A Multi-Constrained Green Routing Protocol for IoT-Based Software-Defined WSN

In recent times, there has been a notable surge in the utilization of Internet of Things (IoT) network devices due to their vast applications. However, this rapid growth has undoubtedly led to raised energy consumption, which, in turn, has raised significant concerns about the environment. Consequently, there is a growing demand for green computing techniques that can mitigate IoT device's energy usage and carbon footprint. Clustering IoT networks is a useful strategy for extending their lifespan. However, clustering presents a complex optimization problem that falls under the category of NP-hard; hence making it a challenging issue. Nevertheless, using meta-heuristics algorithms has greatly improved our ability to tackle such challenges. Therefore, this study introduces a clustering scheme called EQ-AHA, which combines Equilibrium optimization and artificial hummingbird optimization techniques to enhance the efficiency of IoT-based Software-Defined Wireless Sensor Networks (IoT-SDWSN). The primary goal of EQ-AHA is to select the Cluster Heads (CHs) and determine the optimal path between CHs and the Base Station (BS). EQ-AHA employs a fitness function that considers three important factors: the distance between CHs, the distance between nodes and the CHs, and the energy levels of the nodes. Overall, this strategy improves the network's performance by 31.6% compared to other State-of-the-Art (SoA) algorithms.