Two-Phases Flying Process Based Cruise Time Minimization for Collecting Data in a UAV-Enabled Untraveled Wireless IoT Network

Abstract

This work studies a practical scenario where the unmanned aerial vehicle (UAV) is dispatched from the data center to collect the data from the Internet of Things (IoT) devices and return to the data center. These IoT devices with different amounts of data and are sparsely located in an untraveled area. In this scenario, we consider minimizing the UAV's cruise time by jointly optimizing the clustering strategy of the IoT devices, the association strategy between the UAV and the IoT devices, the UAV's trajectory, and the bandwidth allocation strategy. Traditionally, the minimal cruise time is obtained by visiting all IoT devices one by one and this will lead to huge cost. To address this problem, affinity propagation (AP) algorithm is proposed to cluster all these IoT devices first of all. Then, to avoid visiting all devices one by one and realize parallel computation of data collection time, the entire UAV's flying process is divided into two phases, namely, the inter-cluster phase and the intra-cluster phase. For the inter-cluster phase, the minimal cruise time is formulated to minimize total distance among clusters using the traveling salesman problem with a neighborhood (TSPN)-based algorithm. Subsequently, for the intra-cluster phase, we formulate the collecting time minimization problem in each cluster as a mix-integer non-convex optimization problem. To address these issues, we design a two-loop algorithm involving the bisection search, the altering optimization (AO), and the successive convex approximation (SCA) methods. Simulation results validate the effectiveness of our proposed scheme.