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Efficient whale optimisation algorithm-based SDN clustering for IoT focused on node density

The recent advent of Software Defined Networking (SDN) and intelligent networks have prompted the researcher to conduct further investigations into the high-density Wireless Sensor Network (WSN). WSNs have inherent issues that limit their performance, such as sensor resource restrictions that affect power supply, memory, processing units and communications capabilities. This paper proposes new clustering, using a Whale Optimisation Algorithm (WOA) based on the concept of SDN. The proposed protocol considers both sensor resource restrictions and the random diversification of node density in the geographical area. It begins by dividing the sensing area by the SDN controller into virtual zones (VZs) to balance the number of cluster heads (CHs) according to the node density in each VZ; it then uses the WOA, which considers residual energy, communication cost and node density, to define the optimal set of CHs. Simulation results show that the proposed protocol has improved network lifetime and resulted in efficient energy dissipation; furthermore, it has increased the number of packets sent to the sink by approximately 55% compared to traditional protocols, and by about 20% compared to an optimisation based routing protocol.