UAV Trajectory Optimization for Network Edge IoT Device Data Collecting via Deadline Distributions

Integrating edge computing technologies with IoT solutions has revolutionized 5G and 6G networks. Edge computing is crucial for rapidly and effectively gathering and responding to information because it is close to IoT devices. Unmanned Aerial Vehicle (UAV) devices are efficient Internet of Things (IoT) solutions for collecting information. They are particularly advantageous because of their adaptability and ability to cover wide geographical areas. Nevertheless, the constrained energy resources available for UAVs have resulted in the flight path optimization issue emerging as a significant obstacle in numerous current research endeavors. On the contrary, the need for up-to-date information with tight deadlines is a practical challenge, resulting in a multi-objective optimization problem. An efficient method is to compute the distribution of incoming tasks so that real-time goals are handled appropriately rather than computing each deadline in detail. This study employs Gaussian and Poisson distributions to limit deadlines based on clusters of IoT devices. Additionally, the Genetic Algorithm (GA) is utilized to optimize the overall energy consumption of UAV devices. The numerical simulation results demonstrate that our proposed solution achieves optimal power levels compared to randomly or systematically planned routes while guaranteeing the fulfillment of mission timelines and optimizing the deployment of IoT device clusters.