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CHAPTER 1

INTRODUCTION

Wireless sensor networks attracted lots of researchers because of its

potential wide applications and special challenges. Early study on wireless sensor

networks mainly focused on technologies based on the homogeneous wireless

sensor network in which all nodes have same system resource. However,

heterogeneous wireless sensor network is becoming more and more popular

recently. And the result show that heterogeneous nodes can prolong network

lifetime and improve network reliability without significantly increasing the cost.

A typical heterogeneous wireless sensor networks consists of a large number of

normal nodes and a few heterogeneous nodes. The normal node, whose main

tasks are to sense and issue data report, is inexpensive and source-constrained.

The heterogeneous node, which provides data filtering, fusion and

transport, is more expensive and more capable. It may possess one or more type

of heterogeneous resource, e.g., enhanced energy capacity or communication

capability. They may be line powered, or their batteries may be replaced easily.

Compared with the normal nodes, they may be configured with more powerful

microprocessor and more memory. They also may communicate with the sink

node via high-bandwidth, long-distance network, such as Ethernet. The presence

of heterogeneous nodes in a wireless sensor network can increase network

reliability and lifetime.

There are three common types of resource heterogeneity in sensor

node: computational heterogeneity, link heterogeneity, and energy heterogeneity.

Computational heterogeneity means that the heterogeneous node has a more

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powerful microprocessor and more memory than the normal node. With the

powerful computational resources, the heterogeneous nodes can provide complex

data processing and longer-term storage.

Link heterogeneity means that the heterogeneous node has high-

bandwidth and long-distance network transceiver, than the normal node. Link

heterogeneity can provide more reliable data transmission.

Energy heterogeneity means that the heterogeneous node is line

powered, or its battery is replaceable. Among above three types of resource

heterogeneity, the most important heterogeneity is the energy heterogeneity

because both computational heterogeneity and link heterogeneity will consume

more energy resource. If there is no energy heterogeneity, computational

heterogeneity and link heterogeneity will bring negative impact to the whole

sensor network, i.e., decreasing the network lifetime.

Figure 1.1 Node hardware architecture

1.1 THE IMPACT OF HETEROGENEOUS RESOURCES

Placing few heterogeneous nodes in the sensor network can bring

following three main benefits

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1.1.1 Prolonging Network Lifetime

In the heterogeneous wireless sensor network, all normal nodes can

send data report to the sink via the nearest heterogeneous node. And the normal

nodes, especially around the sink, don’t need forward vast packets from other

nodes. Under this data transmission scheme, the typical hops of data transmission

are 2 and significantly less than the average hops in homogeneous sensor

network. In other words, the average energy consumption for forwarding a

packet from the normal nodes to the sink in heterogeneous sensor networks will

be much less than the energy consumed in homogeneous sensor networks. With

the size of network increasing, the gap of energy consumption between these two

kinds of networks will be bigger and bigger.

1.1.2 Improving Reliability of Data Transmission

It is well know that sensor network links tend to have low reliability

and each hop significantly lowers the end-to-end delivery rate. In heterogeneous

nodes, there will be fewer hops between normal sensor nodes and the sink. So the

heterogeneous sensor network can get much higher end-to-end delivery rate than

the homogeneous sensor network.

1.1.3 Decreasing Latency of Data Transportation

Computational heterogeneity can decrease the processing latency in

immediate nodes and link heterogeneity can decrease the waiting time in the

transmitting queue. Fewer hops between sensor nodes and sink node also mean

fewer forwarding latency.

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1.2 CHALLENGES AND ISSUES IN CLUSTERING THE WSNs

Despite the tremendous potentials and its numerous advantages like

distributed localized computing in which failure of one part of the network does

not affect the operation in another part of the network, wide area coverage,

extreme environment area monitoring, WSNs pose various challenges to the

research community. This section briefly summarizes some of the major

challenges faced while clustering the WSNs.

1.2.1 Node Deployment

Node deployment in WSNs is either fixed or random depending on

the application. In fixed deployment the nodes are deployed on predetermined

locations whereas in random deployment the resulting distribution can be

uniform or non-uniform. In such a case careful management of the network is

necessary in order to ensure maximum area coverage and also to ensure uniform

energy consumption across the network.

1.2.2 Heterogeneous Network

Nodes in the WSNs are always not uniform in terms of architecture

functionality and life time. In these cases the network is heterogeneous. Some

nodes are less energy constrained than others. Usually the fraction of nodes

which are less energy constrained is small. In such a type of network the less

energy constraint nodes are chosen as cluster head of a cluster and the energy

constrained nodes are the worker nodes of the cluster. The problem arises in such

a network when the network is deployed randomly and all cluster heads are

concentrated in some particular part of the network resulting in Multilayer

Cluster Based Energy Efficient Routing Protocol unbalanced cluster formation

and also making some portion of the network unreachable. Also, if the resulting

distribution of the cluster heads is uniform and if we use multi-hop

communication, the nodes which are close to the cluster head are under heavy

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load as all the traffic is routed from different areas of the network to the cluster

head is via the neighbors of the cluster head. This will cause quick dying of the

nodes in the vicinity of the cluster heads resulting in holes near the cluster heads

and increasing the energy consumption. Heterogeneous sensor networks require

careful management of the clusters in order to avoid the problems resulting from

unbalanced cluster head distribution as well as to ensure that the energy

consumption across the network is uniform.

1.2.3 Network Scalability

When WSNs is deployed, some time new nodes need to be added to

the network in order to cover more area or to prolong the life time of the current

network. In both the cases the clustering scheme should be able to adapt to

changes in the topology of the network. The key point in designing such

management schemes should be if the algorithm is local and dynamic it will be

easy for it to adapt to topology changes.

