A hybrid Meta-heuristic solution for Energy-Efficient routing in Software-Defined Wireless Sensor Network 

Abstract

Software-Defined Networking (SDN) has been adopted as an emerging networking paradigm within Wireless Sensor Networks (WSNs). SDN enables WSNs with self-configuration and programmable control to dynamically and efficiently manage the network functionalities. Generally, in WSN, smart sensing devices suffer from the low battery issue and they may be deployed in such environments where frequent recharge is not possible after the deployment. Therefore, this work focuses on energy-efficient routing problem considering Software-Defined Wireless Sensor Networks (SD-WSN) architecture. In SD-WSN, Control Server (CS) assigns the tasks to selected Control Nodes (CNs) dynamically. Thus, the CNs' selection process is developed as one optimization (NP-Hard) problem to make the network functional. To solve this problem effectively, a nature-inspired algorithm i.e., Grey Wolf Optimization (GWO) is hybridized with Particle Swarm Optimization (PSO) in order to improve its convergence and overall performance. This hybrid variant of GWO is dedicated to offering a Balanced clustering (BC) based routing protocol, this variant is referred to as HGWO-BC. Further, to solve the problem effectively, a fitness function is designed that considers several parameters e.g., intracluster distance, CS to CNs distance, nodes' residual energy, and cluster size. Thus, the proposed approach performs balanced, energy-efficient, and scalable clustering and prolongs the network life-time. To verify its effectiveness, an exhaustive simulation study is done. Comparative results show that the HGWO-BC approach outperforms other state-of-the-art approaches concerning network life-time, residual energy, network throughput, and convergence rate.