Optimal Edge Computing for Infrastructure-Assisted UAV Systems

The ability of Unmanned Aerial Vehicles (UAV) to autonomously operate is constrained by the severe limitations of their on-board resources. The limited processing capacity and energy storage of these devices inevitably makes the real-time analysis of complex signals - the key to autonomy - challenging. In urban environments, the UAVs can leverage the communication and computation resources of the surrounding city-wide Internet of Things infrastructure to enhance their capabilities. For instance, the UAVs can interconnect with edge computing resources and offload computation tasks to improve response time to sensor input and reduce energy consumption. However, the complexity of the urban topology and large number of devices and data streams competing for the same network and computation resources create an extremely dynamic environment, where poor channel conditions and edge server congestion may penalize the performance of task offloading. This paper develops a framework enabling optimal offloading decisions as a function of network and computation load parameters and current state. The optimization is formulated as an optimal stopping time problem over a semi-Markov process. We solve the optimization problem using Dynamic Programming and Deep Reinforcement learning at different levels of abstraction and prior knowledge of the system underlying stochastic processes. We validate our results in a realistic scenario, where a UAV performs a building inspection task while connected to an edge server.