1.2.4 Uniform Energy Consumption

Transmission is more energy consuming compared to sensing and

computation in WSNs, therefore the cluster heads which performs the function of

transmitting the data to the base station consume more energy compared to the

rest of the nodes. Clustering schemes should ensure that energy dissipation

across the network should be uniform and the cluster head should be rotated in

order to balance energy consumption across the network.

1.2.5 Multi-Hop or Single Hop Communication

The communication model that wireless sensor networks use is either

single hop or multi-hop. Since energy consumption in wireless system is directly

proportional to the square of the distance, single hop communication is expensive

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in terms of energy consumption. Most of the routing algorithms use the multi-

hop communication model because it is more energy efficient in terms of energy

consumption. However, with multi-hop communication the nodes which are

nearer the cluster head are under heavy traffic and can create holes near the

cluster head.

1.2.6 Attribute Based Addressing

The sheer number of nodes it is not possible to assign unique IDs to

nodes in WSNs. Data is accessed from nodes via attributes and not by IDs. This

makes intrusion into the system easier and implementing a security mechanism

difficult.

1.2.7 Cluster Dynamics

Cluster dynamics means how the different parameters of the cluster

are determined for example, the number of clusters in a particular network. In

some cases the number is pre-assigned and in some cases it is dynamic. The

cluster head performs the function of compression as well as the transmission of

data. The distance between the cluster heads is a major issue. It can be dynamic

or can be set in accordance with some minimum value. In case of dynamic then

there is a possibility of forming unbalanced clusters. While limiting it by some

pre-assigned minimum distance can be effective in some cases but this is an open

research issue. Also, cluster head selection can either be centralized or

decentralized. Both have advantages and disadvantages. The number of clusters

might be fixed or dynamic. Fixed number of clusters cause less overhead and the

network will not have to go again and again through the set up phase in which

clusters are formed. In terms of scalability the fix clustering scheme is not

feasible.

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Figure.1.2 Wireless sensor network

1.3 APPLICATIONS OF HETEROGENEOUS WIRELESS SENSOR NETWORK

1.3.1 Disaster Relief Applications

A typical scenario is wildfire detection.Sensor nodes are equipped

with thermometers and determine their own location. sensors are deployed over

a wildfire (from aeroplane). They collectively produce a “temperature map” of

the area disaster relief applications and have commonalities with military

applications. In such an application, sensors should be cheap enough to be

considered disposable since a large number is necessary and lifetime

requirements are not particularly high.

1.3.2 Environment Control and Biodiversity Mapping

WSNs can be used to control the environment. A possible application

is garbage dump sites Another example is the surveillance of the marine ground

floor. An understanding of its erosion processes is important for the construction

of offshore wind farms. WSNs to gain an understanding of the number of plant

and animal species that live in a given habitat. An advantages of WSNs here are

the long-term, unattended, wire free operation of sensors close to the objects that

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have to be observed. The sensors can be made small enough to be unobtrusive,

they only negligibly disturb the observed animals and plants.

1.3.3 Intelligent Building

Buildings waste vast amounts of energy by inefficient Humidity,

Ventilation, Air Conditioning (HVAC) usage. Real-time, high-resolution

monitoring of temperature, airflow, humidity, and other physical parameters-

required. WSN can considerably increase the comfort level of inhabitants and

reduce the energy consumption. Improved energy efficiency as well as improved

convenience are some goals of “intelligent buildings. Sensor nodes can be used

to monitor mechanical stress levels of buildings. Mechanical parameters like the

bending load of girders, it is possible to quickly ascertain via a WSN whether it

is still safe to enter a given building after an earthquake. Other types of sensors

might be geared toward detecting people enclosed in a collapsed building and

communicating such information to a rescue team.

1.3.4 Facility Management

WSNs have a wide range of possible applications- management of

facilities larger than a single building. Examples - keyless entry

applications,where people wear badges that allow a WSN to check which person

is allowed to enter which areas of a larger company site. An extended to the

detection of intruders, for example of vehicles that pass a street outside of

normal business hours. Widearea WSN could track such a vehicle’s position and

alert security personnel. WSN could be used in a chemical plant to scan for

leaking chemicals. These applications combine challenging requirements as the

required number of sensors can be large and they should be able to operate a

long time on batteries.

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1.3.5 Machine Surveillance and Preventive Maintenance

Idea is to fix sensor nodes to difficult to reach areas of machinery

where they can detect vibration patterns. Examples for such machinery could be

robotics or the axles of trains. Advantage of WSNs - cable free operation,

avoiding a maintenance problem in itself. Wired power supply may or may not

be available depending on the scenario. If it is not available, sensors should last

a long time on a finite supply of energy since exchanging batteries is usually

impractical and costly.

1.3.6 Precision Agriculture

WSN to agriculture allows precise irrigation and fertilizing by

placing humidity/soil composition sensors into the fields. A relatively small

number is claimed to be sufficient, about one sensor per 100 m × 100 m area.

Similarly, pest control can profit from a high-resolution surveillance of farm

land. The livestock breeding can benefit from attaching a sensor to each pig or

cow, which controls the health status of the animal raises alarms if given

thresholds are exceeded.

1.3.7 Medicine and health care

WSN in health care applications is a potentially very beneficial.

Possibilities range from postoperative and intensive care, where sensors are

directly attached to patients. Patient and doctor tracking systems within hospitals

can be literally life saving.

1.3.8 Telematics

This is partially related to logistics applications. where sensors

embedded in the streets or roadsides can gather information about traffic

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conditions. Also interact with the cars to exchange danger warnings about road

conditions or traffic jams ahead.

