Latency Optimization in UAV Networks Based on MEC and NS

Abstract

The growing use of Unmanned Aerial Vehicles (UAVs) for things like surveillance and disaster response makes it harder to meet strict real-time performance standards.  To solve this problem, the proposed research combines Mobile Edge Computing (MEC), Network Slicing (NS), and a lightweight Latency-Aware Deep Deterministic Policy Gradient (LLDDPG) algorithm to make task offloading more efficient and lower system latency.  We divided UAV image data into three groups based on file size and sent them to different MEC nodes.  The first tests with static and heuristic load-balancing strategies showed some improvement.  With LLDDPG training, the time it took to process improved from (569, 119, 99,222) seconds to about (425, 320, 3,050) seconds.