GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs. (6th February 2020)

• Record Type: Journal Article
• Authors: Agrawal, Deepika; Wasim Qureshi, Muhammad Huzaif; Pincha, Pooja; Srivastava, Prateet; Agarwal, Sourabh; Tiwari, Vikram; Pandey, Sudhakar
• Is Part Of: International journal of communication systems. Volume 33:Number 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-06
• Subjects: clustering -- cluster head -- grey wolf optimization -- optimization -- wireless sensor networks; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4344
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters. Abstract : Grey wolf optimization (GWO) technique is used to select cluster head (CH). Fitness function is defined for GWO. The following are the parameters responsible for the derivation of fitness function: ○ Average intra‐cluster distance ○ Average sink distance ○ Residual energy ○ CH balancing factor Key findings: The proposed protocol forms stable and balanced clusters. Prolongs the network lifetime.