1.3.9 Logistics

Sensors are requires for several different logistics applications. It is

conceivable to equip goods (individual parcels, for example) with simple

sensors - allow a simple tracking of these objects during transportation. It

facilitate inventory tracking in stores or warehouses. In these applications, there

is often no need for a sensor node to actively communicate. Passive readout of

data is often sufficient. Passive readout is much simpler and cheaper than the

active communication and information processing. RFID (Radio Frequency

Identification) tag cannot support more advanced applications.

1.3.10 Forest Fire Detection

A sensor network is more feasible as an early warning system for

forests. Carefully placing nodes close to vulnerable areas such as hilltops is an

important event. Reducing the number of sensors required to cover a large

geographic area and an important aspect is lifetime. Sensors must operate for a

very long period of time to discover a comparatively rare event.

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CHAPTER 2

LITERATURE REVIEW

2.1 SURVEY OF CLUSTERING TECHNIQUES IN HWSN

There are two types of energy efficient clustering schemes for

WSNs. The clustering schemes used in homogeneous wireless sensor networks

are called homogeneous clustering schemes, and the clustering schemes used in

heterogeneous wireless sensor networks are called heterogeneous clustering

schemes. The first clustering scheme is Low energy adaptive clustering hierarchy

(LEACH), which play a great role in WSNs to enhance the network life time and

improve the power utility. In LEACH cluster is elected in random manner to

achieve the energy distribution. However, the node consumes more power while

transmitting data from node to base station. LEACH performs well under

homogeneous network, but it fails in heterogeneous WSN because the low-

energy nodes will die more rapidly than high-energy nodes.

2.1.1 Hybrid Energy Efficient Distributed Clustering (HEED) [3]

O.Younis et al. (2004), designed to select different cluster heads in a

field according to the amount of energy that is distributed in relation to a

neighboring node. In HEED sensors are quasi-stationary and links between nodes

are symmetric. Energy consumption is non-uniform among all nodes. Same or

different power levels are used for intra-cluster communication. HEED

distribution of energy extends the lifetime of the nodes within the network thus

stabilizing the neighboring node. Only two level hierarchies provided but can be

extended to multilevel hierarchy.

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2.1.2 Distributed Energy Balance Clustering (DEBC) [11]

X.Wang et al. (2007), proposed a protocol for heterogeneous

wireless sensor networks Cluster heads are selected by a probability depending

on the ratio between remaining energy of node and the average energy of

network. The high initial and remaining energy nodes have more chances to be

the cluster heads than the low energy nodes. This protocol also considers two-

level heterogeneity and then it extends the results for multi-level heterogeneity.

DEBC is different from LEACH, which make sure each node can be cluster head

in each n=1/pr.

2.1.3 Cluster Based Service Discovery for Heterogeneous Wireless Sensor Networks [8]

Marin et al. (2008), proposed an energy efficient cluster based

service discovery protocol (C4SD) for HWSNs. The problem addressed in this

technique is to design a service discovery protocol that is suitable for

heterogeneous WSNs and reduces the workload of the resource constrained

devices. Authors proposed a cluster based solution, where a set of nodes are

selected, based on their capabilities. In this algorithm each node is assigned a

unique hardware identifier and weight. Higher the capability grade more

suitability for CH role. These nodes act as a distributed directory of service

registrations for the nodes in the cluster. Since the service discovery messages

are exchanged only among the directory nodes and the distribution of workload

according to the capabilities of the nodes, the communication costs are reduced.

The proposed clustering algorithm reacts rapidly to topological changes of the

sensor network by making decisions based only on the single-hop neighborhood

information, avoids chain reactions and constructs a set of sparsely distributed

CHs. The clustering algorithm is simulated and compared with distributed

mobility adaptive clustering (DMAC). The result shows that it out performs

DMAC.

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2.1.4 Distributed Data Aggregation Energy Efficient Clustering (DDAEEC)

[12]

Kumar, et al. (2009), proposed DDAEE clustering protocol which fit

for the multilevel heterogeneous wireless sensor networks. Here, all the nodes

use the initial and residual energy level to define the cluster heads. It does not

require any global knowledge of energy at every election round. This DDAEEC

algorithm allows a more number of data to send from cluster head to a base

station in a certain time interval. It improves the life time of WSNs.

2.1.5 Stable Election Protocol (SEP) [3]

Smaragdakis et al. (2004), proposed SEP is based on weighted

election probabilities of each node to become cluster head according to the

remaining energy in each node. SEP, for electing cluster heads in a distributed

fashion in two-level hierarchical wireless sensor networks. SEP is heterogeneous-

aware, in the sense that election probabilities are weighted by the initial energy of

a node relative to that of other nodes in the network. This prolongs the time

interval before the death of the first node (stability period), which is crucial for

many applications where the feedback from the sensor network must be reliable.

2.1.6 Novel Stable Selection and Reliable Transmission Protocol for Clustered HWSN [5]

H. Zhou et al. (2008), proposed a model of energy and computation

heterogeneity for heterogeneous wireless sensor networks. They also propose a

protocol named Energy Dissipation Forecast and Clustering Management

(EDFCM) for HWSNs. This algorithm balances the energy consumption round

by round, which will provide the longest stability period for network. The

heterogeneous model they consider is composed of three types of nodes

including Type_0, Type_1 and some management nodes. Type_0 and Type_1

nodes vary in capabilities of sensing, energy and software. They have the

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responsibility of sensing events, while the management nodes perform

management of both types of nodes during cluster formation. EDFCM is

specially proposed for heterogeneous networks to provide the longer lifetime and

more reliable transmission service.

