Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices nearby mobile servers, thereby lowering energy consumption and response time for ground users or IoT devices. Integration Unmanned Aerial Vehicles (UAVs) the computing (MEC) server will significantly benefit small, battery-powered, energy-constrained in 5G future wireless networks. We address problem maximising computation efficiency U-MEC by optimising user association offloading indicator (OI), computational capacity (CC), power consumption, duration, optimal location planning simultaneously. It is possible assign some heavy UAV faster processing small ones (MUs) locally. This paper utilizes k-means clustering algorithm, interior point method, conjugate gradient method iteratively solve non-convex multi-objective resource allocation problem. According simulation results, both local schemes give solution.