Response Delay Optimization in Mobile Edge Computing Enabled UAV Swarm

Unmanned aerial vehicles (UAVs) can be conveniently deployed for environmental monitoring, firefighting, disaster rescue, and so on. However, the utmost challenge is how to transfer the important and urgent information to the control center as quick as possible in face of communication and computation constraints. As one of the promising technologies, mobile edge computing (MEC) technology can be deployed on UAVs to support computation-intensive and latency-critical applications. Therefore, a joint communication and computation optimization model is established for a MEC enabled UAV network, which includes a centralized MEC enabled top-UAV and a swarm of distributed bottom-UAVs. Using stochastic geometry, the successful transmission probability results for a single link and a group of links are derived based on the three-dimensional distribution of UAV swarm. Moreover, the optimal response delay is theoretically achieved with the closed-form solutions by using stochastic geometry and queueing theory. In contrast to the conventional UAVs without MEC capabilities, the optimal response delay is achieved by using our proposed joint communication and computation optimization algorithm in the MEC enabled UAV swarm scenario. The performances of the proposed algorithm are evaluated based on the results from the simulation system and the hardware testbed.