A Multi-Objective Meta-Heuristic Solution for Green Computing in Software-Defined Wireless Sensor Networks

Abstract

Software-Defined Wireless Sensor Network (SDWSN) provides an architecture which enables WSN with self-configurable, scalable and programmable-control features. In SDWSN, network is decoupled into data plane and control plane, Normal Node (NN) at data plane is responsible for data collection and processing. Control Node (CN) in control plane routes the received data from the NNs and forwards it to its neighboring CNs and Control Server (CS) following the programmed instructions. The CNs are dynamically selected and placed wisely to activate SDWSN functions for achieving certain QoS parameters. The CNs require more energy and run with limited battery capacity that is not frequently rechargeable. Considering this scenario, this work proposes a green computing-aware multi-objective solution for the CNs placement problem by utilizing a meta-heuristic approach with energy, distance and relative-load imbalance performance metrics. In this work, a nature inspired Multi-Objective Harris Hawks optimization (MOHHO) meta-heuristic solution is proposed to offer an optimal multi-objective solution. Simulation results show the effectiveness of the proposed MOHHO solution in comparison to other state-of-the-art methods in terms of optimizing energy, load imbalance and delay performance metrics. This significantly enhances the lifetime of the SDWSN.