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CHAPTER 3

DATA AGGREGATION OPTIMAL - LOW ENERGY

ADAPTIVE CLUSTERING HIERARCHY

3.1 INTRODUCTION

The Proposed Data Aggregation Optimal-Low Energy Adaptive

Clustering Hierarchy (DAO-LEACH) protocol is used for an energy efficient

routing. The energy efficient routing in WSN is based on an effective data

ensemble and optimal clustering.

A Wireless Sensor Network (WSN) consists of spatially distributed

autonomous nodes to monitor physical or environmental conditions and to

pass their data through the network to an admin. The WSN is built of “Nodes”

where each node is connected to one sensor. One of the main problems in

WSN is developing an energy efficient routing protocol to enhance the

network longevity.

In order to minimize the energy divertissement of sensor nodes and

optimize the resource utilization, the cluster head is admitted for each user.

The energy efficient routing in WSN is achieved by combining the nodes

having the maximum residual energy. Data sensed by the sensor nodes in

WSN are ultimately transmitted to the base station where the information can

be accessed. Moreover, each sensor node of WSN is composed of four

substantial blocks namely,

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Sensing Unit

Processing Unit

Communication Unit

Power Unit

The Sensing unit measures a certain physical condition like

temperature and pressure in the contributed environment. The Processing unit

includes collecting and processing signals obtained from the sensors. The

Wireless communication unit is amenable for transferring the signals from the

sensor to the user via the Base Station (BS). The Power unit sustains all

previous units to provide the required energy in order to accomplish the

mentioned tasks. At the time of inspection, the energy efficiency has been

prominent as the most important issue in research of WSN. Accordingly,

there is great implication to design an energy efficient routing protocol for

WSN. On the subject of routing protocol, there account two different

solutions from the existing works, given as flat routing and hierarchical

routing. In flat routing, each sensor node encompasses in the same role and

sends their data to sink node precisely which always results in faster energy

consumption and excessive data redundancy.

In hierarchical routing, the complete network is split into several

clusters, correspondingly the distance between the nodes and the hop count.

The central objectives for WSN are reliability, accuracy, flexibility, cost

effectiveness, and ease of deployment. Clustering based routing algorithms

are more capable and convenient than flat routing algorithms in WSN.

Besides, the data aggregation process reduces the number of

message interchange between the node and the BS and it recovers some

energy. The proposed system has been developed with the application of the

efficiencies and deficiencies of Low Energy Adaptive Clustering

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Hierarchy(LEACH), which is one of the prominent and adequate protocols

which promotes the nodes to minimize the energy consumptions in the

networks is LEACH (Low Energy Adaptive Clustering Hierarchy).

3.2 CLUSTERS INWIRELESS SENSOR NETWORK

A Wireless Sensor Network (WSN) is a store of tiny sensors, each

being capable of “sensing/monitoring” the environment, processing these

sensed “signals” and communicating (transmitting and receiving) with other

sensor nodes. Communication in a WSN among any two nodes which are of

one another’s transmission range is achieved through intermediate nodes,

which broadcasts messages to set up a communication channel among the two

nodes. In many applications, the sensor nodes are left unattended to

continuously report their measurements until they run out of energy (battery).

Figure 3.1 exemplifies the Architecture of WSN.

Figure 3.1 Architecture of wireless sensor network

WSN has the following characteristics:

It includes two kinds of nodes

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1. Sensor nodes with limited energy can sense their own

residual energy.

2. One BS without energy restriction is far away from the

area of sensor nodes.

All sensor nodes are immobile. They use the direct

transmission or multi-hop transmission to communicate with

the BS.

Sensor nodes sense environment at a settled rate and always

have data to send to the BS.

Sensor nodes can develop the transmission power of wireless

transmitter according to the distance.

CH performs data aggregation and BS receives the

compressed data.

The lifespan of WSN is the total amount of time taken before

the first sensor node runs out of power.

There are many application areas improved form WSN, e.g., target

tracking and habitat monitoring. Many of these applications require simply an

aggregate value to be reported to the “information sink”. In these cases,

sensors in different regions of the field can collaborate to aggregate the

information they gathered.

It is noticeable by organizing the sensor nodes in groups i.e.,

clusters of nodes wherein significant network performance gains can be

reaped. Clustering not only allows aggregation, but also limits data

transmission primarily within the cluster, thereby reducing both the network

traffic and the contention for the channel.

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Sensor nodes are dimly deployed in WSN which means physical

environment would produce very similar data in close by sensor node and

broadcasting such type of data is more or less redundant. So all these facts

encourage using some kind of grouping of sensor nodes such that the group of

sensor node can be combining or compress data together and broadcast only

compact data. This can diminish localized traffic in individual group and also

diminish global data. This grouping process of sensor nodes in densely

deployed large scale sensor node is known as clustering. The way of grouping

and compressing data belonging to a single cluster is called Data aggregation.

Figure 3.2Cluster based mechanism in WSN

Figure 3.2 specifies the cluster based mechanism in WSN. The

clustering procedure starts with the discovery of neighboring Sensor Nodes

(SN) by sending periodic Beacon Signals. After the creation of the clusters,

each cluster is coordinated by the CH node, which is responsible for getting

the measured values from its cluster’s nodes and then aggregate them before

sending the aggregate to the sink through other Cluster Heads (CHs).

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Here, a clustering protocol is proposed for WSN.

The cluster heads are predicted dynamically depending on the

originator node, which wishes to transmit a message. Thus the

Cluster Heads (CHs) are not static avoiding fast depletion of

their energy.

Computes a node’s implication in time linear in the number of

nodes and linear in the number of edges of the network

neighborhood of the node, irrespectively of the degree of each

node.

Allows for fast network clustering.

