Efficient Aerial Data Collection With Cooperative Trajectory Planning for Large-Scale Wireless Sensor Networks

Due to the flexibility and agility, unmanned aerial vehicle (UAV) is a promising way for gathering data generated by wireless sensor networks. However, the limited battery capacity of the UAV restricts its application on many occasions, e.g., the networks deployed in the wild. In this paper, we propose a cooperative trajectory planning scheme to deal with the energy issue of the UAV, where a truck carrying backup batteries moves along with the UAV acting as a mobile recharging station. Our optimization task is to minimize the total mission time for gathering data from all the sensor nodes, which can be achieved by solving two problems: First, we need to divide the entire mission area into multiple subregions so that the UAV can hover over each subregion to collect the data of the sensor nodes through just one taking-off and landing under the constraint of battery capacity; second, we should find out the optimal trajectory of the truck so that the UAV can get to the hovering positions of each subregion from the truck and fly back to it before the battery drains considering the road condition in real world. We introduce an efficient clustering algorithm to partition the area into subregions in a load-balanced way to minimize the number of movements of the UAV. The trajectory planning task is formulated as a coordinated traveling salesman problem, which is solved by a three-step trajectory planning algorithm heuristically, and we also give the analysis of the upper bound and lower bound to demonstrate the performance guarantee. Numerical results show that our proposed scheme provides an effective and cost-efficient way for the data collection of large-scale wireless sensor networks in practical scenarios.

Automated summary

This paper proposes a cooperative trajectory planning scheme to address the energy issue of unmanned aerial vehicles (UAVs) by using a truck as a mobile recharging station. The scheme aims to minimize the total mission time for gathering data from sensor nodes by dividing the mission area into subregions and finding optimal trajectories for the UAV and the truck. An efficient clustering algorithm is introduced for load-balanced partitioning, and a three-step heuristic algorithm is used to solve the trajectory planning problem. Numerical results demonstrate the effectiveness and cost-efficiency of the proposed scheme for data collection in large-scale wireless sensor networks.