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A UAV Swarm Sensing Oriented Distributed Computing Cooperation Scheme

With the advantages of flexibility and miniaturization, Unmanned Aerial Vehicle (UAV) has been widely applied to operate sensing tasks in many complicated environments. Based on the airborne sensors, a UAV can capture multiple videos or snapshots for monitoring and data analysis. However, the scarce computing resource of one single UAV may cause an unacceptable execution delay for image processing, especially when a batch of images are captured in a very short period of time. To solve the challenge, we propose a UAV swarm based distributed computing collaboration structure to integrate the computing resource of UAVs for real-time sensing tasks. Specifically, a UAV is elected as the coordinator to conduct the resource scheduling strategy for the computing cooperation among the remaining UAVs. In order to improve the utilization of computing resource, different layers of the Convolutional Neural Network (CNN) are computed by diverse UAVs. These UAVs can rapidly process the batch of images in a pipeline manner. Moreover, we formulate a non-linear programming model to minimize the batch execution time. A Distributed CNN based Resource Scheduling (DCRS) algorithm is proposed to obtain the minimal execution time based on deep reinforcement learning. Simulation results demonstrate that our schemes not only can reduce the execution time but also can improve the computing resource utilization significantly.