To maximize network lifetime in Wireless Sensor Networks

(WSNs) the paths for data transfer are elected in such a way that the total

energy consumed along the path is diminished. To support high scalability

and better data aggregation, sensor nodes are generally grouped into disjoint,

non extending subsets called clusters. Clusters create hierarchical WSNs

which incorporate efficient utilization of limited resources of sensor nodes

and thus extend utilization of limited resources of sensor nodes and thus

extending network lifetime.

After the clusters are formed, the CH broadcasts two thresholds to

all nodes in the cluster. These are hard and soft thresholds.

1. A hard threshold is a particular value of an attribute beyond

which a node can be triggered to broadcast data. Thus, the

hard threshold allows the nodes to broadcast only when the

sensed attribute is in the range of interest.

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2. A soft threshold is a small change in the value of an attribute

which can trigger a node to broadcast data again.

Once a node senses a value beyond the hard threshold, it broadcasts

data only when the value of that attributes changes an amount equal to or

greater than the soft threshold. Thus hard threshold and soft threshold

diminish the number of transmissions and save energy.

Energy usage is an important in the design of WSNs which

typically depends on portable energy sources like batteries for power. WSNs

are large scale networks of small embedded devices, exclusive with sensing,

computation and communication capabilities.

Clustering schemes offer diminished communication expense, and

efficient resource allocations thus decreasing the overall energy consumption

and reducing the interferences among sensor nodes. A large number of

clusters will overcrowd the area with small size clusters and a very small

number of clusters will exhaust the CH with large amount of messages

transmitted from cluster members.

The most outstanding benefit of clustering is that it can greatly

reduce the energy consumption of nodes and lengthen the network lifetime.

Clustering is grouping physical networks nodes into a small number of logical

assemblies and continuing them during the network operation. For the initial

construction of clusters, each node performs a cluster construction protocol. If

all clusters require a leader, nodes in each cluster should perform a leader

election protocol. The leader is called as a CH. Since a CH plays a central role

such as collecting sensed data from other nodes and transferring the collected

data to the sink, composed nodes try to become Cluster Heads (CHs). In order

to keep composed nodes from being a CH, two main strategies can be used,

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1. The compromised nodes can be identified and remove during

the initial cluster formation. If a composed node survives the

censorship process, it can efficiently obtain candidacy for

being a CH. Therefore, removing the composed nodes during

the cluster formation is the first defense line for secure

clustering.

2. The composed nodes can be kept from predicting and

manipulating results in CH election and expediting their wins

in the election. This strategy is the second defense line for

secure clustering.

In WSN sensor nodes have limited processing function,

transmission bandwidth, and repository space. This gives rise to new and

unique challenges in data management and information processing. In

network data processing techniques, such as data aggregation, multicast and

broadcast need to be developed. Network lifetime is the key characteristics

used for evaluating the performance of any sensor network. The lifetime of a

network is determined by residual energy of the system, hence main and most

extensive challenge in Wireless Sensor Network (WSN) is the efficient use of

energy resources.

3.3 LOW ENERGY ADAPTIVE CLUSTERING HIERARCHY

Low Energy Adaptive Clustering Hierarchy (LEACH) protocol

organizes the nodes into groups, so that each cluster has a cluster head for a

specific period to its own cluster. LEACH is an adaptive and self organized

and clustering protocol. During the data transmission to the sink node, the

operation of LEACH is split into rounds. Where each round accomplishes

with a setup phases of cluster formation and followed by a steady state phase.

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In Set-up Phase, it consists of two phases;

1. Advertisement Phase

2. Cluster Set-up Phase

In Steady Phase, it consists of two phases;

1. Schedule Creation

2. Data Transmission

Although LEACH is able to increase the network period, there are

still a number of concerns about the assumptions used in this protocol. Furthermore, the idea of dynamic clustering brings additional overhead, e.g.

head adjustments, advertisements etc., which may diminish the gain in power consumption. Further, the protocol considers that all nodes begin with the

same amount of energy capacity in each election round, assuming that being a

Cluster Head consumes approximately the same amount of energy for each

node. The protocol should be continued to account for uniform strength nodes, i.e., use power-based threshold.

LEACH performs a random selection of cluster heads to achieve

load balancing amid the sensor nodes. This model has some deficiencies

which are described as below:

In LEACH, a sensor node is chosen as the cluster head using

the distributed probabilistic method. This approach compensates lower message overhead, but cannot satisfy that cluster heads are

uniformly distributed over the entire network.

In LEACH, it is counterfeit that all nodes are isomorphic and

all nodes have similar amount of energy capacity in each amount of selection round.

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In order to locate the deficiencies described above, a data-

aggregation based optimal clustering-LEACH (DAO-LEACH) is proposed in

this paper. In DAO-LEACH, the residual energy of sensor nodes is examined

in cluster formation and cluster-head selection. Subsequently the non-cluster

node determines its cluster head based on the residual energy of the possible

cluster heads and the size of the cluster.

Figure 3.3 Flow chart of LEACH protocol

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Figure 3.3 illustrates the flow of LEACH protocol. The main

benefit of the job is to reduce the energy consumption and improves the

network longevity. Accordingly, DAO-LEACH is proposed here for data

ensemble based optimal clustering which results in generating energy

efficient route for data transmission between the source and the sink node.

Energy consumption is the consequence problem in wireless sensor networks

because nodes are battery operated. It is desirable to make these nodes as

cheap and energy efficient as possible. Figure 3.4 specifies the LEACH’s

hierarchical routing architecture.

Figure 3.4LEACH’s Hierarchical routing architecture

Figure 3.4 specifies the LEACH’s hierarchical routing architecture.

LEACH is completely dispersed, demanding no control information from the

base station, and the nodes do not require knowledge of the global network in

order to operate LEACH. Distributing the energy through the nodes in the

network is competent in reducing energy distraction from a global perspective

and enhancing system lifetime.

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Besides, Gaussian distribution based nodes deployment has been

performed for effective coverage of sensing area and node aggregation is

performed based on the conditional probability theorem

3.4 NETWORK DEPLOYMENT MODEL

An efficient deployment pattern associates location management

and power management. In several applications, the desired lifetime of a

sensor network is of the order of a few years. It may be inaccessible or

inadmissible to change batteries in sensor nodes once a wireless sensor

network is deployed. So, it is demanding and asserts to design long lived

sensor networks covered by the energy constraint.

Node deployment is a crucial concern to be solved in wireless

sensor network. An appropriate node deployment can diminish the complexity

of problems in WSN. Disparate node deployment models have been proposed

to reduce the complexity. Deployment of sensor nodes can be random or

fixed. In random deployment, nodes are deployed in a random aspect. In fixed

deployment, address of the nodes is specified. Moreover, it can extend the

period of WSNs by minimizing the energy consumption.

Here, there are three node deployment models for a sensor network

are considered,

A Uniform random deployment

A Square grid deployment

A Tri-Hexagon Tiling (THT) deployment

Since the preference of performance metrics differs in application

specific WSNs, it is beneficial to investigate a set of them. Three performance

metrics are inspected.

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Coverage

Energy consumption

Worst case delay

Consider a WSN in a 2D plane with N sensor nodes, which are

deployed on the environment by 2D Gaussian distribution (i.e., Normal

Distribution). It is given by Equation (3.1),

( , )f a b 12 a b

2 2

2 22 2i i

a b

a a b b

e (3.1)

where ,i ia b is the deployment point whereas a and b are the standard

deviation for a and b dimensions respectively. Then, the deployment point is

considered at the central point of the disk i.e., ia = ib = 0. The Gaussian

distribution (GC) is given as,

2 2

2 22 21,2

a b

a b

a b

f a b e (3.2)

The traffic pattern accepted is that each node senses its data and the

BS is responsible for gathering data from sensors, periodically. In this part,

the coverage probability has also been derived with respect to the Gaussian

distribution ,a b . The coverage probability with respect to Gaussian

distribution a b is derived. The two dimensions a and b are independent

and submitted with the same standardization of Gaussian distribution

a b . Concurrently the deployment points of both dimensions are the

center point of the disk A, termed as, O (0, 0). To be distinct, the probability

density functions of a and b will be given as follows:

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2

221( )2

a

f a e (3.3)

2

221(b)2

b

f e (3.4)

2 2

222

1(a,b) ( ) ( )2

a b

f f a f b e (3.5)

In the above equation 2 2a b is the square of distances from the

point (a, b) to the center point. By solving Equation (3.5), it is obtained that

any two points in the disk having the same distance’s’ to the center point have

the same deployment probability.

3.5 CLUSTER FORMATION

In wireless sensor networks, clustering sensor nodes into smaller

groups is an effective technique to carry out scalability, self-organization,

power saving, channel access and routing, etc. A wireless sensor network

naturally subsists of a potentially large number of resource constrained sensor

nodes and a few relatively powerful control nodes. Each sensor node is

battery powered, and has a low-end processor, a limited amount of memory

and a low power communication module capable of short-range wireless

communication.

Sensor nodes typically use irreplaceable power with the limited

capacity, the node’s capacity of computing, communicating, and storage is

very limited, which requires WSN protocols need to conserve energy as the

main objective of maximizing the network lifetime. An energy efficient

communication protocol LEACH, which employs a hierarchical clustering

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done based on information received by the BS. The BS periodically changes

both the cluster membership and the CH to conserve energy.

The CH collects and aggregates information from sensors in its

own cluster and passes on information to the BS. By rotating the CH

randomly, energy consumption is expected to be uniformly distributed.

However, LEACH possibly chooses too many CHs at a time or randomly

elects the CHs far away from the BS without considering the node’s residual

energy. As a result, some CHs drain their energy early thus reducing the

lifespan of WSN.

The main target of hierarchical routing or cluster based routing is to

efficiently maintain the energy usage of sensor nodes by involving them in

multi-hop communication within a particular cluster. Cluster formation is

commonly based on the energy reserve of sensors and sensors proximity to

the CHs.

Clustering plays an essential role for energy saving in WSNs. With

clustering in WSNs, energy consumption, lifetime of the network and

scalability can be improved. Because only CH node per cluster is required to

perform routing task and the other sensor nodes just forward their data to CH.

Clustering has important applications in high-density sensor

networks, because it is much accessible to manage a set of cluster

representatives (CH) from each cluster than to manage whole sensor nodes.

In WSNs the sensor nodes are resource constrained which means

they have defined energy, broadcast power, memory, and computational

facility. Energy consumed by the sensor nodes for connecting data from

sensor nodes to the BS is the decisive cause of energy reduction in sensor

nodes.

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In large sensor networks, the sensor nodes can be grouped into

small clusters by their physical adjacency to achieve better efficiency, and

each cluster may select a cluster-head to coordinate the nodes in the cluster.

Most of the adequate cluster based protocols have been developed for sensor

networks to obtain scalability, energy saving, channel access and routing, etc.

A randomly expanded sensor network requires a cluster formation

protocol to partition the network into clusters. When cluster heads are

required, nodes in each cluster may also perform a leader election protocol to

determine their cluster head.

Figure 3.5 Cluster formation

Figure 3.5 specifies the cluster formation. Cluster formation in

wireless sensor networks is based on the time duration for receiving the

adjacent nodes message and the residual energy EnergyR of the adjacent node.

Thus, the clustering protocol is divided into rounds where each round is

triggered to find the optimal cluster heads for each sensor nodes in the

network. Let it be assumed that the sensor nodes exchange beacon messages

with its neighbor which composed the list of neighbors and its residual

energy. It is also defined that two nodes do not transmit data at the same time

slot in order to reduce the interference.

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3.5.1 Clustering Objectives

Generally the clustering objective is set in order to promote the

applications requirements. For example if the application is delicate to data

latency, intra and inter cluster connectivity and the length of the data routing

paths are consistently treated as precedent for CH selection and node

grouping.

3.5.1.1 Load balancing

Even handling of sensors between the clusters is consistently an

objective for setups where CH performs data processing or important intra-

cluster management commitments. When CH do data aggregation, it is

essential to have identical number of nodes in the clusters so that the

connected data report becomes ready approximately at the same time for more

processing at the BS or at the next tier in the network.

3.5.1.2 Fault tolerance

In frequent applications, WSNs will be operational in hard

environments and thus nodes are commonly disclosed to the improved risk of

malfunction and physical damage. Tolerating the deficiency of CHs is

frequently crucial in such applications in order to avert the loss of important

sensor’s data. The most inductive way to reclaim from a CHs failure is to

re-cluster the network. Re-clustering is not only a resource concern on the

nodes, but also a very troublesome to the on-going operation. Therefore,

instant fault tolerance methods would be more convenient for that account.

Authorizing back up CHs is the most eminent scheme sought in the literature

for recovery from a CH failure.

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3.5.1.3 Increased connectivity and reduced delay

Excepting that CHs have very long-haul communication

competence, e.g. a satellite link, inter-CH connectedness is an essential

requirement in most of the applications. This is especially accurate when CHs

are taken from the sensors population. The affinity goal can be just limited to

ensure the opportunity of a path from every CH to the BS or be more

prohibitive by commanding a bound on the length of the path.

When some of the sensors suspect the CH role, the connectivity

equitable makes network clustering one of the many alternatives of the

connected domineering set problem.

3.5.1.4 Minimal cluster count

This objective is regularly common when CHs are especially as

designed resource-rich nodes. The constraint can be expected to the

complexity of deploying these types of nodes. Additionally, the size of these

nodes tends to be much larger than sensors, which makes them easily

detectable. Node visibility is highly inadmissible in many WSNs applications.

3.5.1.5 Maximal network longevity

As sensor nodes are energy-constrained, the network’s lifetime is a

major thing, especially for applications of WSNs in hard environments. When

CHs are richer in resources than sensors, it is critical to minimize the energy

for intra-cluster communication. On the other hand, when CHs are regular

sensors, their lifetime can be continued by limiting their load. Mixed

clustering and route setup has also been considered for maximizing network’s

lifetime. Adaptive clustering is also a feasible choice for achieving network

longevity.

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The time duration for cluster formation procedure is taken as TCF,

which has triggered every network operation time duration and duration of

cluster formation termed as rounds for selecting new cluster heads. Since

WSN depends on multi-hop hierarchical network architecture, the hop

distance and the hierarchy level plays a vital role in the cluster formation.

The cluster formation procedure comprises four stages.

Stage 1

Stage 1 operation involves information gathering about the

neighbor nodes by broadcasting the beacon messages. Then, the respective

nodes collect reply messages from the neighboring sensor nodes for the

broadcast of beacon messages.

Stage 2

In stage 2, a sorting algorithm is executed to obtain the list of

neighbor nodes regarding its hop distance. The list of neighbor nodes is

enacted in descending order

Stage 3

When its two- hop neighbor node is not enclosed, all the members

of stage 2 are analyzed one-by-one and any one two-hop neighbor will be

crowned for being as a candidate for the cluster.

Stage 4

Stage 4 handles the execution of sorting algorithm based on the

residual energy of the neighbor nodes. Each round of cluster formation

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procedure operates in all the four stages for effective clustering to provide

better communication with the sensor nodes and the data ensemble.

3.6 OPTIMAL CLUSTER HEAD SELECTION

Clustering is one of the important methods for prolonging the

network period in WSN. It comprises grouping of sensor nodes into clusters

and electing CHs for all the clusters. CHs collect all the data from

corresponding cluster’s nodes and forward the accumulated data to base

station.

The election of Cluster Head node in LEACH has some

deficiencies such as,

Some enormous clusters and very limited clusters may exist in

the network at the same time.

Unreasonable CH election while the nodes have different

energy.

Cluster member nodes diminish energy after the CH was dead.

Ignores residual energy, geographic location and other

information, which may easily lead to the failure of the CH

node.

In LEACH, CH role is rotated among all sensor nodes by re-

clustering the network after specific number of data gathering cycles are

called round. During each round, a fixed percentage of total network nodes

are elected as CHs which then start cluster formation process by advertising

their election to the non CH nodes which on receipt of these equal transmit

power advertises, from different CHs, join one with the highest received

signal strength.

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While improving the limitations of LEACH, many clustering

proposals for increasing network lifetime are reported suggesting different

strategies of CH election and its role rotating among the sensor nodes. These

strategies of CH election may broadly be categorized as deterministic,

adaptive and combined metric (hybrid).

In LEACH, during some round, it is possible that none of the nodes

elects itself as CH and all the nodes have to act as forced CHs. Figure 3.6

specifies the LEACH’s clustering communication hierarchy for WSNs

Figure 3.6 The LEACH clustering communication hierarchy for WSNs

3.6.1 Energy Loss in CH Selection

Sensors are usually classified into different types of networks

depending upon topology, order of data traversal, routing methods etc. It can

be classified as clustered or un-clustered. In the event of without clusters,

sensed data can be relayed in a single hop or multi-hop fashion to the base

station or data sink. In cluster based sensor networks, sensors can broadcast

their sensed data to the appointed or elected CH of a given cluster. Data to the

CHs can be transmitted in a single hop and multi-hop fashion.

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CH can be special kind of sensors with higher energy, processing or

memory. In such cases, a central control algorithm run by the BS can select

the special sensor to serve as CH in a given round or it can be elected by

sensors in the cluster itself.

