Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey
This paper has compared the performance of two nature-inspired optimization algorithms for getting optimal coverage in WSNs and observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA.
Abstract
In order to solve the critical issues in Wireless Sensor Networks (WSNs),\nwith concern for limited sensor lifetime, nature-inspired algorithms are\nemerging as a suitable method. Getting optimal network coverage is one of those\nchallenging issues that need to be examined critically before any network\nsetup. Optimal network coverage not only minimizes the consumption of limited\nenergy of battery-driven sensors but also reduce the sensing of redundant\ninformation. In this paper, we focus on nature-inspired optimization algorithms\nconcerning the optimal coverage in WSNs. In the first half of the paper, we\nhave briefly discussed the taxonomy of the optimization algorithms along with\nthe problem domains in WSNs. In the second half of the paper, we have compared\nthe performance of two nature-inspired algorithms for getting optimal coverage\nin WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant\nColony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The\nsimulation results confirm that LO gives better network coverage, and the\nconvergence rate of LO is faster than that of IGA-BACA. Further, we observed\nthat the optimal coverage is achieved at a lesser number of generations in LO\nas compared to IGA-BACA. This review will help researchers to explore the\napplications in this field as well as beyond this area. Keywords: Optimal\nCoverage, Bio-inspired Algorithm, Lion Optimization, WSNs.\n