectively. The values for the thresholds are set after a long process of trial and
error and after observing the outputs for different values of thresholds, the values
of hard threshold and soft threshold are finalized as 2 and 100 respectively.
As discussed earlier the cluster heads in LEACH protocol are formed by
considering its threshold value that is calculated from the ‘random number’
generated by each node during the instant of path request broadcast prior to
transmission. Here however a new concept of setting the threshold based on the
energy of each node at any instant or ‘round’ is used for calculating the threshold
value that is used for the cluster head selection. In LEACH and MODLEACH the
network decides what value should be set as threshold and when this privilege is
taken from the network and the threshold is manually set it becomes
MODLEACH-HT or MODLEACH-ST.

Also a constraint that initially the number of cluster heads that need to be
formed per instant or ‘round’ should be not less than 10, is set so data crowing
does not occur at the cluster heads and all the nodes are evenly distributed among
the cluster heads. As far as energy of the node is considered both the amplification
energy, transmitter energy and receiver energy are individually set so that there’s a
clear demarcation between total energy utilized and the amount of energy lost
during the period of transmission or reception.

CHAPTER 6: RESULTS AND DISCUSSIONS
6.1 NETWORK LIFE TIME ANALYSIS
As discussed earlier LEACH protocol was modified to form Modified LEACH
(MODLEACH) using the idea of effective cluster head replacement and further the
concept of ‘thresholding’ was introduced into MODLEACH to form
MODLEACH-HT and MODLEACH-ST. Individual Matlab codes were created
for all the four protocols and the outputs were generated for all of them
individually. The results were analyzed and conclusions were drawn for three main
parameters of interest namely the Network Life Time, Throughput and Cluster
Head formation.
The Network Life Time parameter is analyzed by considering the number of
nodes that are remaining active or in this pretext remaining ‘alive’ at the end of
each event time instant or ‘round’ and also by monitoring for how long or more
precisely for how many ‘rounds’ the nodes, even a single node, remain alive at the
end of the simulation. Here we have considered a 2500 round scenario. The plots
of ‘Rounds Vs Number of Alive Nodes’ for all the four above mentioned protocols
are generated and are analyzed.

FIGURE 8 LEACH ROUNDS Vs ALIVE NODES
FIGURE 9 MODLEACH ROUNDS Vs. ALIVE NODES

FIGURE 10 MODLEACH HT ROUNDS Vs. ALIVE NODES
FIGURE 11 MODLEACH ST ROUNDS Vs. ALIVE NODES

6.2 THROUGHPUT ANALYSIS
In this simulation scenario the message packets are first transmitted from the
individual nodes to the Cluster Heads and then from the Cluster Heads to the Base
Station (Sink Node). The Packet Delivery Ratio is not considered in this scenario
however the parameters ‘Number of packets transmitted by a cluster head’ and
‘Number of packets received by the base station’ are used for approximating the
value of throughput. The number of packets being transmitted by a cluster head
necessarily means the number of packets received by it from its constituent nodes
provided there is no packet loss within the cluster head. The individual plots of
‘Rounds Vs No of Packets to Cluster Head’ and ‘Rounds Vs No of Packets to Base
Station’ for all the four protocols are generated.
FIGURE 12 THROUGHPUT ANALYSIS OF LEACH

FIGURE 13 THROUGHPUT ANALYSIS OF MODLEACH
FIGURE 14 THROUGHPUT ANALYSIS OF MODLEACH HT

FIGURE 15 THROUGHPUT ANALYSIS OF MODLEACH ST
6.3 CLUSTER HEAD FORMATION
Since the conventional LEACH is modified solely with the idea of cluster
head replacement scheme in mind one of the most important parameters to be
considered includes the number of cluster heads that are being formed during each
time instant or ‘round’. The plot of ‘Rounds Vs Number of Cluster Heads’ is
generated individually for all the four protocols.