CHs can be ordinary sensors given additional duty of a CH due to

various election parameters such as distance, energy, centrality, etc, elected

either by BS or sensors in the cluster itself.

Sensors in cluster-based sensor network can be of fixed or dynamic

nature. Fixed cluster sensor network is composed of sensors which are

associated with a single cluster permanently from the moment they are

deployed till the time they run out of energy. In the case of dynamic cluster

based sensor network, the sensors change their cluster depending on the

parameter on which it is pre programmed. Some of the parameters are length,

intensity, proximity to the data sink and size of the cluster, etc.

Let it be assumed that the intra-cluster communication section is

long enough, so that all member nodes of a cluster having data can send to

their respective CH and all CHs having data can send to the sink node. The

CH performs data aggregation before transmitting the data to the sink node.

Energy consumption of the cluster heads is reasonably expensive.

So the residual energy of sensor node is the substantial criteria for the election

of cluster head. In addition, data ensemble can save considerable energy while

the source nodes forming one cluster are deployed in a relatively small area

when the sink node is far away from the source nodes, because sensor nodes

requires a little energy for transmitting data to the cluster head instead of

sending data directly to the sink. Hence, it is logical to infer that the nearer

source nodes within a cluster, the lesser energy they consume to send data.

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LEACH is a cluster based protocol. LEACH randomly decides a

few sensor nodes as CHs and rotates this role to evenly distribute the energy

load among the sensors in the network. In LEACH, the CH nodes compress

data arriving from nodes which belong to the corresponding cluster and send

an aggregated packet to the base station in order to reduce the amount of

information which must be transmitted to the base station. WSN is considered

to be a dynamic clustering method.

Figure 3.7 Cluster formations in LEACH

When LEACH organizes a cluster, it can either design uniformly a

cluster (good-case scenario) or not (bad-case scenario). In LEACH, as a local

cluster is formulated by the selected Cluster Head, location of CHs affects the

number of member nodes in a local cluster. Figure 3.7 specifies the

formations of cluster in LEACH.

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If there are many member nodes in a local cluster, the energy

consuming of a Cluster Head is increased. Furthermore, if there are little

member nodes in local cluster, the energy consumption of a Cluster Head is

diminished. That is, that the energy consumption of Cluster Head is affected

by the number of member nodes. As a result, in LEACH, it is hard to keep up

the balance of node energy of the whole sensor networks.

In LEACH, all member nodes deliver sensing data precisely to a

Cluster Head or the sink node because LEACH assumes the transmit power

control. However, a sensor is convenient for communicating the node with

outside sensing range based on multi-hop routing method because the node’s

communication is limited.

LEACH-C (LEACH-Centralized) is identical to LEACH. It means

that two algorithms are the same to data transmission process among the BS

and the sensor nodes. Furthermore, the process of Cluster Head selection in

LEACH-C is different with LEACH. LEACH-C uses a central control

algorithm to form the clusters which may produce better clusters by

dispersing the CH nodes through the network.

During the setup phase of LEACH-C, all nodes send information

about their current location and energy level to the sink node. A sink

computes the average energy level of all nodes through the received message,

and then give the right which is not possible for the Cluster Heads if the

sensor node have lower energy than the average energy level.

Using the remaining nodes as possible CHs, the BS finds clusters

using the simulated annealing algorithm to solve the NP-hard problem of

finding optimal clusters. This algorithm experiments to minimize the amount

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of energy for the non-cluster head nodes to transmit their data to the CH, by

minimizing the total sum of squared distance between all the non-cluster head

nodes and the closest CH.

After the CHs are elected, a member of nodes can select the Cluster

Head to which they can communicate with minimum energy consumption. A

cluster is organized by the node transmitting the message as a determined CH

node. After clustering, The CH performs TDMA scheduling, transmit the

schedule to member nodes in local clusters, and later start the data

transmission time. The solid point of LEACH-C is that it can equally

distribute waste to energy among the sensor nodes by positioning CHs into

the center of cluster.

Energy Efficiency is one of the crucial issues and designing power-

efficient protocols is critical for delaying the lifetime. A cluster is responsible

for transmitting any information gathered by the nodes in its cluster and may

corporate and compress the data before transmitting it to the sink. In spite of

this, the added responsibility results in a higher rate of energy drain at the

CHs. LEACH addresses this by probabilistically rotating the role of the

Cluster Head among all nodes.

The cluster sector is a local area assigned by user’s requirement. It

is composed of a cluster head node and member nodes. A CH is for

assembling the sensed data by the member nodes. The number of sensing data

in the hierarchical routing is lower as CH works. Thus, the hierarchical

routing is more energy efficient routing method than the flat routing.

Figure 3.8 specifies the cluster based WSN architecture.

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Figure 3.8 Cluster based WSN architecture

A process of clustering is as follows. A sink node selects CHs

between all distributed sensor nodes. Exclusive Cluster Head makes a local

cluster by using advertisement message. Member nodes send sensing data to

the own Cluster Head. A CH collects sensing data from member nodes for

Data Aggregation that precludes duplicate data. When a sink node demands

user demand, in response to user demand, a CH prevents unneeded query

flooding. In order to communicate with sensor nodes which are outside

sensing range, a sensor node is convenient for multi-hop networking.

It is important to measure the number of cluster member nodes in a

local cluster based on multi-hop clustering. If there are many member nodes

in a local cluster, the energy consumption in a local cluster is improved. The

energy drain of a Cluster Head is also increased. Furthermore, if there are

little member nodes in a local cluster, the energy consumption is low. The

energy drain of a cluster head is also low. So, it is important to know how

many member nodes are needed to set up a local cluster for energy efficient

sensor networks. Figure 3.9 indicates the selection process of CH.

