Equitable Energy-Efficient Resource Optimization and Task Distribution for Green Multi-UAV Assisted Socially Internet of Things

Mobile edge computing (MEC), powered by renewable energy, is expected to be an environmentally benign computing solution for the Internet of Things (IoT). However, the erratic needs for computation and renewable energy typically lead to a mismatch between system requirements and energy supply, which wastes excess energy or results in a deficit. Hence, it is essential to minimize energy usage to enhance the system's performance. A multi-objective problem has been considered to reduce the overall energy usage and computational cost in an unmanned aerial vehicle (UAV) assisted social aware Internet of Things (SAIoT) network. Resources are optimized and allocated with a cluster head selection using particle swarm optimization (PSO), and task distribution is done using artificial bee colony (ABC) optimization. The simulated results show that the proposed approach, equitable energy-efficient task splitting and resource optimization (EE-TSRO), minimizes energy usage and latency. The proposed approach may be used in vehicle-to-vehicle communication, natural disaster management, robotic surgery, etc.