Swarm Intelligence–Based Energy‐Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications
G. Devika, D. Ramesh, Asha Gowda Karegowda · 2020
The industrial and scientific communities have witnessed an amplified interest for wireless sensor networks (WSNs) from few years concerning much on potential application under various domains. However, WSN has got corner of attention concerning mainly with factor of energy, in addition to assuring non-redundant data without compromising with QoS and transmission time. This shortcoming can be rectified with adaptation of energy-efficient strategies to facilitate extension of WSN lifetime with avoidance of unnecessary delays as much as possible. Among the various approaches for optimizing energy consumption, clustering techniques stand ahead among all sensor networks. Clustering technique influences strongly with greater work on energy conservation with its architectural design. One part of artificial intelligence is Swarm Intelligence (SI) which is inspired and designed looking on to different physical and chemical properties of multi-agents to solve optimization problems. The solution for WSN optimization problems based on SI clustering models are proving powerful, effective, and simple in order to improve lifetime of WSN. This chapter answers more frequent SI questions what, why, how, and where SI can be applied so as to optimize n/w energy utilization. The chapter covers over almost 60+ SI algorithms applications in brief. Furthermore, various issues of WSN clustering and WSN services are briefed for the sake of completeness. The major contribution is the survey of various SI techniques applied for WSN, in particular, for cluster formation and CH selection. The study reveals that among the various SI algorithms, PSO has been extensively applied for WSN cluster formation followed by use of ABC, CS, BFO, and ACO. We have categorized SI algorithms based on social behavior of insect, bacteria, bird, fish, animal, and others, among which, our survey (covering papers from 2000 to 2019) unfolds that almost 50% work is contributed by insects based SI. There is still lot of scope to explore new SI-based WSN in particular for cluster formation.