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Figure 3.9 Cluster head selection process

3.6.1.1 Cluster set-up phase

In set-up phase, the CH is elected and then it forms a group. After

some time, the corresponding Cluster Head’s (CH) energy is reduced and to

the CH selection process is done in rotation based on the energy. Some nodes

with more residual energy turn into CHs and send CH information to inform

alternate nodes. The alternate nodes with less residual energy send

information about joining cluster to a CH.

3.6.1.3 Cluster steady phase

In cluster steady phase clusters are created and the corresponding

CH is elected. After the CH receives the data it can be aggregated and the data

can be transmitted to the base station. During the set up phase, each sensor

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node sends information about its current location to the BS. In order to

determine good clusters, the BS needs to ensure that the energy load is evenly

distributed among all the sensor nodes. Figure 3.10 exemplifies the phases of

LEACH.

Figure 3.10 Phases of LEACH

A sensor node sends its energy level to the BS. The BS computes

average node energy and determines which node has high energy. The nodes

having higher energy compared to average energy are chosen as Cluster Head

for the current round.

After that an advertisement broadcasts message to the rest of the

nodes. The non CH nodes must keep their receiver on during the phase of set

up to hear the advertisements of all the CH nodes. After this phase is

complete, each non-CH node decides the cluster to which it will belong for

this round. This determination is based on the received signal strength of the

advertisement.

With respect to the above deduction, an election weight is

determined by taking account of the concentration degree of sensor nodes and

their residual energy for optimal cluster-head selection. Let WSN of N nodes

be considered as {1, 2… N}. ( ) is termed to be the concentration degree

of node i, (i.e.) the number of sensors that can sense the environment during

rth round.

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W (k, r) is given as the election weight of k in rth round,

11

(3.6)

= ( )

( ) (3.7)

( )1

( )

( )( , ) (1 )r rEnergy krEnergy k

R D kw k rNRc

(3.8)

where C is the number of clusters, REnergy(K) is the initial energy of the node k,

( ) is the average residual energy in rth round. is stated as the

adaptive factor to fiddle with the impact of concentration degree and residual

Energy of node k in round r.

With the reduction of REnergy, will steadily increases to adapt to the

declination of the number of effectual sensor nodes in WSN.

Moreover, it is vital to evaluate the optimal probability for a sensor

node to become a cluster head. In order to determine that, The following

terms may be considered

dMH is termed as the average distance between the cluster member

and cluster head. E0 is given as the energy required by a sensor for data

transmission, M is the deployed area and dHS is represented as the distance

between the cluster head and the sink node. With these considerations, the

equations presented below are framed.

max bmax 2 20 0 ( , ) ,a

MHd a b a b da db (3.9)

when the distance of a significant group of nodes to the sink is greater than d0

then,

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02opHS

N MS dd

(3.10)

Thus, the optimal probability of a sensor node to become a CH, Pop,

is computed as follows,

opop

SP

N (3.11)

It is also stated that if the clusters are not formed in an optimal way,

the total energy consumption of the sensor network per round is increased

considerably either when the number of clusters which are created is larger or

particularly when the number of the clusters which are formed is less than the

optimal number of clusters. Figure 3.11 illustrates the overall flow of the

proposed model.

Figure 3.11 Overall flow of the proposed model

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3.7 NODE AGGREGATION VIA DATA ENSEMBLE

In WSNs, in-network aggregation is the process of compressing

locally the data gathered by the sensor nodes, so that only the shortened data

travel across several hops to their destination. The problem of aggregating

data generated by sporadic events in random locations of the monitored area

is located.

The main intention of Data Aggregation is to collect and aggregate

data in an energy efficient manner so that network life time is enlarged. Data

gathering is determined as the systematic way of sensed data from multiple

sensors to be ultimately transmitted to the base station for processing. In

consideration of the sensor nodes are energy constrained, it is incompetent for

all the sensors to address the data directly to the BS.

Sensor nodes are energy constrained. It is incompetent for all the

sensors to address the data directly to the BS. Data developed from

neighboring sensors is generally redundant and highly correlated. In addition,

the amount of data developed in large sensor networks is consistently enormous

for the BS to process. In order to solve these problems the Data Aggregation can

be used. Figure 3.12 depicts the taxonomy of Data Aggregation.

Figure 3.12 Taxonomy of data aggregation

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Data Aggregation usually associates the fusion of data from

multiple sensors at intermediate nodes and transmission of the aggregated

data to the BS. Data Aggregation can exclude redundancy, minimize the

number of transmissions. Data generated from adjacent sensors is often

redundant and highly correlated.

Data aggregation experiments to gather the most critical data from

the sensors and make it convenient to the sink in an energy efficient manner

with minimum data latency. Data aggregation is essential in frequent

applications such as environment monitoring where the freshness of data is

also an important factor.

It is demanding to develop energy efficient data aggregation

algorithms so that network period is enlarged. There are several factors which

resolve the energy efficiency of sensor network architecture, the data

aggregation system and the underlying protocol.

In Addition, the amount of data generated in large sensor networks

is usually enormous for the BS to process. Hence, methods are needed for

combining data into high-quality information at the sensors or intermediate

nodes which can reduce the number of packets transmitted to the BS resulting

in conservation of energy and bandwidth. This can be polished by data

aggregation.

Data Aggregation is defined as the process of aggregating the data

from multiple sensors to eliminate redundant transmission and provide fused

information to the BS. Data aggregation usually involves the fusion of data

from multiple sensors at intermediate nodes and transmission of the

aggregated data to the BS. It can be concluded that data gathering is to collect

the data from the neighbor node to be sent to sink and Data aggregation is the

process of removing redundancy among them.

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In energy-constrained sensor networks of large size, it is inefficient

for sensors to broadcast the data directly to the sink. In such scenarios,

sensors can broadcast data to a local aggregator or CH which aggregates data

from all the sensors in its cluster and broadcasts the concise digest to the sink.

