Executive Summary : | The increasing number of Internet of Things (IoT) devices and applications pose challenges in latency, processing capacity, and scalability due to the adoption of fifth-generation (5G) networks. The key concern is the limited data processing resource and battery capacity of IoT devices for executing tasks with limited latency prerequisites, such as target tracking, disaster response, and forest monitoring. A computation offloading system is required to address this issue, particularly in delay-sensitive applications. Task offloading via Mobile edge computing (MEC) has been researched in recent years, with the implementation of UAV-enabled MEC expanding its reach in recent times. Advancements in wireless energy transfer technology can be used to mount a radio-frequency (RF) energy transmitter on UAVs in conjunction with a MEC server to provide stable energy supply and computation resources to IoT devices equipped with an energy harvesting module. However, a single UAV cannot meet coverage and computation requirements due to limited energy and computational resources. The proposed project aims to design a framework for a multi-UAV-enabled MEC system to offer adaptable computation support to IoT devices with strict service deadlines. The goal is to serve maximum IoT devices by jointly optimizing the UAV trajectory, resource distribution, and computation offloading while completing assigned tasks on schedule. There is limited research on cooperative MEC and multi-UAVs with RF energy transmitters. The project aims to close the research gap in time-sensitive IoT applications by focusing on the stated issues using a multi-UAV enabled framework. |