Particle Swarm Optimization and Genetic Mutation Based Routing Technique for IoT-Based Homogeneous Software-Defined WSNs

Recent advancement in wireless sensor network has evolved as an open system which can be reconfigured dynamically. Generally, these networks have different limitations and challenges such as energy consumption in data collection, control node election, load balancing, etc. An efficient load balancing in terms of data collection and forwarding is depended on the routing techniques which are responsible to provide an effective path to transmit the collected data such that the minimum amount of energy should be consumed in the process. The control nodes are responsible for assigning the task and data transmission in the cluster-based routing techniques. The selection of the control node is an NP-hard problem. In this paper, an adaptive particle swarm optimization (PSO) ensemble with genetic mutation-based routing is introduced to select control nodes for IOT-based software-defined WSN. The proposed method plays a significant role in selecting the control nodes based on the fitness value. Fitness value takes energy and distance parameters into consideration. First, the proposed work is implemented for homogeneous nodes that can be deployed with single and multiple sinks. Further, the proposed work can also implement for the heterogeneous sensor nodes having different computing power accompanied by single and multiple sinks. The simulation result of the proposed method outperforms over some other existing algorithms under the different arrangements of the network.