Coordinated UAV-UGV trajectory planning based on load balancing in IoT data collection

被引：0
|
作者：
Zhu Y. [1 ]
Wang S. [1 ]
机构：
[1] School of Electronic Science and Engineering, Nanjing University, Nanjing
来源：
Tongxin Xuebao/Journal on Communications | 2024年 / 45卷 / 01期
基金：
中国国家自然科学基金;
关键词：
data collection; load balancing; region partitioning; trajectory planning; unmanned aerial vehicle;
D O I：
10.11959/j.issn.1000-436x.2024005
中图分类号：
学科分类号：
摘要：
To improve the efficiency of large-scale Internet of things (IoT) data collection, a coordinated trajectory planning algorithm for multiple aerial and ground vehicles based on load balancing region partitioning was proposed, where unmanned aerial vehicles (UAVs) acting as aerial base stations were dispatched to gather data from IoT devices and unmanned ground vehicles (UGVs) acting as mobile battery swap stations were used to compensate for the shortage of UAV's energy. Aiming at shortening the mission completion time, the optimization task was to minimize the longest mission time among a fleet of UAV-UGVs, which was formulated as a variant of min-max multi-depot vehicle routing problem and solved from the load-balancing perspective. Specifically, the IoT devices were assigned to the UAV-UGVs' service zones by a load-balancing region partition algorithm, based on which the trajectory planning problem of multiple UAV and UGV was reduced to several independent route planning problems for each UAV-UGV pair. Then, a cooperative trajectory planning strategy was developed to optimize the route in each service zone. Numerical results validate that the proposed algorithm outperforms the compared algorithms in terms of mission completion time and balancing degree. © 2024 Editorial Board of Journal on Communications. All rights reserved.
引用
收藏
页码：41 / 53
页数：12
相关论文
共 26 条
  • [1] AAD W, BENNIS M, CHEN M Z., A vision of 6G wireless systems: applications, trends, technologies, and open research problems, IEEE Network, 34, 3, pp. 134-142, (2020)
  • [2] MORELLO R, MUKHOPADHYAY S C, LIU Z, Et al., Advances on sensing technologies for smart cities and power grids: a review, IEEE Sensors Journal, 17, 23, pp. 7596-7610, (2017)
  • [3] ANBARASAN M, MUTHU B, SIVAPARTHIPAN C B, Et al., Detection of flood disaster system based on IoT, big data and convolutional deep neural network, Computer Communications, 150, pp. 150-157, (2020)
  • [4] MOSTAFAEI H, CHOWDHURY M U, OBAIDAT M S., Border surveillance with WSN systems in a distributed manner, IEEE Systems Journal, 12, 4, pp. 3703-3712, (2018)
  • [5] BUSHNAQ O M, CHAABAN A, AL-NAFFOURI T Y., The role of UAV-IoT networks in future wildfire detection, IEEE Internet of Things Journal, 8, 23, pp. 16984-16999, (2021)
  • [6] GUO F X, YU F R, ZHANG H L, Et al., Enabling massive IoT toward 6G: a comprehensive survey, IEEE Internet of Things Journal, 8, 15, pp. 11891-11915, (2021)
  • [7] AL-KARAKI J N, KAMAL A E., Routing techniques in wireless sensor networks: a survey, IEEE Wireless Communications, 11, 6, pp. 6-28, (2004)
  • [8] ZHANG X W, DAI H P, XU L J, Et al., Mobility-assisted data gathering strategies in WSNs, Journal of Software, 24, 2, pp. 198-214, (2013)
  • [9] WANG C L, YAN J H., Path planning for UAV to collect sensor data in large-scale WSNs, Transactions of Beijing Institute of Technology, 35, 10, pp. 1044-1049, (2015)
  • [10] FU S, YANG X Y, ZHANG H J, Et al., UAV path intelligent planning in IoT data collection, Journal on Communications, 42, 2, pp. 124-133, (2021)
123