Energy-Efficient Path Planning Scheme of Multiple UAVs for Reliable Data Collection

Due to the flexibility and superior Line-of-Sight (LoS), Unmanned Aerial Vehicles (UAVs) have shown significant potential in Internet of Things (IoT) data collection. As the scale of IoT expands rapidly, higher demands are placed on energy efficiency and data transmission reliability. However, the limited battery life of UAVs restricts the application of a single UAV in large-scale, high-density wireless networks for data collection and data being sent to cloud for processing leads to poor Quality of Service (QoS) in traditional networks. To address these challenges, this paper aims to minimize UAV energy consumption while ensuring data collection reliability, proposing an energy-efficient data collection scheme in a cooperative multi-UAV scene. This scheme divides the non-convex problem into three subproblems for solution. First, to ensure the reliability of data transmission, introducing the guarantee of outage probability as a constraint, hovering altitude of the UAV is optimized to achieve the maximum coverage radius in the target area. Second, an Affinity Propagation (AP) clustering algorithm is introduced to partition the geographical area into the clusters with the minimum number which corresponds to the number of UAV movements. Finally, the set of horizontal position of the UAV hovering points can be optimized. And then, based on the three-dimensional (3D) coordinates, a hierarchical path planning algorithm for multiple UAVs is proposed which is formulated as a minimize maximum multiple traveling salesman problem (min-max MTSP) and solved effectively. Simulation results demonstrate that compared to existing methods, the proposed multi-UAV data collection scheme can ensure the reliability and decrease energy consumption.