Unlike the other energy efficient protocols, the process of cluster

head selection in EDFCM is based on a method of one step energy consumption

forecast. It uses the average energy consumptions of the two types of cluster

heads in previous round for this purpose. The more remaining energy in a node

after the operation of next round, higher the chances of node to be selected as a

cluster head. In EDFCM protocol, the operation of network can be divided into

two phases: cluster formation phase and data collecting phase. Cluster formation

phase of EDFCM is very similar to that of LEACH, but there are two differences:

The selection probability is a weighted function.

It guarantees a stable number of cluster heads each round.

2.1.7 Energy Efficient Heterogeneous Clustered Scheme (EEHC) [9]

Xin, G et al. (2008), EEHC for electing cluster heads in a distributed

fashion in hierarchical wireless sensor networks. The election probabilities of

cluster heads are weighted by the residual energy of a node relative to that of

other nodes in the network. The algorithm is based on LEACH and works on the

election processes of the cluster head in presence of heterogeneity of nodes.

2.1.8 The Steady Clustering Scheme for HWSN [4]

Liaw et al. (2009), proposed a protocol based on SGCH (Steady

Group Clustering Hierarchy). This protocol divides all nodes into groups by

initial energy. This algorithm proceeds in two steps: Grouping stage and data

transmission stage. Groups are generally clusters. In this algorithm, BS

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broadcasts a Group Head Request (GHR) to all nodes. Every node sends back the

acknowledgement (ACK) with ID and initial energy information. BS selects the

group head by sending a Group Head GH message and group ID. Now every

group head finds its member by sending group request message to all nodes.

Following this way, algorithm forms the groups (clusters). Algorithm considers

the multilevel heterogeneity of sensor nodes in terms of energy.

2.1.9 Routing Protocol for Balancing Energy Consumption in HWSN [7]

Li X. et al. (2007), developed and analyzed a protocol based on

residual energy and energy consumption rate (REECR). It presented the protocol

based on the REECR rather than periodic rotation and stochastic election.

REECR protocol was not perfect in balancing the energy and stability of

network, so they proposed a zone based improvement of this REECR protocol,

naming ZREECR (Zone Based Residual Energy and Energy Consumption Rate).

This protocol improves the stability period. The problem that is considered in this

work is that the cluster head may be very near or very far from BS. In such a

case, balancing the energy consumption is a very tough task and leads to

instability.

2.1.10 Base Station Initiated Dynamic Routing Protocol [9]

S. Verma et al. (2008), propose a routing protocol that is based on

clustering and uses heterogeneity in nodes to increase the network lifetime. In

this scheme, some nodes which are stronger than other nodes in terms of power,

computational capability and location awareness, work as the cluster heads. They

forward information to their parents, towards the base station by aggregating all

the information from their clusters members. Assumptions are considered in this

schemes are: All nodes are deployed uniformly in the field and CHs will be

assumed dead only when their energy is very less. There is no collision between

inter cluster and intra cluster communication. Transmission power of the CH is

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adjusted in such a way that only single hop broadcast is possible. In this

algorithm, how far a CH is from the BS, is defined as level. Low level means that

CH is near to the BS and if level is high it means CH is away from BS

accordingly. Data flow will be always from higher level to lower level. Decision

of levels by base station is based on the range of the CH and normal node.

Ranges of all the nodes are enough to ensure the connectivity and coverage. BS

sets its level to zero and broadcasts a packet to initiate the scheme. Base station

mentions that this packet is only for CHs. Since the CHs have different signal

strength from normal nodes, they receive the packet and set their levels

accordingly. When the CHs of first level are selected, they broadcast their level.

CHs at lower level receive the packet according to the signal strength. They

choose their parent from upper level CHs only. This process is repeated again

and again until all CHs are connected. CH now broadcast a message that all

sensor nodes should join the CH according to the RSS (Radio Signal Strength).

Communication between CH and sensing nodes is single hop, while

communication between different CH is multiple hops. All CHs sends their

position, level and energy consumption to the BS at the end of the round. BS then

analyzes the energy consumption of different CH at the same level.

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CHAPTER 3

EXISTING TECHNIQUES

In recent years, researchers have proved that clustering is an efficient

scheme in increasing network lifetime and scalability of WSNs. In clustering

schemes, there are two types of nodes in one cluster, one cluster head (CH) and

several cluster members. Cluster members collect data from the environment

periodically and send the data to the CHs. CHs serve as fusion point for data

aggregation, so that the actual data transmitted to the base station (BS) is

reduced. Clustered WSNs could also be classified as single-hop and multi-hop. In

a single-hop clustered WSN, the sensor nodes communicate directly with the CH

using a single-hop communication. In a multi-hop WSN, the CHs use multi-

hopping to reach the BS. However, multi-hop communication is often required,

when the communication range of the sensor nodes are limited or the monitoring

area is very large. For direct communication, the CHs furthest away from the BS

are the most critical nodes, whereas in multi-hop communication; the CHs closest

to the BS are burdened with a heavy relay traffic load and die first. Therefore

clustering and multi-hop communication are the most efficient routing schemes

in WSNs to balance the relay traffic over the network and effectively overcome

the path loss effects.

In an existing technique, the effect of heterogeneity in terms of

battery energy is studied because the cost of a sensor node is ten times more than

the cost of an embedded battery. An author considered three types of nodes –

normal, advanced and super nodes with different battery energy. In S-EECP, the

CHs are elected based on different weighted probabilities. The weighted

probability is evaluated based on the ratio between residual energy of each node

and average energy of the network. The nodes with high initial and residual

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energy will have more chances to become CHs per round per epoch. In M-EECP,

after the election of CHs, the member nodes communicate directly with the CH

using a single-hop communication. On the other hand, CHs use multi-hopping to

reach the BS.

