Implementation of energy efficient coverage aware routing protocol for wireless sensor network using genetic algorithm

International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.1, January 2015
DOI:10.5121/ijfcst.2015.5103 23
IMPLEMENTATION OF ENERGY EFFICIENT
COVERAGE AWARE ROUTING PROTOCOL FOR
WIRELESS SENSOR NETWORK USING GENETIC
ALGORITHM
Khushbu Babbar1
, Kusum Lata Jain2
and G.N. Purohit3
Department of Computer Engineering, Banasthali University, Jaipur, Rajasthan
ABSTRACT
In recent years, wireless sensor network have been used in many application such as disaster reservation,
agriculture, environmental observation and forecasting .Coverage preservation and energy consumption
are two most important issues in wireless sensor networks. To increase the network lifetime, we propose an
energy efficient coverage aware routing protocol for wireless sensor network for randomly deployed sensor
nodes. Some of the routing protocol is based on energy efficiency and some are based on coverage aware.
The proposed routing protocol is based on both the issues i.e. coverage and energy, in which we first find
the k-mean i.e. the degree of coverage, so that we can use this in the selection of cluster heads in wireless
sensor network by using Genetic Algorithm for increasing network lifetime and coverage. For cluster head
selection each node evaluates its k-mean and energy by internal function which used as fitness function in
genetic algorithm. The proposed algorithm “Implementation of energy efficient coverage aware routing
protocol for Wireless Sensor Network” is designed for homogeneous wireless sensor network. Simulations
results show that proposed algorithm increases the network lifetime by reduce the energy consumption and
preserve coverage. Simulation is done with MATLAB and a comparison of algorithm with benchmark
algorithms is also performed.
KEYWORDS
Wireless Sensor Network, Genetic Algorithm, Coverage, Single hop Communication, K-mean, Cluster.
1. INTRODUCTION
Wireless sensor network (WSN) has emerged as an important area for research and development.
Wireless sensor networks are found suitable for applications such a surveillance, agriculture,
smart homes, traffic management, disaster detection etc. The key constraints in the development
of WSNs are cost, limited energy, limited memory, limited computational capability, and the
memory size of the sensor nodes. Many routing protocols for WSNs have been developed. The
major routing challenges and design issues that affect routing process in WSN are whether node
deployment is defined or not, Energy consumption of node without losing accuracy of data, Data
Reporting Model of node, Node Heterogeneity, Fault Tolerance, network support for Scalability,
Network Dynamics, Transmission Media, Connectivity, Coverage, area of sensor network, Data
Aggregation of repeated data packets, Quality of Service in data delivery. There are many aspects
which affect the routing protocol design we consider only the limited computing power of nodes.
Rather there are number of approaches for the WSN, the research is based on the Hierarchical
approach. A critical aspect of applications in WSN is network lifetime. WSN are usable as long
as they can communicate sensed data to processed node. Sensing and communication are
important activities and they consume energy so power management and coverage preservation

24
can effectively increase the network lifetime. In some protocols sensor nodes transmit their
sensed data directly to a BS. Thus, the nodes located far from the BS will die quickly since they
dissipate more energy in transmitting data packets. Clustering is used as a tool for the energy
efficient coverage aware routing protocol, and in this protocol network is organized in clusters.
Such a sensor network contains two types of nodes; cluster head and cluster members. All the
data processing such as data fusion and aggregation are local to the cluster. Cluster heads changes
over time in order to balance the energy dissipation of nodes. Cluster member sense the data and
send it to cluster head and cluster head sends the data to BS. Many protocols are made which is
based on energy efficiency and some are based on coverage. Thus, there is a need of a new
protocol scheme, which is based on both energy efficiency and coverage to increase the network
lifetime. In this paper, we analyze energy efficient coverage aware routing protocol for
homogeneous wireless sensor network in which genetic algorithm used for cluster head selection
for WSN. We first describe the protocol and then we provide simulation results in MATLAB and
determine performance analysis of given protocol compared with benchmark clustering algorithm
LEACH.
The paper is organized as follows. In the Section -2, LEACH protocol and its limitations are
discussed, Section -3 summarizes Genetic Algorithm, Section-4 summarizes network parameter
Description of the proposed algorithm is included in Section – 5 and Section6 and 7 includes the
network preliminaries and Section-8 includes the simulation results and the final discussion is
concluded in Section-9.
2. LEACH PROTOCOL
The LEACH protocol is one of the bench-mark protocols in the Wireless Sensor Networks.
LEACH protocol has two phases: Setup phase and Steady state phase. In the setup phase, nodes
are organized into clusters and in the steady state phase data is transferred from cluster head to
sink via single-hop method. In each cluster a node is selected as a cluster head and the remaining
nodes are called cluster-members. Data collected by the member nodes, locally processed by the
cluster head. Due to local data processing and data transmission from cluster head to base station,
cluster heads consume more energy than energy consumed by member nodes. Our paper has
highlighted the practical implementation of protocol in MATLAB and determines network
lifetime.
2.1. Limitations
In LEACH location of the cluster head is on random basis. This is also true for the number of
cluster member in a cluster. An optimum solution for both of above mentioned issues is always
required. Establish control over location of cluster heads and size of the clusters in terms of
number of members always has been considered as a challenge and solving this problem requires
efficient clustering algorithms in energy consumption and network load balancing. In the
following section, a new approach is suggested for the optimal selection of cluster head using
genetic algorithm. The approach is based in the density of nodes in the network. Following
figures express the effect of density in nodes organization. Figure 1(a), shows the nodes
organization just according to nodes energy and regardless of density that is not optimal in
number of nodes. Figure (b), express the nodes organization according to nodes density that is
more optimum location for the cluster head.
3. GENETIC ALGORITHM
Genetic algorithms are randomized search and optimization techniques guided by the principles
of evolution and natural genetics. GAs perform search in complex, large landscapes, and provide

