Energy-Efficient Data Collection From UAV in WSNs Based on Improved PSO Algorithm

• Published in: IEEE sensors journal Vol. 24; no. 21; pp. 35762 - 35774
• Main Authors: Zeng, Yaoping, Guo, Guanghua, Chen, Shisen, Qiang, Yifan, Liu, Jia
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Autonomous aerial vehicles; Data acquisition; Data collection; Energy consumption; Nodes; Particle swarm optimization; Particle swarm optimization (PSO); Sensors; successive convex approximation (SCA); Target acquisition; Trajectory; Trajectory optimization; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Wireless sensor networks; wireless sensor networks (WSNs)
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3453937

With the rapid development of wireless sensor networks (WSNs), the energy challenges associated with data acquisition in WSNs have catched increasing attention in recent years and the unmanned aerial vehicle (UAV) has become an effective means of data acquisition. This article introduces a data acquisition scheme for WSNs with the assistance of a single UAV. Our target is to minimize the total system consumption while ensuring that the UAV collects the required amount of data for the sensor nodes (SNs) by jointly optimizing the flight trajectory of the UAV as well as the wake-up schedule of the SNs. Since the problem is nonconvex, we propose an algorithm based on successive convex approximation (SCA) to efficiently solve the problem. First, in most cases, the coordinates of SNs are generally unknown, so we propose an enhanced particle swarm optimization (PSO) algorithm based on improved distance vector hop (DV-Hop) to get accurate node coordinates; next, we jointly optimize the UAV trajectory and the wake-up time allocation of the SNs. Finally, the experimental results show that our scheme performs better than other referenced solutions in achieving high-precision localization of the nodes and reducing the system energy consumption.