FIGURE 16 ROUNDS Vs. NUMBER OF CLUSTER HEADS FOR LEACH
FIGURE 17 ROUNDS Vs. NUMBER OF CLUSTER HEADS FOR MODLEACH

FIGURE 18 ROUNDS Vs. NUMBER OF CLUSTER HEADS FOR
MODLEACH-HT
FIGURE 19 ROUNDS Vs. NUMBER OF CLUSTER HEADS FOR
MODLEACH-ST

6.4 COMPARITIVE ANALYSIS
Analyses of the above Network Life Time plots provide a visual proof that
Modified LEACH protocol with Soft Threshold (MODLEACH-ST) has alive
nodes that have survived longer instants or ‘rounds’ compared to the other three
protocols. Though for MODLEACH and MODLEACH-HT the number of alive
nodes surviving each round is higher when compared to conventional LEACH,
they do not survive many ‘rounds’. Whereas for MODLEACH-ST, the number of
alive nodes and the period of node survival are comparatively higher and hence it’s
an optimal protocol as far as network life time is concerned.
Comparing the Throughput plots it can be inferred that Modified LEACH with
Soft Threshold relatively shows a better throughput performance than the other
three protocols. Though there are fluctuating minor variations between LEACH,
MODLEACH and MODLEACH-HT, MODLEACH-LT shows progressively more
variation than the others and this variation further supports the high performance of
the MODLEACH-ST protocol.
The Cluster Head count Plot analyses clearly reveals that the number of cluster
heads formed per time instant is higher for MODLEACH-ST than any other
protocol. Also it’s duration of cluster head replacements lasts for more ‘rounds’ or
instants than all the other three protocols. The latter conclusion only corroborates
the point made earlier made the network life time of MODLEACH-ST being
higher than any of the other three protocols.

CHAPTER 7: CONCLUSION AND FUTURE WORK
7.1 CONCLUSION
In this work, we give a brief discussion on emergence of cluster based routing in
wireless sensor networks. We also propose MODLEACH, a new variant of
LEACH that can further be utilized in other clustering routing protocols for better
efficiency. MODLEACH tends to minimize network energy consumption by
efficient cluster head replacement after very first round and dual transmitting
power levels for intra cluster and cluster head to base station communication. In
MODLEACH, a cluster head will only be replaced when its energy falls below
certain threshold minimizing routing load of protocol. Hence, cluster head
replacement procedure involves residual energy of cluster head at the start of each
round. Further, soft and hard thresholds are implemented on MODLEACH to give
a comparison on performances of these protocols considering throughput and
energy utilization.
7.2 FUTURE WORK
In future, we will carry our work to calculate routing load of MODLEACH,
MODLEACHST and MODLEACHHT analytically and to apply efficient cluster
head replacement mechanism along with dual transmission power levels in other
clustering routing protocols ofwireless sensor networks to study their impact in a
broader sense.

REFERENCES
[1] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan “Energy-Efficient
Communication Protocols for Wireless Microsensor Networks”. In Proceedings of
Hawaiian International Conference on Systems Science, January 2011.
[2] C. Intanagonwiwat, R. Govindan, and D. Estrin. ”Directed Diffusion: A
Scalable and RobustCommunication Paradigm for SensorNetworks”. In
Proceedings of the 6th Annual ACM/IEEE International Conference on Mobile
Computing and Networking(MOBICOM), pp. 56-67, August 2011.
[3] D. Estrin, R. Govindan, J. Heidemann, and S. Kumar. “Next Century
Challenges: Scalable Coordination inWireless Networks”. In Proceedings of the
5th Annual ACM/IEEE International Conference on Mobile Computing and
Networking( MOBICOM), pp. 263-270, 2012.
[4] M. Jiang, J. Li, and Y. C. Tay. “Cluster Based Routing Protocol”. Internet
Draft, 2012.
[5] C. Y. Chong and S. P. Kumar, “SensorNetworks: Evolution, Opportunities and
Challenges”, Proceedings of the IEEE, 91, No. 8, pp. 1247-1256, Aug 2003.

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