25
near-optimal solutions for objective. Initially, the genetic algorithm begins with a primary
population including random chromosomes that consist of genes. In the next step, the algorithm
biases individuals toward the optimum solution through repetitive processes such as crossover
and selection operators. A new population can be produced by two methods: steady-state GA and
generational GA. In the first case, one or two members of population are replaced, while the
generational GA replaces all the produced individuals at each generation. Proposed algorithm
uses the second method so that the GA keeps the specified qualified individuals from the current
generation and copies them into the new generation as part of the solution. Other individuals of
the new population are obtained by crossover and mutation functions. Following steps are used in
proposed genetic algorithm
 [Start] Generate random population of n chromosomes (suitable solutions for the problem)
 [Fitness] Evaluate the fitness f(x) of each chromosome x in the population
 [New population] Create a new population by repeating following steps until the new
population is complete
 [Selection] Select two parent chromosomes from a population according to their fitness (the
better fitness, the bigger chance to be selected)
 [Crossover] With a crossover probability cross over the parents to form a new offspring
(children). If no crossover was performed, offspring is an exact copy of parents.
 [Mutation] With a mutation probability mutate new offspring at each locus (position in
chromosome).
 [Accepting] Place new offspring in a new population
 [Replace] Use new generated population for a further run of algorithm
 [Test] If the end condition is satisfied, stop, and return the best solution in current population
[Loop] Go to step 2
4. NETWORK AND ENERGY PARAMETERS
Network Lifetime: Network Lifetime is the number of rounds in which the first node dies in
round based routing protocol. In the random selection method cluster head selection in each round
is done on the basis of 1/p and residual energy. When a node becomes dead in the network, it will
not be the part of the network.
Coverage: A point in network considered as covered if it is in sensing range of node. In proposed
work Coverage is calculated percentage as number of points in the network is covered by sensing
range of at least one node at a given time.
Coverage= Number of point of network covered by sensing range of at least one node/ total
number of points in the network* 100.
The main contribution of my work is the design, implementation and verification of a new
protocol using k-mean and genetic algorithm. The proposed algorithm “Energy Efficient
Coverage Aware Routing Protocol for Wireless Sensor Network” is designed for homogeneous
wireless sensor network. Proposed algorithm is based on both critical issue energy and coverage.
Simulations results show that proposed algorithm increases the network lifetime by reduce the
energy consumption and preserve coverage. Simulation is done with MATLAB and a comparison
of algorithm with benchmark algorithms is also performed.
5. PROPOSED APPROACH FOR CLUSTERING USING GENETIC ALGORITHM
Proposed algorithm works in rounds. Each round consists of Initial phase and Steady phase. In
this study, single-hop communication is used within the normal nodes and from cluster head to
BS. Network administrator selects a threshold value for the cluster head selection process. In

