Mobile Edge Computing for Unmanned Vehicles

Unmanned aerial vehicles (UAVs) and autonomous vehicles are becoming more ubiquitous than ever before. From medical to delivery drones, to space exploration rovers and self-driving taxi services, these vehicles are starting to play a prominent role in society as well as in our day to day lives.

 Efficient computation and communication strategies are paramount to the effective functioning of these vehicles. Mobile Edge Computing (MEC) is an innovative network technology that enables resource-constrained devices - such as UAVs and autonomous vehicles - to offload computationally intensive tasks to a nearby MEC server. Moreover, vehicles such as self-driving cars must reliably and securely relay and receive latency-sensitive information to improve traffic safety. Extensive research performed on vehicle to vehicle (V2V) and vehicle to everything (V2X) communication indicates that they will both be further enhanced by the widespread usage of 5G technology.

 We consider two relevant problems in mobile edge computing for unmanned vehicles. The first problem was to satisfy resource-constrained UAV's need for a resource-efficient offloading policy. To that end, we implemented both a computation and an energy consumption model and trained a DQN agent that seeks to maximize task completion and minimize energy consumption. The second problem was establishing communication between two autonomous vehicles and between an autonomous vehicle and an MEC server. To accomplish this goal, we experimented by leveraging an autonomous vehicle's server to send and receive custom messages in real time. These experiments will serve as a stepping stone towards enabling mobile edge computing and device-to-device communication and computation.