An evolutionary computing-based energy-efficient solution for IoT-enabled software-defined sensor network architecture

Software-defined networking (SDN) has emerged as an evolving technique in wireless sensor networks (WSNs). SDN enables WSNs with programmable control to manage network functions dynamically and efficiently. In Internet of Things (IoT) applications, smart sensors suffer from the low battery issue, generally deployed in harsh network environments where regular recharge is not feasible. Moreover, integrating SDN with IoT-enabled sensor network puts forward several challenges, for example, control nodes' selection, load balancing, and energy cost optimization while aggregating the collected data, focusing on heterogeneous traffic data. Thus, an energy-efficient data collection technique via definite sensing control in two-level IoT-enabled software-defined heterogeneous WSN (2SD-HWSN) is formulated as an optimization problem, with transmission distance from smart sensors, residual energy of sensors, and load based on node density. The proposed algorithm is divided into two: set-up and transmission phases. In the set-up phase, the control server (CS) elects the best-suited control nodes (CNs) and sets up a schedule for coordinating data transmission. Further, normal nodes join appropriate CNs based on distance and residual energy. This way, CNs form clusters and route sensed data during the transmission phase. Therefore, an alternative nature-inspired algorithm, that is, grey wolf optimization (GWO), is hybridized with particle swarm optimization using a low-level co-evolutionary technique to improve its overall performance. This hybrid variant of GWO, known as HGWO-BC, offers balanced clustering (BC) via novel fitness function design. An exhaustive simulation study is performed in different scenarios considering homogeneous and heterogeneous sensors. Comparative results show that the HGWO-BC outperforms state-of-the-arts concerning network lifetime, instability period, residual energy, throughput, and computational efforts.