Efficient Binary Task Offloading Optimization in Large-Scale IoT Networks via UAV-Enhanced Mobile Edge Computing

Unmanned aerial vehicle (UAV)-enhanced mobile edge computing (U-MEC) integrates flexible deployment and wide coverage, effectively reducing computation latency for edge network devices. Whereas, optimization models that only consider a few or tens of nodes will hide the performance deficiencies of high-complexity algorithms in large-scale IoT networks. This paper aims to investigate the binary task offloading problem based on minimizing the shrinkage ratio in large-scale IoT networks. By optimizing the computation mode selection, bandwidth, and computing resource allocation of users, the goal is to maximize the sum of benefits brought to all users by the U-MEC network. To address the challenging mixed-integer nonlinear programming (MINLP) problem, an efficient solution based on the region coverage and a posterior method that eliminates unknown hovering constraints is adopted to decompose the problem into multiple independent small-scale subproblems. For each subproblem, the coordinate descent (CD) technique and a greedy strategy are employed, which is based on Karush-KuhnTucker (KKT) conditions to provide the closed-form solutions of bandwidth and computing resource allocation at the edge server. Experimental results demonstrate the effectiveness of our approach in terms of solution accuracy and execution time.