Intelligent Mobile Edge Computing Networks for Internet of Things

In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of Things (IoT) in the presence eavesdropping environments, where there are multiple users who can offload their confidential tasks to computational access point (CAP) assistance computation. One unmanned aerial vehicle (UAV) attacker exists system and it listen data transmission from CAP. We optimize design MEC network, by adaptively allocating offloading ratio wireless bandwidth, reduce linearly weighted cost latency as well energy consumption (EnC). Specifically, starting deep reinforcement learning, we devise a Q-network (DQN) adjust which help calculate suppress UAV efficiently. finally provide some simulation results validate proposed strategy. particular, strategy achieve much lower compared conventional ones, terms EnC.