Optimal Computation Offloading in Edge-Assisted UAV Systems

The ability of Unmanned Aerial Vehicles (UAV) to autonomously operate is constrained by the severe limitations of on-board resources. The limited processing speed and energy storage of these devices inevitably makes the real-time analysis of complex signals - the key to autonomy - challenging. In urban environments, the UAV 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 task to improve response time to sensor input and reduce energy consumption. However, the complexity of the urban topology and the 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 Markov process.