26
Initial phase all nodes send the residual energy, k-mean and probability parameters to the own
internal function to calculate fitness function. K-mean i.e. degree of coverage means sensor node
covered by k nodes which are in its sensing range. According to the result of internal function, the
node starts a counter. The nodes with high result of internal function are selected by the network
administrator and notify its candidacy as cluster head to the base station. The objective of the
process to find the cluster heads with higher capabilities and distribute them in the network so
that the total network energy consumption is minimized and coverage get preserved.
5.1. Details of algorithm
5.1.1. Initial phase:
In initial phase of each round a population and fitness function is be used to select cluster head
and cluster member. In first round of algorithm choose K initial cluster heads z1, z2……, zK from
the n nodes {x1, x2,……,xn}. Than cluster member will be selected for each cluster head.
Step1: Initially all nodes have same probability to become a cluster head.
Step2: F(t) is the fitness function used to select the cluster head and defined as
F(t)=(Er * T(n) * Kmean))
where
Er=Residual energy of the node
T(n)= probability of node to become cluster head as given by LEACH
Kmean= Degree of coverage
In this equation the energy, kmean and probability have a direct relationship with the output of
function, so increased value of inputs will produce a larger output the optimal value obtained for a
particular node will be eligible to be a cluster node.
Step 3:
After calculating this built in F(t) function. All nodes sort in descending order. For each network,
network administrator decides number of cluster heads. Candidate nodes send their information to
the sink for cluster head selection.
Step 4: Selection of Cluster Head
BS selects the cluster head and the result is sent to the network. At the BS a predefined number of
nodes introduced themselves as the candidate of cluster head will determine the length of the
chromosome. Each of the genes in this chromosome is identifier of number of network nodes.
Single-point crossover is performed on chromosome which is selected and mutation is performed,
that may change a bit by leap or jump a bit state to generate one or more new chromosomes. After
this base station selects a chromosome that has minimum difference of energy from last round
and with best probability and high distribution function as cluster head to network. Other nodes
bind to the nearest cluster head.
Step 5: BS will send the information to selected cluster head
Step 6: Cluster Formation
Selected cluster head send advertisement to normal nodes. Non cluster head nodes join to closest
CH to form cluster
Step 7: Sensing unneeded node to sleep state

27
If k-coverage of area>2 then node deployed in that area will goes into sleep mode. Selection of
node will be done by round robin algorithm.
5.1.2. Steady State Phase
After initial state each node knows its cluster head. In each period a cluster head only
receive one package form each member nodes of the cluster. Cluster head aggregate the
data and send to it BS
5.2 Pseudo-code of the Proposed Approach:
BEGIN
1: Specify the probability (p), number of nodes (n);
2: Einit(s)=E0, s=1,2, …, n;
5.2.1 Initial Phase
1: Each node calculate Fitness Function F(t)
2: Start Counter
4: CCH{s}=TRUE; //node s be a candidate CH
3: SendToBS(IDx,) (Xx,Yx)
// The first predefine number of node reaches at threshold value will sends their candidacy to the
sink for cluster head selection.
5: GAinBS
//BS perform the GA on the node which sends their candidacy for cluster and select cluster head
for the network.
6: SendToCH ← BS send message to selected cluster head.
7: if (CH{s}=TRUE) then
8: BC (ADV) ← broadcast an advertisement message;
9: Join(IDi); //non-cluster head node i join into the closest CH
10: K-coverage of area> 2 then node in that area will goes into sleep mode //Selection of node
will be done by round robin algorithm
11: Cluster(c); //form a cluster c;
12: end if
5.2.2 STEADY-STATE PHASE
1: If (CH(s)=TRUE) then
2: Receive(IDi, Data) //receive data from members;
3: Aggregate(IDi, Data) //aggregate received data;
4: TansToBS(IDi, Data); //transmit received data;
5: else
6: TansToCH(IDi, Data); //transmit sensed data;
7: end if
8: end if
9: } // round is completed
END
6. NETWORK PRELIMINARIES
Simulation is done with MATLAB. A network with 100 nodes deployed over an area of size 100
x 100 m2
. All nodes are of same specification. All nodes in the network are having the same

