QUALITY OF SERVICE IMPROVEMENT IN WIRELESS SENSOR NETWORKS

A PROJECT REPORT

Submitted by

LAKSHMI.CSUJARITHA.S

EMAYAWATHY.RDEEPIKA.D

in partial fulfillment for the award of the degreeof

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 ANDCOMMUNICATION COMMUNICATION COLLEGE OF ENGINEERING, GUINDY COLLEGE OF ENGINEERING, GUINDYANNA 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 PROTOCOL OPTIMIZATION

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 Code Specifications

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 Architecture1.3 FIGURE 2 Sensing Node Architecture

CHAPTER 5

5.1 FIGURE 3 Throughput for various routing protocols5.1 FIGURE 4 Average E-2-E delay for various routing protocols5.1 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 nodes6.1 FIGURE 9 MODLEACH Rounds Vs. Alive nodes6.1 FIGURE 10 MODLEACH HT Rounds Vs. Alive nodes6.1 FIGURE 11 MODLEACH ST Rounds Vs. Alive nodes6.2 FIGURE 12 Throughput analysis of LEACH6.2 FIGURE 13 Throughput analysis of MODLEACH6.2 FIGURE 14 Throughput analysis of MODLEACH HT6.2 FIGURE 15 Throughput analysis of MODLEACH ST6.3 FIGURE 16 LEACH Rounds Vs. No. of Cluster Heads6.3 FIGURE 17 MODLEACH Rounds Vs. No. of Cluster Heads6.3 FIGURE 18 MODLEACH HT Rounds Vs. No. of Cluster Heads6.3 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 cope with 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. For example 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 sensor networks 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.

Forest fire 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 by fire in the trees or vegetation.

Natural disaster prevention:

Wireless sensor networks 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 sensor networks 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. Resource Constraints

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 sensor nodes once they are deployed. Resource constraints

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 before the 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 sensor nodes 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 become increasingly 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 purpose of 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 support of 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 become pervasive 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 support because they 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

corresponds to 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 protocol having 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 protocol for 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 source nodes 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 protocol provided a better QoS guarantee. It was inferred that

AODV protocol outperformed 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 protocol using NS2

network simulation and they identified the drawbacks of that protocol in 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 protocol could achieve significant power utilization

and time performance in sensor networks.

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 proposed protocol, 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. Chapter 4 is the detailed description of our

proposed MODLEACH 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 source to 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 protocol to protocol.

There are various well known proactive routing protocols. Example: DSDV,

OLSR, WRP etc.

Reactive routing protocol is also known as on demand routing protocol. In this

protocol route is discovered whenever it is needed Nodes initiate route discovery

on demand basis. Source node sees its route cache for the available route from

source to destination if the route is not available then it initiates route discovery

process. The on- demand routing protocols have two major components:

Route discovery: In this phase source node initiates route discovery on demand

basis. Source nodes consults its route cache for the available route from source to

destination otherwise if the route is not present it initiates route discovery. The

source node, 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 protocol instead of

proactive. It minimizes the number of broadcasts by creating 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 a route request (RREQ) packet. The

neighboring nodes in turn broadcast the 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 of the neighbor

from which the first copy of the broadcast packet 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. For route maintenance, when a source node moves, it

can reinitiate a route discovery process. If any 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 source node. Based on the received

information, the source might 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 for WSNs was

LEACH (Low Energy Adaptive Clustering Hierarchy) . It is probably the first

dynamic clustering protocol which addressed specifically the WSNs needs, using

homogeneous stationary sensor nodes 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 such as

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 sensor node. Initially a node decides to be a CH

with a probability “p” and broadcasts its 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 choose a

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 sensor networks.

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 concept of thresholding is obtained from the TEEN (Threshold

sensitive Energy Efficient sensor Network 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 soft threshold.

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 node to 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 inter- nal

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 cur- rent 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 other- wise 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 protocol is 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 VALUEMAC Protocol 802.15.4No. of Packets 100

No. of Bits per packet 512Fragmentation units 78

End Time 20 secondsSimulation 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 of wireless sensor networks to study their impact in a

broader sense.

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