Self-healing and optimal fault tolerant routing in wireless sensor networks using genetical swarm optimization

Abstract

A wireless sensor network (WSN) is used in area monitoring, surveillance, virtual reality, artificial intelligence, etc. The interaction between sensor nodes (SNs) and base station (BS) play a major role in WSN. SNs use more energy to transmit data, and the occurrence of faults in these nodes may lead to complete network failure. To ensure enhanced network performance, the incorporation of an efficient fault detection mechanism becomes essential in WSN. The major aim of the work is to improve the self-healing capacity of the network. To meet the above requirement, a fault-tolerant routing path identification with genetical swarm optimization (FTGSO) is introduced in this work. Genetical swarm optimization (GSO) is the integration of the two optimizations like Genetic Algorithm (GA) and Particle swarm optimization (PSO). The cluster head (CH) selection carried out on the basis of residual energy, coverage, communication cost and proximity. Further, a fault-free routing path is introduced by GSO, and a self-healing method is employed to resolve any network connectivity issues and resume the normal system operation. The analysis and simulation outcomes are compared with other optimization algorithms to verify the effectiveness. The evaluation results verify the enhanced performance of the introduced scheme with a higher PDR (packet delivery ratio) of 96.8%, lower energy consumption of 0.19 J with a minimum packet loss ratio of 3.2% and 30 ms of E2E (end-to-end delay) compared to other existing routing protocols. Also, the performance of the proposed method is compared on the basis of E2E, energy efficiency, (PLR) packet loss ratio and PDR.