28
energy at starting point and having maximum energy. All nodes consume same energy for
transmission and reception Sensor nodes have three states –In the active mode, a sensor observes
the environment and communicates with other sensors. In the sleep mode, a sensor cannot sense
or transmit data .In off mode, the nodes are completely turned off .A sensor can change to active
mode from sleep mode to active mode whenever it receives appropriate signal from base station.
In this proposed algorithm, sleep and active mode are used. The nodes are randomly deployed.
Simulations performed for performance analysis of proposed algorithm. The simulation result
gives in terms of the coverage percentage of network area. The coverage percentage is calculated
on the basis of percentage of X, Y point covered by sensing range of at least one node in the
network and a comparison of algorithm with benchmark algorithms is also performed.
7. SIMULATION PARAMETERS
Simulation Parameters : following simulation parameters are used to perform simulation.
Table1: Simulation parameters
Table 2: The values of genetic algorithm
8. SIMULATION RESULTS
The comparison is done between the LEACH and CPCP protocol with the new technique under
the same simulation condition and values. Table 3 lists the obtained simulation results using
LEACH, CPCP and EECA. Table 4 shows the comparison and the number of rounds after which
first dead node and last dead node occur in the network when all nodes are active using LEACH
and EECA. As results shows that the number of round in which first round dead is 322 from 10
simulations with LEACH protocol is increased to 870 rounds with proposed algorithm. The
Parameter Values
Simulation Round 8000
Number of rounds 100+1(Node+BS)
Topology Size 100 X 100
CH probability 0.05
Initial Node Power 0.5 Joule
Nodes Distribution Nodes are Randomly Distributed
Energy for Transmission (ETX) 50*0.000000001
Energy for Reception (ERX) 50*0.000000001
Energy for Data Aggregation
(EDA)
5*0.000000001
Parameter Value
Initial population 100
Crossover rate 0.5
Mutation rate 0.5

29
proposed technique has improved the performance in terms of increasing the network lifetime by
comparing with LEACH. Table 5 shows the comparison and the number of rounds after which
first dead node occur in the network when all nodes and selected nodes are active according to k-
mean using CPCP and EECA. As a results shows that that the number of round in which first
dead nodes when all nodes are active is 788 and selected nodes are active is 876 from 10
simulation with CPCP protocol is increased to 858 and 1509 respectively The proposed technique
has improved the performance in terms of coverage by comparing with CPCP protocol. The
results show that the proposed approach outperforms LEACH and CPCP in terms of lifetime of
network and coverage.
Table 3. Number of round in which first dead node and last dead node occur in the network for CPCP and
LEACH
Routing Protocols Round number in which
first dead node occur
Round number in which
last dead node occur
EECA 1701 7414
LEACH 387 1302
CPCP 903 6051
Table 1. Comparison of LEACH and EECA
Simulation Run
LEACH EECA
Round number
in which first
dead node
occur when all
nodes are
active
Round number
in which last
dead node
occur when all
nodes are active
Round
number in
which first
dead node
occur when all
nodes are
active
Round
number in
which last
dead node
occur when all
nodes are
active
Simulation Run1 387 1302 910 2701
Simulation Run2 340 1049 853 2281
Simulation Run3 355 885 865 3060
Simulation Run4 312 1029 929 3240
Simulation Run5 383 1301 840 2640
Simulation Run6 354 1051 876 3380
Simulation Run7 370 1030 868 3080
Simulation Run8 380 1084 818 3540
Simulation Run10 386 1127 888 2633

30
Figure 1. Number of Round in which first dead node occur in LEACH
Figure 2. Number of Round in which last dead node occur in LEACH
Figure 3. Number of Round in which first dead node occur in EECA

31
Figure 4. Number of Round in which last dead node occur in EECA
Table 1. Comparison of CPCP and EECA
Simulation Run
CPCP EECA
Round number
in which first
dead node
occur when all
nodes are
active
Round number
in which first
dead node
occur when
selected nodes
are active
Round
number in
which first
dead node
occur when all
nodes are
active
Round number
in which last
dead node
occur when
selected
nodes are
active
Simulation Run10 760 902 868 1105

32
Figure 5. Number of Round in which first dead node occur when all nodes are active
in CPCP
Figure 6. Number of Round in which first dead node occur when all nodes are active
in EECA
Figure 7. Number of Round in which first dead node occur when selected nodes are active
in CPCP

33
Figure 8. Number of Round in which first dead node occur when selected nodes are active
In EECA
9. CONCLUSIONS
In this paper, new approach Energy Efficient Coverage Aware routing protocol architecture,
reduce energy consumption by using clustering technique, therefore increase system lifetime, and
increase coverage of network by turning off some redundant nodes. The performance evaluation
in terms of network lifetime and coverage was conducted using MATLAB. This work proposed a
genetic algorithm based new approach for clustering for wireless sensor network. The LEACH
protocol does not consider k-mean of the node in cluster head selection. Proposed algorithm is for
the homogeneous network which uses k-mean method for cluster head selection and in this BS
selects the cluster head from the candidate cluster head using genetic algorithm. The proposed
technique has improved the performance in terms of network lifetime and coverage by comparing
with LEACH and CPCP protocol. The simulation result also shows that 95% of network area gets
covered. Hence proposed routing protocol work for both important issues of the wireless sensor
network i.e. network lifetime and coverage.
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