Energy efficient and coverage aware grey wolf optimizer based clustering process for Software-defined wireless sensor networks

Abstract

Clustering has been adopted as a critical technique for reducing energy consumption in wireless sensor networks (WSNs). Furthermore, the selected group of Cluster Heads (CHs) should enhance the network’s coverage while minimizing any impact on its energy efficiency. Although distributed CHs election processes have improved the network’s lifespan, they have struggled to provide adequate coverage. Moreover, they usually use impractical assumptions like unlimited transmission range and location cognition. On the other hand, centralized meta-heuristic-based CHs selection algorithms have shown remarkable improvement in clustering problems using centralized decisions. However, software-defined wireless sensor networks (SDWSN) have gained a prevalent use in recent years. SDWSN consists of stripping control from nodes and centralizing it in an external controller. This paper introduces an energy-efficient and coverage-aware Grey wolf optimizer (GWO) based clustering process for SDWSN. Based on a novel fitness function, our proposed algorithm aims to maximize coverage, decrease blind spots, and preserve energy in the network. Our algorithm is widely evaluated on various homogeneous and heterogeneous network scenarios, with varied network sizes and base station (BS) locations, versus prominent clustering algorithms. The obtained results testify that our proposed algorithm shows better performance in energy efficiency and CHs distribution. Our algorithm improved coverage up to a mean of 31.42%, 27.72%, 18.69%, 21.12%, 13.81%, 4.14%, 3.8% and 8.31% compared to CREEP, DEEC, I-LEACH, HEESR, BRE-LEACH, PSO-ECHS, EPSO-CEO, and GWO-C, respectively, in all simulation cases.