Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.

I. Introduction

With the development of the economy and technology, the Internet of Things (IoT) came into being [1]. It refers to the connection of any object to the network through information sensing equipment according to an agreed protocol, and the object exchanges and communicates through information media to realize intelligent identification, positioning, tracking, supervision, etc. As an emerging network technology and industrial model, it has gradually become an important strategic goal for various enterprises. In recent years, IoT has been successfully applied to many fields ranging from daily life to industrial manufacturing such as intelligent transportation [2], smart home [3], smart cities [4], and smart grids [5].