Recently, unmanned aerial vehicles (UAVs) as flying wireless communication platform have attracted much attention. Benefiting from the mobility, UAV aerial base stations can be deployed quickly and flexibly, and can effectively establish Line-of-Sight communication links. However, there are many challenges in UAV communication system. The first challenge is energy constraint, where the UAV battery lifetime is in the order of fraction of an hour. The second challenge is that the coverage area of UAV aerial base station is limited and the commercial UAV is usually expensive. Thus, covering a large target region all the time with sufficient UAVs is quite challenging. To solve above challenges, in this paper, we propose energy efficient and fair 3-D UAV scheduling with energy replenishment, where UAVs move around to serve users and recharge timely to replenish energy. Inspired by the success of deep reinforcement learning, we propose a UAV Control policy based on Deep Deterministic Policy Gradient (UC-DDPG) to address the combination problem of 3-D mobility of multiple UAVs and energy replenishment scheduling, which ensures energy efficient and fair coverage of each user in a large region and maintains the persistent service. Simulation results reveal that UC-DDPG shows a good convergence and outperforms other scheduling algorithms in terms of data volume, energy efficiency and fairness.
Unmanned aerial vehicle (UAV) data collection is a promising research direction that can be applied to many practical scenarios. Due to the limited energy of sensors in wireless sensor networks (WSN), UAV, which is considered as a mobile fusion center, can effectively prolong the lifetime of sensor via supporting communication with the sensor directly. However, since the UAV's energy constrained, it is necessary that multiple UAVs provide data collection to sensors in large areas. In this paper, we consider a scenario where multiple UAVs collect data from a set of two-dimensional distributed sensors. The UAV can communicate with sensors while flying or hovering. The goal is to minimize the total time that all UAVs from data center and return to data center after completing all collection tasks, while giving each sensor a certain amount of data and energy. The problem of minimizing the task completion time of multiple UAVs is still a big challenge. We solve the multi-UAV problem by jointly optimizing UAV-sensor association mechanism and data collection method of UAVs. The numerical results show that the proposed multi-UAV data collection scheme shortens the task completion time.INDEX TERMS UAV, data collection, trajectory optimization, traveling salesman problem.
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