FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks

Abstract

In mobile wireless sensor networks, energy consumption and area coverage are two well-known optimization problems. An efficient and restricted sensor movement is essential so that redundant area coverage, as well as consumed energy, can be reduced to mitigate these two issues in mobile wireless sensor networks. To make equilibrium between energy consumption and the total area coverage by the sensor nodes is a difficult task. In this context, optimized path planning for sensor movement is crucial to reach the target. The article presents a Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization (FIS-RGSO) of energy and coverage in smart green mobile wireless sensor networks. The objective of this article is to achieve minimum energy consumption by the sensors through their optimum movements so that sensors can cover maximum area and increase their lifetime. The proposed approach improves the sustainability and performance of green sensor networks in terms of a lifetime and energy-efficiency by implementing restricted and organized sensor movements based on the decision taken by the Fuzzy Inference System, which leads to minimum energy consumption and less distance traversing. The simulation results reveal that our proposed model reduces the consumed energy in a range of 5%–45% as compared with the existing method in reverse glowworm swarm optimization (RGSO) algorithm. The total distance covered by the sensors is also minimized by almost 7%–62% as compared with the existing one. The proposed method has experimented extensively and the result shows it performs better than the existing one in terms of the total number of live sensors that exist after execution. Therefore, the proposed methodology is realized as an energy-efficient model in wireless sensor networks that proliferate network lifetime.