Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network

TL;DR

This paper proposes a joint data collection and sensor positioning scheme using multiple UAVs in wireless sensor networks, optimizing UAV trajectories and sensor data transmission to improve accuracy and efficiency.

Contribution

It introduces a novel optimization framework that jointly optimizes UAV trajectories, sensor transmission, and positioning points, addressing challenges of sensor localization and data collection in WSNs.

Findings

The scheme effectively reduces sensor positioning error.

It maximizes data upload from sensors.

Simulation confirms improved positioning and data collection performance.

Abstract

Due to the high mobility and easy deployment, unmanned aerial vehicles (UAVs) have attracted much attention in the field of wireless communication and positioning. To meet the challenges of lack of infrastructure coverage, uncertain sensor position and large amount of sensing data collection in wireless sensor network (WSN), this paper presents an efficient joint data collection and sensor positioning scheme for WSN supported by multiple UAVs. Specifically, a UAV is set as the main UAV to collect data, and other UAVs are used as auxiliary UAVs for sensor positioning using time difference of arrival (TDoA). A mixed-integer non-convex optimization problem with uncertain sensor position is established. The goal is to minimize the average positioning error of all sensors by jointly optimizing the UAV trajectories, sensor transmission schedule and positioning observation points (POPs). To solve this optimization model, the original problem is decomposed into two sub-problems based on the path discrete method. Firstly, the block coordinate descent (BCD) and successive convex approximation (SCA) techniques are applied to iteratively optimize the trajectory of the main UAV and the sensor transmission schedule, so as to maximize the minimum amount of data uploaded by the sensor. Then, based on the trajectory of the main UAV, a particle swarm optimization (PSO)-based algorithm is designed to optimize the POPs of UAVs. Finally, the spline curve is applied to generate the trajectories of auxiliary UAVs. The simulation results show that the proposed scheme can meet the requirements of data collection and has a good positioning performance.