Three types of sensor nodes deployed uniformly in a square region,

that is, normal nodes and a few super and advanced nodes. Note E0 is the initial

energy of normal nodes. Let m be the fraction of N normal nodes, which own α

times more energy than the normal ones, we refer to these nodes as advanced

nodes. Thus, there are m × N advanced nodes equipped with initial energy E0(1 +

α). The proportion m0 of super nodes among advanced nodes are equipped with β

times more energy than the normal nodes. Thus, there are m × m0 super nodes

equipped with initial energy E0(1 + β). Hence, the total initial energy of the new

heterogeneous network setting is given by the following equation

……..(3.1)

Where S = (α – m0 × (α − β)). All the CHs are elected periodically by

different weighted probability. Each Member node communicates with their

respective CHs by using single-hop communication (i.e. intra-cluster

communication). Then CHs collect the data from the member nodes in their

respective clusters, aggregate it and transmit it to the BS using multi-hop

communication (i.e. inter-cluster communication).

3.1 Network Model

The first state assumption for the sensors in network model:

All the sensor nodes are uniformly dispersed within a square field.

Sensor nodes and the BS are left unattended after deployment.

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Each member node communicates with their respective CHs using

single-hop communication approach and multi-hop communication

approach is adopted for inter-cluster communication.

A WSN consists of heterogeneous nodes in terms of node energy.

All the sensor nodes are of equal significance.

Data aggregation is the most popular process of aggregating the data

from multiple nodes to eliminate redundant transmission in

clustering schemes, in which each CH aggregates the collected data

and transmits the fused data to the BS.

The BS node has a rechargeable battery in comparison with the other

nodes in the network.

3.2 Optimal Clustering

A similar radio model as discussed in for radio hardware energy

dissipation is used here.

Assume that the distance between transmitter and receiver is d, the

energy consumed for transmitting L bits data from transmitter to the receiver is

given by the following equation

……(3.2)

Where Eelec is the amount of energy consumption of the wireless

circuit for sending and receiving data. By equating the two expressions at d = d0,

we have d0 = Both the parameters εfs free space and the εmp multi-

path fading channel models vary according to the distance between a sender and

a receiver. If the distance is less than a threshold d0, the free space (fs) model is

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used; otherwise, the multi path (mp) model is used. The cost of energy required

for receiving the data is given by the following equation

………..(3.3)

Assume area M × M square metres, and N is the number of nodes

uniformly distributed over the square area. The BS is located at the centre of the

network for simplicity. Each non-CH node sends L bits data to the elected CH

node. Thus, the energy dissipated in the CH node during a round is given by the

following equation

……..(3.4)

Where k is the number of clusters, EDA is the processing cost of a bit

report to the BS and dBS is the average distance between a CH and the BS. The

energy used in a non-CH is given by the following equation

……..(3.5)

Where dCH is the average distance between a cluster member and its

CH, which is given by the following equation

……(3.6)

Where, ρ(x, y) is the node distribution. By combining (3.4) and (3.5),

the total energy dissipated during a round is obtained and given by the following

equation

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……(3.7)

By differentiating, Eround with respect to k and equating to zero, the

optimal number of clusters can be evaluated by the following equation

……..(3.8)

If the distance of a significant percentage of nodes to the BS is

greater than d0 then, the same analysis as discussed in,the following equation

…..(3.9)

By using (3.8) and (3.9), the optimal probability of a node to become

a CH, popt, is obtained which can be computed by the following equation

……(3.10)

Substituting (3.6)–(3.9) into (3.7), the energy Eround dissipated during

a round is obtained. The optimal probability of a node to become a CH is very

important because if the clusters are not constructed in an optimal way, the total

energy consumed during a round is increased exponentially.

3.3 CH Election Mechanism

In LEACH, during the set-up phase, each node generates a random

number between 0 and 1. If this random number is less than the threshold value,

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T(s), which is given by (3.11), then the node becomes a CH for the current round.

During each round, new CHs are elected and as a result balanced load energy is

distributed among the CHs and other nodes of the network

……(3.11)

Where popt is the desired percentage of CHs, r is the count of current

round, G is the set of sensor nodes that have not been elected as CHs in the last

1/popt rounds. LEACH is an iterative process and each iteration is referred to as a

‘round’. Here, round r is defined as a time interval where all clusters members

have to transmit to the CH once.

The first improvement in (3.11) is inclusion of the residual energy

level available for node. It can be derived by reducing the threshold, denoted by

(3.11), relative to the ratio between residual energy of each node and average

energy of the network which is given by the following equation

……(3.12)

Where Ei is the current residual energy of each node and Eavg is the

average energy of the network. The average energy is used as the reference

energy for each node. Initially, all the nodes need to know the total energy and

network lifetime which can be determined a priori. Therefore estimate the

average energy Eavg of the network at rth round by the following equation

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……(3.13)

Where R denotes the total number of rounds of the network lifetime,

which means that every node consumes the same amount of energy in each

round. It is assumed that all the nodes die at the same time, R is the total number

of rounds from the network beginning to all the nodes dying. Let Eround denote the

energy consumed by the WSN in each round. Thus, R can be calculated by the

following equation

…….(3.14)

This above modification of the threshold equation has a drawback.

After certain number of rounds the network is stuck, however, there are still alive

nodes available with enough energy to transmit data to the BS. The reason is that

the remaining nodes have a very low energy level which makes the CH threshold

level too low and hence, CHs election process becomes unstable. Further

modification has been done in threshold (3.12) to solve the above problem. It is

expanded by a factor which increases the threshold for any node that has not been

elected as CH for the last 1/pi rounds. Hence, the new modified threshold value is

given by the following equation (see (3.15)) where rs is the number of

consecutive rounds in which a node has not become CH. Hence, the chance of

each type of node to become a CH increases because of a higher threshold value.

