ECAR: Energy efficient cluster based adaptive routing for large scale WSN

Abstract

Wireless sensor networks consist of a large number of randomly distributed nodes in a given area. WSN nodes are battery-powered, so they lose all their energy after a certain period and this energy constraint affects the network lifetime. This study aims to maximize network lifetime while minimizing overall energy use. In this study, a novel Energy Efficient Cluster based Adaptive Routing (ECAR) approach has been proposed for large-scale WSNs. Initially, the Genetic Bee Colony algorithm (GBCA) is introduced, which provides an effective way for selecting cluster heads based on node degrees, node centralities, distances to neighbors, and residual energy. Consequently, the Quantum Inspired African Vulture Optimization algorithm (QIAVO) is utilized to find a routing path between the source and the destination over the cluster heads. To optimize the network performance, QIAVO considers multiple objectives, including residual energy, distance, and node degree. The proposed method is evaluated based on average packet delivery ratios, energy consumption, and average end-to-end delays. According to simulation results, the proposed protocol successfully balances the energy consumption of all sensor nodes and increases network lifespan.