Swarm Intelligence-Based Energy-Efficient Framework in IoT

Abstract

Internet of things (IoT) has been developed for use in a variety of fields in recent years. The IoT network is embedded with numerous sensors that can sense data directly from the environment. The network’s sensing components function as sources, observing environmental occurrences and sending important data to the appropriate data center. When the sensors detect the stated development, they send this world data to a central station. Sensors, on the other hand, have limited processing, energy, transmission, and memory capacities, which might have a detrimental influence on the system. We have concentrated our current research on lowering sensor energy consumption in IoT network. This study chooses the most appropriate potential node in the IoT network to optimize energy usage. Throughout this paper, we suggest a fusion of techniques that combines PSO’s exploitation capabilities with the GWO’s exploration capabilities. The fundamental concept is to combine the strengths of the PSO’s capability to exploit with Grey Wolf Optimizer’s ability for efficient potential node selection. The proposed method is compared to the traditional PSO, GWO, Hybrid WSO-SA, and HABC-MBOA algorithms on the basis of several performance metrics.