Energy-Efficient UAV Trajectory Planning based on Flexible Segment Clustering Algorithm

Abstract

This paper plans the energy-efficient UAV trajectory when a UAV gathers data from massive IoT devices in a given area. The UAV trajectory design is addressed by two steps, i.e., IoT node clustering and UAV flight path planning for scanning the clusters, which are formulated as Cluster Minimization (CM) problem and Traveling Salesman Problem (TSP) in this work, respectively. The CM aims to contribute fewest clusters with minimal overlap to cover all the IoT devices and the per cluster size approaching the UAV communication coverage. On the other hand, the TSP seeks to design the shortest flight path to cover all the grouped clusters while minimizing energy consumption. Specifically, this work mainly focuses on the CM problem since the TSP issues have been well addressed in the past. In particular, we design a two-stage ILP optimization model to formulate the CM problem and propose two flexible clustering algorithms with low complexity, i.e., segment clustering (SC) and its variant, saying shifted SC (SSC). For the proposed ILP model and algorithms, we conduct extensive simulations under five different topologies and compare the performance results with existing methods. The simulation results indicate that the performance achieved by the proposed SSC algorithm is closest to the optimal results obtained from the ILP model. Moreover, it outperforms the existing methods under most topologies regarding cluster numbers, trajectory path length, and power consumption.