Multi-Unmanned Aerial Vehicles Trajectory Optimization for Age of Information Minimization in Wireless Sensor Networks

HU Haonan; HAN Ming; LI Wenpeng; ZHANG Jie

doi:10.11999/JEIT230458

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1222-1230

HU Haonan, HAN Ming, LI Wenpeng, ZHANG Jie. Multi-Unmanned Aerial Vehicles Trajectory Optimization for Age of Information Minimization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1222-1230. doi: 10.11999/JEIT230458

Citation:

HU Haonan, HAN Ming, LI Wenpeng, ZHANG Jie. Multi-Unmanned Aerial Vehicles Trajectory Optimization for Age of Information Minimization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1222-1230. doi: 10.11999/JEIT230458

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Multi-Unmanned Aerial Vehicles Trajectory Optimization for Age of Information Minimization in Wireless Sensor Networks

doi: 10.11999/JEIT230458

HU Haonan²,
HAN Ming^{1, 2
,
,},
LI Wenpeng^{1, 2},
ZHANG Jie^{2, 3}

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communication Technology, Chongqing, Chongqing 400065, China
3.
The University of Sheffield, Sheffield S10 2TN, British

Funds: The National Natural Science Foundation of China (61831002), Chongqing Graduate Research Innovation Project (CYS21300)

Received Date: 2023-05-19
Rev Recd Date: 2023-09-26

Available Online: 2023-10-08

Publish Date: 2024-04-24

Abstract

Abstract

Due to the limited transmitting power of sensors in the Wireless Sensor Network (WSN) and high probability of large distance between sensors and their associated Base Station(BS), the sensor data may not be received in time. This will reduce the data freshness of sensor data and affect the quality of decision for delay sensitive service. Therefore, the use of Unmanned Aerial Vehicles (UAVs) to assist in collecting sensor data has become an effective solution to decrease the data freshness, measured by Age of Information (AoI), in wireless sensor networks. A UAV trajectory optimization algorithm based on the Multi-Agent Proximal Policy Optimization (MAPPO) method is developed in this paper, which employs a centralized-training and distributed-execution framework. By jointly optimizing the flight trajectories of all UAVs, the average AoI of all ground nodes is minimized. The simulation results verify the effectiveness of our proposed UAV trajectory optimization algorithm on minimizing the AoI in the WSN.
- Unmanned Aerial Vehicles(UAV)-assisted communication,
- Age of Information (AoI),
- Trajectory planning,
- Multi-agent reinforcement learning

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References(20)

References

[1]	SHAH S K, JOSHI K, KHANTWAL S, et al. IoT and WSN integration for data acquisition and supervisory control[C]. 2022 IEEE World Conference on Applied Intelligence and Computing, Sonbhadra, India, 2022: 513–516.
[2]	AL-MASHHADANI M A, HAMDI M M, and MUSTAFA A S. Role and challenges of the use of UAV-aided WSN monitoring system in large-scale sectors[C]. 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ankara, Turkey, 2021: 1–5.
[3]	AL-SHARE R, SHURMAN M, and ALMA’AITAH A. A collaborative learning-based algorithm for task offloading in UAV-aided wireless sensor networks[J]. The Computer Journal, 2021, 64(10): 1575–1583. doi: 10.1093/comjnl/bxab100.
[4]	VERMA S and ADHYA A. Routing in UAVs-assisted 5G wireless sensor network: Recent advancements, challenges, research GAps, and future directions[C]. 2022 3rd International Conference on Intelligent Engineering and Management, London, United Kingdom, 2022: 422–428.
[5]	KAUL S, YATES R, and GRUTESER M. Real-time status: How often should one update?[C]. 2012 Proceedings IEEE INFOCOM, Orlando, USA, 2012: 2731–2735.
[6]	ZHANG Guangyang, SHEN Chao, SHI Qingjiang, et al. AoI minimization for WSN data collection with periodic updating scheme[J]. IEEE Transactions on Wireless Communications, 2023, 22(1): 32–46. doi: 10.1109/TWC.2022.3190986.
[7]	LIU Juan, WANG Xijun, BAI Bo, et al. Age-optimal trajectory planning for UAV-assisted data collection[C]. IEEE Conference on Computer Communications Workshops, Honolulu, USA, 2018: 553–558.
[8]	TONG Peng, LIU Juan, WANG Xijun, et al. UAV-enabled age-optimal data collection in wireless sensor networks[C]. 2019 IEEE International Conference on Communications Workshops, Shanghai, China, 2019: 1–6.
[9]	JIANG Wenwen, SHEN Chao, AI Bo, et al. Peak age of information minimization for UAV-aided wireless sensing and communications[C]. 2021 IEEE International Conference on Communications Workshops, Montreal, Canada, 2021: 1–6.
[10]	ZHOU Conghao, HE Hongli, YANG Peng, et al. Deep RL-based trajectory planning for AoI minimization in UAV-assisted IoT[C]. 2019 11th International Conference on Wireless Communications and Signal Processing, Xi'an, China, 2019: 1–6.
[11]	ABD-ELMAGID M, FERDOWSI A, DHILLON H S, et al. Deep reinforcement learning for minimizing age-of-information in UAV-assisted networks[C]. 2019 IEEE Global Communications Conference, Waikoloa, USA, 2019: 1–6.
[12]	YI Mengjie, WANG Xijun, LIU Juan, et al. Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks[C]. IEEE Conference on Computer Communications Workshops, Toronto, Canada, 2020: 716–721.
[13]	ZHANG Qian, MIAO Jiansong, ZHANG Zhicai, et al. Energy-efficient video streaming in UAV-enabled wireless networks: A safe-DQN approach[C]. GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, China, 2020: 1–7.
[14]	FU Fang, JIAO Qi, YU F R, et al. Securing UAV-to-vehicle communications: A curiosity-driven deep Q-learning network (C-DQN) approach[C]. 2021 IEEE International Conference on Communications Workshops, Montreal, Canada, 2021: 1–6.
[15]	HU Jingzhi, ZHANG Hongliang, SONG Lingyang, et al. Cooperative internet of UAVs: Distributed trajectory design by multi-agent deep reinforcement learning[J]. IEEE Transactions on Communications, 2020, 68(11): 6807–6821. doi: 10.1109/TCOMM.2020.3013599.
[16]	WU Fanyi, ZHANG Hongliang, WU Jianjun, et al. Cellular UAV-to-device communications: Trajectory design and mode selection by multi-agent deep reinforcement learning[J]. IEEE Transactions on Communications, 2020, 68(7): 4175–4189. doi: 10.1109/tcomm.2020.2986289.
[17]	WU Fanyi, ZHANG Hongliang, WU Jianjun, et al. UAV-to-device underlay communications: Age of information minimization by multi-agent deep reinforcement learning[J]. IEEE Transactions on Communications, 2021, 69(7): 4461–4475. doi: 10.1109/TCOMM.2021.3065135.
[18]	SAMIR M, ASSI C, SHARAFEDDINE S, et al. Age of information aware trajectory planning of UAVs in intelligent transportation systems: A deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(11): 12382–12395. doi: 10.1109/TVT.2020.3023861.
[19]	AL-HOURANI A, KANDEEPAN S, and JAMALIPOUR A. Modeling air-to-ground path loss for low altitude platforms in urban environments[C]. 2014 IEEE Global Communications Conference, Austin, USA, 2014: 2898–2904.
[20]	SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[EB/OL]. https://arxiv.org/abs/1707.06347, 2017.