Task Offloading for Post-Disaster Rescue in Unmanned Aerial Vehicles Networks

Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, owing to their flexible mobilities and fast deployment. However, UAVs typically have very limited battery and computational capacities, which makes them harder to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue of the battery and computation resource limitation with a fog computing based UAV system. Specifically, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To avoid the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

Automated summary

In post-disaster rescue missions, unmanned aerial vehicles (UAVs) are effective tools, but their limited battery and computational capacities hinder heavy computation tasks. This paper proposes a fog computing based UAV system that utilizes unmanned ground vehicles (UGVs) to offload computation tasks from UAVs. Through a stable matching algorithm and an iterative algorithm, the proposed scheme improves utilities of UAVs and reduces average delay.