In S-EECP and M-EECP, ti is denoted as the rotating epoch. All the nodes cannot

possess the same residual energy when the network evolves. If the rotating epoch

is the same for all the nodes, then the energy will not be well distributed among

the nodes and therefore the low-energy nodes will die more quickly than the

high-energy nodes. To solve this problem, different epochs based on residual

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energy Ei(r) of each node per round are chosen which is given by the following

equation:

………..(3.15)

3.4 Data Communication Phase

Once the clusters are formed and the TDMA schedule is fixed, the

data communication phase can begin. The active sensor nodes periodically

collect the data and transmit it during their allocated transmission time to the CH.

The radio of each non-CH or member node can be turned off until the node’s

allocated transmission time which minimizes energy consumption in these nodes.

The CH node must keep its receiver on to receive all the data from the member

nodes in the cluster. When all the data have been received, the CH nodes

aggregate the data and route the aggregated data packets to the BS via multi-hop

communication approach.

………………(3.16)

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3.5 Traffic Model

The network traffic model depends on the network application and

the behavior of sensed events. The process of data reporting in WSNs is usually

classified into three categories: (i) time driven, (ii) event driven and, (iii) query

driven. In the time driven case, sensor nodes transmit their data periodically to

the BS. Event driven networks are used when it is desired to inform the BS about

the occurrence of an event. In query-based networks, BS sends a request of data

gathering when it is needed. The time driven scenario is the main focus in S-

EECP and M-EECP because in time driven WSNs, nodes take readings of the

environment and report the same data in a periodic way. Thus, the traffic

generated by these WSNs becomes very predictable, and the use of TDMA-based

mechanisms is not only possible, but also recommended from the perspective of

low-energy consumption.

26

CHAPTER 4

PROPOSED TECHNIQUES

There are various approaches proposed for energy efficient

clustering mechanism in heterogeneous wireless sensor networks. In order to

reduce the energy consumption, cluster formation and cluster head selection is

introduced. A distributed energy efficient clustering mechanism required for

cluster formation and energy efficiency. In this paper the proposed new cluster

head selection mechanism to increase the life time of WSN and a reconfiguration

approach to optimize the energy consumption in inter cluster communication.

4.1 Distributed Energy Efficient Clustering Mechanism

There are two phases that are accomplished in DEECM: setup

phase and steady state phase. In the setup phase, cluster head selection and

cluster formation process is completed. In the steady state phase, selected cluster

head is identified and the selected cluster head uses the inter cluster

communication. Owing to randomness property of WSN; cluster can be small or

large in their deployed region. Large sized clusters consumes more energy than

small sized clusters, because it has long intra cluster distance, i.e., the distance

between the cluster members and the cluster head. In this paper, an improvised

technique that uses multi hop communication links between the cluster members

and the cluster heads is proposed here. Moreover, cluster heads communicate

with the base stations in a multi hop manner. So energy is distributed equally

among the nodes and this result in considerable conservation of energy. The

distance based probability is a function of the distance of the normal sensor to the

advanced sensor. The proposed system develop distributed algorithm which

27

addresses the energy-efficient clustering under the joint coverage and routing

constraint.

There are some assumptions for our protocol as given below;

Base station should be known to all cluster members.

Base station should be independent of energy resources.

Sensor nodes are static.

Sensor nodes locations are not known.

4.2 Cluster Head Selection

Cluster head selection and cluster formation processes are

completed in the setup phase. The cluster head aggregate the data and send to

base station.

Figure 4.1 M-DEECM

Each sensor node computes its approximate distance according to

the strength of receiving signals. The sensor nodes elect the cluster head

according to high energy or threshold. At initial, advanced node is elected as a

cluster head which consists of high threshold value. Each sensor nodes chooses a

random number between 0 and 1 individually. If this is lower than the calculated

28

threshold T(i) for node i, then it becomes a cluster head. Each node becomes a

cluster head in one epoch. An epoch is defined as,

epoc h= 1Popt

………….(4.1)

In the three level heterogeneous nodes (normal nodes, advanced

nodes, and super nodes), a reference value Popt has been replaced by weighted

probabilities. The advanced node (m) has α amount of more energy than normal

nodes.

Pnorm= Popt1+αm

…...….. (4.2)

Padvanced= Popt1+∝m

(1+∝) ..…….….(4.3)

Psuper= Popt1+βm

(1+β ) ………….(4.4)

The total initial energy of the new heterogeneous network setting is

given by the following equations,

E total=N × Eo ×(1+m× S ) …………(4.5)

Where,

S = (α – mo × (α – β))

The CHs are elected periodically by different weighted probability.

Each member node communicates with their respective CHs by using, multi-hop

communication (i.e. inter-cluster communication) when the distance between

normal node to base station is greater. Then CHs collect the data from the normal

29

nodes in their respective clusters, aggregate it and transmit it to the BS using

multi-hop communication.

Assume that the distance between transmitter and receiver is d, the

energy consumed for transmitting L bits data from transmitter to the receiver is

given by the following equation,

Etx ( L, d )=¿L × Ed+L× ɛmp × d4

L × Eelec+L ×ɛ fs ×d 2

¿ …………… (4.6)

Where, Eelec is the amount of energy consumption of the wireless

circuit for sending and receiving data. The energy require for receiving the data is

given by the following equation,

Erx = L × Eelec ……………….(4.7)

The probability of a node to become a cluster head, Popt which can be calculated by the equation,

Kopt=√ N2 π √ ε fs

εmp

M

dBS2 ……………(4.8)

Where, d BS=0.765∗M

2

By using (4.8), the optimal probability of a node to become a

cluster head is constructed. The average energy Eavg of the network at ith,

Eavg ( i )= 1N

E (1− iR

) ………….(4.9)

Where, R is the total number of rounds of the network lifetime.