This results in significant energy savings for the energy constrained sensors.

In the Data aggregation of WSN, two security requirements,

confidentiality and integrity should be fulfilled. Specifically, the fundamental

security issue is Data confidentiality, which conserves the sensitive

transmitted data from static attacks, such as eavesdropping.

Data aggregation process is performed by specific routing protocol.

The intention here is aggregating data to minimize the energy expenditure. So

sensor nodes should route packets based on the data packet content and

choose the next hop in order to promote in network aggregation.

In order to save resources and energy, data must be aggregated to

avoid the overwhelming amount of traffic in the network. Figure 3.13

demonstrates the model of data aggregation and node aggregation.

Figure 3.13 Data aggregation model and node aggregation model

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There has been extensive work on Data aggregation schemes in

sensor networks. The aim of Data aggregation is to eliminate redundant data

transmission and enhance the lifetime of energy in WSN. Data aggregation is

the process of one or several sensors collecting the detection result from other

sensor. The collected data must be processed by a sensor to reduce the

transmission. It can be BS or sometimes an external user who has permission

to interact with the network. Data transmission among sensor nodes,

aggregators and the queried consumes a lot of energy in WSN.

3.7.5 Energy Efficiency

The performance of the sensor network should be prolonged as

long as possible. In an optimal Data Aggregation scheme, each sensor should

have increased the same amount of energy in each data collecting round.

A data aggregation pattern is energy efficient if it maximizes the performance

of the network. If it is suspected that all sensors are equally essential, the

energy consumption of each sensor should be reduced. This concept is taken

by the network lifetime which computes the energy efficient of the network.

3.7.6 Network Lifetime

Network lifetime may be the essential metric for the evaluation of

sensor networks. In a resource constrained environment, the utilization of

every finite resource must be embarrassed. Network lifetime as a part of

energy consumption employs the rare position wherein it forms an upper

bound for the utility of the sensor network. Network lifetime actively depends

on the lifetime of the single nodes which aggregates the network. Energy

efficiency and Network lifetime are compatible in which increasing the

energy efficiency enlarges the lifetime of the network.

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3.7.7 Latency

Latency is described as the delay convoluted in data aggregation. It

can be deliberated as the time delay among the data packets received at the

sink and the data generated at the source nodes.

In this operation, a cluster of nodes in a WSN is replaced with a

single node without altering the underlying joint deployment of the network.

While aggregating the nodes, data ensemble also takes place. It is needed to

find a macro node which is capable of aggregation. However, the process

incorporates two steps: Path definition and Pair of Combinable nodes.

Following, conditional probability has been applied for adept node

aggregation process. The conditional probability of the macro node should be

equal to the product of all component nodes conditional probabilities. It is

explained here with an example (Figure 3.14).

Figure 3.14 Sample network

If the nodes B, C and D are combined into a macro node M, then

the conditional probability of M |A (A- predecessor) is equal to:

P (M|A) = P (B, C, D|A) = P (B|A) P (C|A) P (D|B, C) (3.12)

It is also stated from the above figure that the conditional probability

of a macro node's successor is equivalent to the conditional probability of the

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successor given that all the component sensor nodes are in the macro node,

except the nodes which are not linked directly to the successor node. Here, E

is the successor and the above statement is given as,

P (E|M) = P (E|B, C, D) (3.13)

By aggregating the sensor nodes using conditional probability

theorem, the data has also been aggregated and packed for transmission

through an efficient path to the sensor node.

3.7.8 Energy Efficient Routing

A clustering based protocol LEACH utilizes randomized rotation of

local cluster base stations (Cluster-Head) to evenly distribute the energy.

LEACH protocol considers that all the nodes are homogeneous and

they can transmit with enough power to reach the base station and also each

node possesses enough computational power. It is also considered that the BS

is fixed and the nodes observation is correlated.

The main idea of LEACH resides in forming clusters of sensor

nodes based on the incoming signal strength and then local CHs are used as

routers to the sink. The energy saving phenomenon is achieved by employing

transmissions by those clusters alone rather than sensor nodes. One of the

interesting features of LEACH has the flexibility of randomly changing the

CHs.

Balancing the dissipation of energy from nodes with respect to time

through this scheme also makes LEACH an important approach. The sensor

nodes elect themselves to be CHs at any given time with a certain probability.

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At each interval the decision whether a node is to be elevated to CH is made

dynamically and solely by each node independent of other nodes to minimize

overhead in CH establishment.

With the accomplishment of all process explained above, an energy

efficient route has been obtained to transmit the aggregated data from the

source to sink. This decision on CH selection is a function of the percentage

of optimal CHs in network.

Threshold sensitive Energy Efficient sensor Network (TEEN) is a

cluster based routing protocol based on LEACH which improves it at the

same time by transferring the data less frequently. The network is treated as

collection of simple nodes namely, first-level CHs and second-level CHs.

LEACH strategy is used in this protocol for cluster formation. After building

the clusters, the CH broadcasts two thresholds namely hard and soft

thresholds to all the nodes, which are the key feature of Threshold sensitive

Energy Efficient sensor Network (TEEN).

Hard threshold is the minimum threshold used to trigger a sensor

node to switch on its transmitter and therefore transmit to the CH. Thus, the

hard threshold will ask the sensor node to perform transmission only when the

sensed attribute is in the required range and reduces the number of

transmissions significantly.

Once a node sense a value at or beyond the hard threshold, the data

is addressed only when the attributes is changed by an amount greater than or

equal to the threshold. That is, soft threshold reduces the number of frequent

transmissions even after the hard threshold is crossed if there is no change or

little change in the value of sensed attribute compared to soft threshold.

