Lifetime Improvement in Wireless Sensor Networks Using Hybrid Grasshopper Meta-Heuristic

Abstract

The energy efficiency and lifetime of wireless sensor networks (WSNs) are the more focusing points. The WSNs faced many challenges during the data transmission. Node deployment, leader selection, and optimal route selection are challenges that affect the energy level and lifetime of WSNs. Many existing techniques have been proposed to node deployment, cluster leader and optimal route selection. But, all existing techniques have not given satisfactory results in the network energy optimization. Therefore, this paper presents hybrid artificial grasshopper optimization algorithm (HAGOA). It is an inherited behavior of artificial grasshopper optimization and artificial bee colony variance. The proposed algorithm will place sensor nodes using artificial grasshopper optimization technique. These sensor nodes may be static or dynamic that depends on the network scenario. The cluster head selection and optimal route selection will perform using artificial bee colony variance. It also performs balancing between exploration and exploitation phases in the given search space. This algorithm is a combination of two families: Artificial grasshopper and ABC Variance, respectively. It compares with existing classical and swarm intelligence (SI) protocols in the terms of remaining energy, sensor node lifetime, consumed energy, end to end delay and maximum number of rounds.