Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network

Sensors

Manickam Ramachandran¹  Sundararaj Suganthi²  Miroslav Mahdal³  Nagappan Umapathi⁴ 

[1] Data Analytics Lab, REST Labs, Kaveripattinam, Krishnagiri 635112, India;Department of Computer and Communication, Sri Sairam Institute of Technology, Chennai 600044, India;Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic;Department of Electronics and Communication Engineering, Jyothishmathi Institute of Technology and Science, Karimnagar 505481, India;

关键词: cluster head (CH);    energy consumption;    metaheuristics;    particle swarm optimization (PSO);    wireless energy transfer;   

DOI  :  10.3390/s22051736

来源: DOAJ

【 摘 要 】

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

【 授权许可】

Unknown