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Figure 3.15 Energy efficient routing

The Figure 3.15 presented above shows the energy efficient routing

for adequate communication of nodes having high residual energy. Here, the

nodes deployment has been achieved by the Gaussian distribution, by which

the process cannot be affected with high mobility of sensors. Data aggregation

based optimal clustering supports in reducing energy dissipation of nodes,

thereby decreasing the energy consumption and prolonging the WSN lifespan.

3.8 RESULTS AND DISCUSSIONS

Various parameters have been taken into account to show that the

proposed system yields better results when compared with the existing

architecture.

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Figure 3.16 Throughput difference between the leach and DAO-leach

In this evaluation phase, the simulation network size is taken as

100x100m, in which 100 nodes have been deployed in a random distribution

and the Base Station’s position is placed as co-ordinates 100, 45. The initial

energy is initialized as 2 Joules. From the Figure 3.16 it is known that the

proposed approach DAO-Leach shows higher throughput rate when compared

with the existing Leach. The Simulation Time in X axis and the unit in ms is

taken and Throughput in Y axis is taken which is having the unit in Mbps.

The proposed approach yields the effective improvement in the Message

Delivery.

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Table 3.1 Performance analysis – throughput

Simulation Time (ms)Throughput % of

variation LEACH DAO-LEACH

80 2 2.8 29%

260 2.8 3.4 17.65%

510 3.2 3.9 17.95%

1050 3.4 5 32%

Table 3.1 represents the percentage of variation in throughput

between LEACH and DAO-LEACH relative to simulation time.

Figure 3.17 Graph between variance of energy (nJ) with the simulation time (ms)

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The proposed mechanism is an energy efficient mechanism for the

proof. Figure 3.17 depicts that proposed DAO-LEACH methodology provides

the energy efficiency over the former methodology LEACH method. This

provides a proof of the energy efficiency of the proposed work. The

simulation time is taken in X axis which is having the unit ms and Variance of

Energy is taken in Y axis which is having the unit nJ.

Table 3.2 Performance analysis – variance of energy

Simulation Time (ms)Variance of energy % of

differenceLEACH DAO-LEACH

10 0.0037 0.0056 33.93%

20 0.0046 0.006 23.33%

30 0.0049 0.0073 32.877%

40 0.0050 0.0081 38.271%

50 0.0065 0.0094 30.86%

Table 3.2 represents the percentage of difference in variance of

energy between LEACH and DAO-LEACH corresponding to simulation

time.

The proposed approach uses energy efficient cluster selection

mechanism which provides the efficient routing process. By this process the

data transmission is taking place with the help of cluster heads. From the

Figure3.18 it is known that the proposed methodology DAO-LEACH is

consuming lesser energy when compared with the existing approach LEACH

process.

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Figure 3.18 Average energy utilization (nJ) vs. simulation time (ms)

Table 3.3 represents the percentage of difference in average energy

utilization between LEACH and DAO-LEACH corresponding to simulation

time.

Table 3.3 Performance analysis – average energy utilization

Simulation Time (ms)Avg. Energy Utilization % of

differenceLEACH DAO-LEACH

10 274 297 7.75%

20 274.6 297.3 7.64%

30 274.3 296.4 7.46%

40 273.6 295.7 7.47%

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50 272.8 294.8 7.463%

60 280 303.6 7.774%

Figure 3.19 Simulation time (ms) vs. end to end delay

In the Figure 3.19, the graph is between the Simulation Time (ms)

with the End to End delays. The End to End delay can be stated as the average

time delay to transmit the packets to sink or to the base station. In the

proposed approach DAO-LEACH, the End-to-End delay is significantly

reduced when compared with the existing protocol LEACH. In the above

graph the green values represent the existing LEACH protocol and red values

represent the DAO-LEACH protocol.

Table 3.4 represents the percentage of difference in end to end

delay between LEACH and DAO-LEACH corresponding to simulation time.

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The initial decrease in percentage difference is due to the initial lower traffic

tolerance during the transmission of packets.

Table 3.4 Performance analysis-end to end delay relative to simulation time

Simulation Time (ms)End to End delay % of

difference LEACH DAO-LEACH

10 0.21 0.162 22.86%

20 0.207 0.165 20.29%

30 0.212 0.166 21.70%

40 0.257 0.168 34.64%

50 0.296 0.171 42.23%

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Figure 3.20 Simulation time (ms) vs. PDR (%)

From above Figure 3.20 it is known that the Packet Delivery Ratio

(PDR) in the proposed approach is higher than the existing approach. The

PDR can be defined as the ratio of the number of delivered data packet to the

destination.

Table 3.5 Performance analysis-PDR

Simulation Time (ms)PDR (%) % of

difference LEACH DAO-LEACH

10 97 98 1.0205%

20 96.7 97.85 1.176%

30 96 97.45 1.488%

40 95.5 97.05 1.598%

50 95 96.7 1.759%

The proposed approach DAO-LEACH yields better PDR when

compare to the former approach LEACH. From the above graph it is known

that as the simulation time (ms) increases the PDR (%) decreases. The

simulation result provides that the proposed DAO-LEACH the PDR up to

30% approximately when compared with the existing LEACH protocol.

Table 3.5 represents the percentage of difference in PDR between

LEACH and DAO-LEACH corresponding to simulation time

3.9 CONCLUSION

Since a decade, WSN has been envisioned to support numerous

monitoring applications, in which energy efficient routing is much

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consequential to enhance the lifetime and stability of the system. Focusing

that, DAO-LEACH has been proposed in this paper for determining efficient

route for communication and data transmission among the nodes. Gaussian

distribution is adopted for node deployment which is highly adaptive for node

mobility in WSN. Moreover, data aggregation has been performed with the

conditional probability based node aggregation method, where the data

ensemble has been attained effectively. Also optimal clustering and cluster

head selection procedures are incorporated by which the energy dissipation of

nodes can be reduced considerably. Finally, an energy efficient route is

obtained for communicating the source and the sink node which increases the

longevity of the network.