An Intelligent Task Offloading Algorithm (iTOA) for UAV Network

Unmanned Aerial Vehicle (UAV) is emerged as a promising technology to support human activities, such as target tracking, disaster rescue and surveillance. However, these tasks require a large computation load on image or video processing, which imposes huge pressure on the UAV computation platform. To solve this issue, in this work, we propose an intelligent task offloading algorithm (iTOA) for UAV edge computing network. Compared with existing methods, iTOA is able to intelligent perceive the environment of the network to decide the offloading action based on deep Monte Calor Tree Search (MCTS), the core algorithm of Alpha Go. MCTS will simulate the offloading decision trajectories into the future to acquire a best decision by maximizing reward, such as lowest latency or power consumption. To accelerate the search convergence of MCTS, we also proposed a splitting deep neural network (sDNN) to supply the prior probability for MCTS. The sDNN is trained by a self-supervised learning manager. Here, the training data set is obtained from iTOA itself as its own teacher. Compared with game theory and greedy search based methods, the proposed iTOA improves service latency performance by 33% and 60%, respectively.