Optimized routing technique for IoT enabled software-defined heterogeneous WSNs using genetic mutation based PSO

Abstract Now a days, emerging trends in the field of wireless sensor networks (WSNs) tend to work on more complex scenarios and flexible network models as the conventional WSN systems that are based on a classical arrangement of sensors. Generally, these networks have different limitations such as control node election, data aggregation, load balancing during data collection etc. The load balancing depends on the effective routing techniques which provide an optimum path to transmit the data such that the minimum amount of energy should be consumed. The control nodes are responsible for assigning the task and data transmission in the cluster-based routing techniques and the selection of the control node is an NP-hard problem. To resolve this problem, an adaptive particle swarm optimization (PSO) ensemble with genetic mutation-based routing is proposed to select control nodes for IoT based software-defined WSN. The proposed algorithm plays a significant role in selecting the control nodes by considering energy and distance parameters. The proposed work is implemented for the heterogeneous networks having different computing power accompanied by single and multiple sinks. The experiment was carried out on the scale of the performance matrix such as fitness value, stability period, average residual energy, etc. The simulation result of the proposed algorithm outperforms over other algorithms under the different arrangements of the network.