Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive computation-intensive mobile applications services. Therefore, the Unmanned Aerial Vehicle (UAV) edge computing (MEC) paradigm offers low latency communication, computation, storage capabilities, which makes it an attractive way mitigate these limitations by offloading them. Nevertheless, majority schemes let IoT devices send intensive tasks connected server, predictably limits performance gain due overload. this paper, besides integrating task load balancing, we study resource allocation problem for multi-tier UAV-aided MEC systems. First, efficient load-balancing algorithm is designed optimizing among ground servers through handover process as well hovering UAVs over crowded areas are still loaded fixed location base stations server (GBSs). Moreover, formulate joint offloading, integer minimize system cost. Furthermore, based on deep reinforcement learning techniques proposed derive solution. Finally, experimental results show that approach not only has a fast convergence but also significantly lower cost when compared benchmark approaches.