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Due to the advantages of flexible deployment and extensive coverage, unmannedaerial vehicles (UAVs) have great potential for sensing applications in thenext generation of cellular networks, which will give rise to a cellularInternet of UAVs. In this paper, we consider a cellular Internet of UAVs, wherethe UAVs execute sensing tasks through cooperative sensing and transmission tominimize the age of information (AoI). However, the cooperative sensing andtransmission is tightly coupled with the UAVs' trajectories, which makes thetrajectory design challenging. To tackle this challenge, we propose adistributed sense-and-send protocol, where the UAVs determine the trajectoriesby selecting from a discrete set of tasks and a continuous set of locations forsensing and transmission. Based on this protocol, we formulate the trajectorydesign problem for AoI minimization and propose a compound-action actor-critic(CA2C) algorithm to solve it based on deep reinforcement learning. The CA2Calgorithm can learn the optimal policies for actions involving both continuousand discrete variables and is suited for the trajectory design. {Our simulationresults show that the CA2C algorithm outperforms four baseline algorithms}.Also, we show that by dividing the tasks, cooperative UAVs can achieve a lowerAoI compared to non-cooperative UAVs.

Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning