Presentation on mtech thesis on An Energy efficient cluster head selection in wireless sensor networks using improved Grey Wolf optimization

1. An energy-efficient cluster head selection in
wireless sensor network using Improved
Grey wolf optimization algorithm
Project Supervisor Name : Dr. Ritu Garg
Submitted By : Nikhil kumar
Roll No : 32013114
Semester : 3rd
MTech, Computer Engineering
National Institute Of Technology , Kurukshetra
Dissertation Topic
1

2. Content
• Introduction
• Problem Description
• Related Work
• Current status of work
• Conclusion and Future Work
• References
2

3. INTRODUCTION
1. Sensors in WSN are small, inexpensive, low-power, intelligent and disposable. The sensor
nodes are self-configuring and contain one or more sensors, integrated with wireless
communication devices and data processing components and a limited energy source.
2. Due to the large number of nodes and the possibly hazardous environment in which these nodes
are deployed, their batteries are often assumed to be nonreplaced.
3. Network lifetime is, therefore, dependent on the lifetime of individual nodes. This raises the
issue of energy-efficient design of the network.
.
3

4. Working flow of cluster head and base station (BS) in wireless sensor network (WSN)
4

5. Problem Description
 Wireless sensor networks comprise a large number of small sensor nodes scattered across
limited geographical areas.
 The nodes in such networks carry sources of limited and mainly unchangeable energy
 clustering is the most prominent solution to preserve the energy in wireless sensor Network
 Improper cluster head selection can lead to high energy
 Thats why for optimal clustering, an energy efficient cluster head selection is quite important
 This research effort focuses on the design of an energy-efficient cluster head selection algorithm
for WSNs
5

6. Vast research has been done in the area of wireless sensor networks in order to increase the
lifetime of the network.
Algorithms devised for increasing the longevity of the network can be broadly categorized into two
1. Heuristic-based clustering algorithm :
• low-energy adaptive clustering (LEACH) is of the predominant clustering algorithm which
elects the cluster head with some feasibility .
• reducing the unwanted traffic and energy consumption of nodes
• increasing the longevity of the network.
• However, it does not provide any adequate information about the number of cluster heads in a
network
Related Work
6

7. 2. MetaHeuristic-based clustering algorithm :
• Meta-Heuristic algorithms act as the most promising approach for NP-hard combinatorial
problems.
• Since they mimic from nature, it concentrates mainly on the aspirant which has a high survival
rate.
• Algorithms. Some of the approaches are ant colony optimization (ACO), fish colony optimization
(FCO), bird flocking behaviour, particle swarm optimization (PSO), firefly algorithm (FA) ,bat
algorithm (BA), cuckoo search (CS), artificial bee colony optimization (ABC), fish swarm
optimization Grey wolf optimizer (GWO)
7

8. • Recently,in 2020 cluster head selection in WSN using Grey Wolf Optimization is implemeted and
The observed results convey that the proposed algorithm outperforms better compared to E-
LEACH, GA, CS, PSO-C, and FFOA algorithms in terms of energy consumption, network lifetime
and packet received by the BS .
• Grey wolf albha ,beta ,delta lead omega wolf toward the area of search space that are promising
the optimal solution and this behaviour may lead to entrapment in locally optimal solution
,means still we can more improve for the cluster head selection .
• we required to implement a algorithm which should be the improved version of grey wolf
optimization so that cluster head selection in wireless sensor network can also be improved .
8

9.  Side-effect of GWO is the reduction of the diversity of the population and cause GWO to fall
into the local optimum which can be improved and due to this cluster head selection can also be
improved .
 To overcome these issues, recently an improved grey wolf optimizer (IGWO) algorithm is
Introduced.
 The IGWO algorithm benefits from a new movement strategy named dimension learning-based
hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in
nature. DLH uses a different approach to construct a neighborhood for each wolf in which the
neighboring information can be shared between wolves. This dimension learning used in the
DLH search strategy can enhance the balance between local and global search and maintains
diversity.
Current status of work
9

10. *
Flowchart of Improved Grey Wolf optimizer algorithm (I-GWO)

11.  The IGWO algorithm Can be implemented for the clusted head selection in wireless sensor
network which mainly contributes to selecting the cluster heads by considering the residual
energy and distance measurement of the sensor nodes.
 Initially, all the sensor nodes send their information (node_id, residual energy, location) to the
base station.
 I-GWO algorithm executed at the base station to select the optimal CH (i.e. by sensor node
information) and to form the optimal clusters. In order to process the cluster formation, we use
the weight function which involves the intra-cluster distance information, residual energy, and
neighborhood ratio of CHs respectively
…
11

12. The objective function of Cluster Head selection
 we use the objective function which utilizes the intra-cluster distance among the sensors and the
distance from the target node.
The objective function 𝑓1, 𝑓2 is mathematically represented as;
we use I-GWO algorithm to select the optimal CH to linearly decrease the function. The combined
objective function is mathematically represented as
𝐹=𝜇×𝑓1+(1−𝜇)𝑓2, 0< 𝜇<1
I
CH
m
i
E
f


1
1
2
min
)
,
(
)
,
(T
(
1
1
min
i
n
i
j
1
BS
CH
CH
n
f i
i
m
i
i

 
 

12

13. Experimental setup
Network Configuration
13
Parameter Value
Network Field (300,300)
Base station Position (150,150)
Sensor Node 400
Initial Energy 2J
Number of Cluster Head 20
E(elec) 50 nJ/bit
Packet Size,message size 4000bits,5000bits
IGWO parameter
Parameter Value
No of search agents 50
C (2-0)
a (0-1.5)
µ 0.27
Dimension of search agent 20
No of iteration 5000

14. Performance analysis of Algorithm
 The performance of the proposed algorithm will be measured using three metrics namely total
energy consumption (TEC), network lifetime (NL) and packet received by BS (PR-BS).
 These three performance metrics will be used to analyze the performance of the proposed
algorithm with other algorithms
 In order to measure the performance of energy consumption, firstly we will executed the
algorithms by varying the number of sensor nodes from 400 to 700 and the number of cluster
heads from 20 to 50.
14

15. Conclusion and Future Work
we presented a novel cluster head selection algorithm based on IGWO using efficient search agent
representation and novel objective function. For the energy efficiency, we have considered intra-
cluster distance, sink distance and the residual energy of sensors respectively. In addition to that, we
have formulated the weighted function for the efficient cluster formation
we have tested the proposed algorithm in the homogeneous network. In the future, the same can be
tested on heterogeneous networks
15

16. [1] M. C. M. Thein, and T. Thein, “An energy efficient cluster-head selection for wireless sensor
networks,” 2010 International Conference on Intelligent Systems, Modelling, and Simulation, IEEE,
pp. 287-291, 2010.
[2] Kaushik Sekaran, R. Rajakumar, K. Dinesh , “An energy-efficient cluster head selection in wireless
sensor network using grey wolf optimization algorithm ,” TELKOMNIKA Telecommunication,
Computing, Electronics and Control Vol. 18, No. 6, December 2020, pp. 2822~2833
[3] M. M. Afsar and M. H. Tayarani-N, “Clustering in sensor networks: A literature survey,” Journal of
Network and Computer Applications, vol. 46, pp. 198-226, 2014.
[4] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in engineering software,
vol. 69, pp. 46-61, 2014
[5] Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S., An Improved Grey Wolf Optimizer
for Solving Engineering Problems, Expert Systems with Applications (2020),
REFERENCES
16

17. THANK YOU
17