Therefore, every node consumes the same amount of energy at each epoch.

R=Etotal

E round ………….(4.10)

The average energy for the different epoch based on residual

energy Ei(r) of each node per round can be obtained as,

30

Eavg (r )= 1N∑i=1

N

Ei(r ) …………..(4.11)

4.3 Multi-Hop Communication Mechanism

Two major communication patterns are single-hop and multi-hop

which is mostly used in WSNs. Single-hop communication transmits the data

directly to the BS without any relay, thus the node located away from the BS will

consume more energy than the other nodes and drains faster, die out first. To

conquer this problem, we use shortest path algorithm.

Directed weighted graph for direct communication G = V, E,

where V is a set of nodes and E is a set of edges. Each edge is a pair (v, w),

where v, w V. Edges are sometimes referred to as arcs. If the pair is ordered,

then the graph is directed. Vertex w is adjacent to v if and only if (v, w) E. In

an undirected graph with edge (v, w), and hence (w, v), w is adjacent

to v and v is adjacent to w. Sometimes an edge has a third component, known as

either a weight or a cost.

A cycle in a directed graph is a path of length at least 1 such

that w1 = wn; this cycle is simple if the path is simple. For undirected graphs, we

require that the edges be distinct. The logic of these requirements is that the

path u, v, u in an undirected graph should not be considered a cycle, because

(u, v) and (v, u) are the same edge. In a directed graph, these are different edges,

so it makes sense to call this a cycle. A directed graph is acyclic if it has no

cycles. A directed acyclic graph is sometimes referred to by its

abbreviation, DAG.

An undirected graph is connected if there is a path from every

vertex to every other vertex. A directed graph with this property is called strongly

connected. If a directed graph is not strongly connected, but the underlying graph

(without direction to the arcs) is connected, then the graph is said to be weakly

31

connected. A complete graph is a graph in which there is an edge between every

pair of vertices.

Figure 4.2 Direct Weighted Graphs

4.4 Data Communication Phase

Formerly the clusters are formed and the TDMA plan is set, the

data communication phase can begin. The active node periodically collects and

transmits the data on the basis of allocated time to the CH, where the remaining

node are turned off to minimize the energy consumption. After collecting

information from all the nodes, CH aggregate the data and route that to BS via

multi-hop communication.

32

CHAPTER 5

SOFTWARE DESCRIPTION

5.1 INTRODUCTION TO NETWORK SIMULATOR 2 (NS2)

Network Simulator (Version 2), widely known as NS2, is simply an

event driven simulation tool that has proved useful in studying the dynamic

nature of communication networks. Simulation of wired as well as wireless

network functions and protocols (e.g., routing algorithms, TCP, UDP) can be

done using NS2. In general, NS2 provides users with a way of specifying such

network protocols and simulating their corresponding behaviors. Due to its

flexibility and modular nature, NS2 has gained constant popularity in the

networking research community since its birth in 1989.

Ever since, several revolutions and revisions have marked the

growing maturity of the tool, thanks to substantial contributions from the players

in the field. Among these are the University of California and Cornell University

who developed the REAL network simulator,1 the foundation which NS is based

on. Since 1995 the Defense Advanced Research Projects Agency (DARPA)

supported development of NS through the Virtual Internetwork Tested

(VINT)project .Currently the National Science Foundation (NSF) has joined the

ride in development. Last but not the least, the group of researchers and

developers in the community are constantly working to keep NS2 strong and

versatile.

5.2 BASIC ARCHITECTURE

33

Figure 5.1 shows the basic architecture of NS2. NS2 provides users

with executable command ns which takes on input argument, the name of a Tcl

simulation scripting file. Users are feeding the name of a Tcl simulation script

(which sets up a simulation) as an input argument of an NS2 executable

command ns. In most cases, a simulation trace file is created, and is used to plot

graph and/or to create animation.NS2 consists of two key languages: C++ and

Object-oriented Tool Command Language (OTcl).

While the C++ defines the internal mechanism (i.e.,a backend) of

the simulation objects, the OTcl sets up simulation by assembling and

configuring the objects as well as scheduling discrete events (i.e., a frontend).

The C++ and the OTcl are linked together using TclCL. Mapped to a

C++ object, variables in the OTcl domains are sometimes referred to as handles.

Conceptually, a handle (e.g., n as a Node handle) is just a string (e.g.,_o10) in the

OTcl domain, and does not contain any functionality. Instead, the functionality

(e.g., receiving a packet) is defined in the mapped C++ object (e.g., of class

Connector). In the OTcl domain, a handle acts as a front end which interacts with

users and other OTcl objects. It may defines its own procedures and variables to

facilitate the interaction.

In figure 5.1 is a general user can be thought of standing at the left

bottom corner, designing and running simulations in Tcl using the simulator

objects in the OTcl library. When a simulation is finished,NS produces one or

more text-based output files that contain detailed simulation data,if specified to

do so in the input Tcl script.

34

Figure 5.1 Basic Architecture of network simulator

Note that the member procedures and variables in the OTcl domain

are called instance procedures (instprocs) and instance variables (instvars),

respectively. Before proceeding further, the readers are encouraged to learn C++

and OTcl languages.

NS2 provides a large number of built-in C++ objects. It is advisable

to use these C++ objects to set up a simulation using a Tcl simulation script.

However, advance users may find these objects insufficient. They need to

develop their own C++ objects, and use a OTcl configuration interface to put

together these objects.

After simulation, NS2 outputs either text-based or animation-based

simulation results. To interpret these results graphically and interactively, tools

such as NAM (Network AniMator) and XGraph are used. To analyze a particular

behavior of the network, users can extract a relevant subset of text-based data and

transform it to a more conceivable presentation.

NS2 is an object oriented simulator written in OTcl and C++

languages. While OTcl acts as the frontend (i.e., user interface), C++ acts as the

backend running the actual simulation (Figure 5.2). There are two types of

35

classes in each domain. The first type includes classes which are linked between

the C++ and OTcl domains. In the literature, these OTcl and C++ class

hierarchies are referred to as the interpreted hierarchy and the compiled hierarchy

respectively. The second type includes OTcl and C++ classes which are not

linked together. These classes are neither a part of the interpreted hierarchy nor a

part of compiled hierarchy.

Figure 5.2 C++ and OTcL 5.3 NS2 SIMULATION STEPS

The following steps shows the three key guideline is defining a

simulation scenario in a NS2:

Step 1: Simulation Design

The first step in simulating a network is to design the simulation. In

this step, the users should determine the simulation purposes, network

configuration and assumptions, the performance measures, and the type of

expected results.

Step 2: Configuring and Running Simulation

This step implements the design in the first step. It consists of two phases:

36

• Network Configuration Phase

In this phase network components (e.g.,node, TCP and UDP) are

created and configured according to the simulation design. Also, the events such

as data transfer are scheduled to start at a certain time.

• Simulation Phase

This phase starts the simulation which was configured in the

Network Configuration Phase. It maintains the simulation clock and executes

events chronologically. This phase usually runs until the simulation clock

reached a threshold value specified in the Network Configuration Phase.

In most cases, it is convenient to define a simulation scenario in a Tcl

scripting file (e.g., <file>) and feed the file as an input argument of an NS2

invocation (e.g., executing “ns <file>”).

Step 3: Post Simulation Processing

The main tasks in this step include verifying the integrity of the

program and evaluating the performance of the simulated network. While the

first task is referred to as debugging, the second one is achieved by properly

collecting and compiling simulation result.

The data can be used for simulation analysis or as an input to a

graphical simulation display tool called Network Animator(NAM) that is

developed as a part of VINT project.NAM has a nice graphical user interface

similar to that of a CD player (play, fast-forward, rewind, pause and so on),and

also has a display speed controller. Further, it can graphically present information

such as throughput and number of packet drops at each link, although the

graphical information cannot be used for accurate simulation analysis.

37

4.4 KEY FEATURES

1. Router Queue Management Technique

2. Multicasting

3. Simulation of Wireless Networks

4. Traffic Source Behaviour-CBR, VBR

5. Transport Agents-UDP/TCP

6. Routing

7. Packet Flow

8. Network Topology

9. Applications-Telent,FTP,Ping

10. Tracking Packet on all links/specific links

38

CHAPTER 6

RESULTS AND DISCUSSION

A heterogeneous clustered WSN has been simulated with 100 sensor nodes. The

normal nodes, advanced nodes and super nodes are randomly distributed over the

remote control area. Therefore the horizontal and vertical coordinates of sensor

node is selected randomly. The base station considered to be in corner of the

sensing field.

6.1 SIMULATION WINDOW FOR CLUSTER FORMING PHASE AND

SELECTION OF CLUSTER HEAD

Figure 6.1 Cluster Forming Phase

Three types of nodes are deployed and selected the cluster head

based on the transmission distance and residual energy. In the experiment, the

39

cluster members i.e., normal nodes use multi-hopping to communicate with the

elected CH. CHs use multi-hopping to communicate with the BS.

6.3 SIMULATION RESULT FOR NETWORK LIFETIME

Figure 6.3 Network Lifetime

Network lifetime strongly depends on the lifetimes of single nodes

that constitute heterogeneous WSNs. The definition of network lifetime is

determined by the kind of service it provides. Often, it is necessary that all the

sensor nodes stay alive as long as possible because network performance

decreases as soon as single node dies. In this scenario, it is important to know

when the first node dies.

40

6.4 SIMULATION RESULT FOR PACKET DELIVERY RATIO

Figure 6.3 Packet Delivery Ratios

It defines Number of data packets received at the BS for DEECM

protocol. Therefore DEEM overcomes the imbalance energy consumption

problem by using multi-hop communication among all nodes. It achieves balance

of energy consumption among all nodes and enhances the network lifetime.

41

CHAPTER 7

CONCLUSION

In this work a new clustering based protocol – Distributed energy

efficient clustering mechanism for heterogeneous wireless sensor networks is

obtained. In this protocol, three types of nodes with different battery energy

which is a source of heterogeneity. To suggest an improvised technique that

further enhances the conservation of energy than the existing methods. A new

method is designed, that uses multi hop communication links between the cluster

members and the cluster heads. Further the cluster heads communicate with the

base stations in a multi hop manner. WSN has a rigid requirement regarding its

power consumption due to the limited and non rechargeable energy supply. So by

employing this new improvised technique, the energy is distributed equally

among the nodes and this brings about a considerable conservation of energy.

This method is helpful when the clusters far from base stations need to

communicate with these base stations and they communicate using multi hop

links thereby reducing the energy consumed. Furthermore, the intra cluster

communications using multi hop links distributes the energy among the nodes

within the cluster thereby further reduction of energy consumed. Hence this

method provides an efficient way of conserving energy in wireless sensor

networks and thus increasing the lifetime of wireless sensor nodes within a

network.

42

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