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How to Learn on the Fly? On Improving the Uplink Throughput Performance of UAV-Assisted Sensor Networks

Published: 19 June 2023 Publication History

Abstract

In UAV-assisted sensor networks, the random and unknown duty cycling of battery-powered ground Sensor Nodes (SNs) (such as wireless IoT devices) can significantly affect both energy consumption and communication performance of the data-collecting UAVs. The state of the art typically assumes a unified system with a coherent design of UAV and SNs, wherein the SNs' behavior is considered to be known, and accordingly the mobility of UAV is planned. In practical settings, however, SNs and UAVs can possibly be administered by independent stakeholders. In such an independent system, the UAV needs to learn (in online) its trajectory by understanding the random behavior of SNs in order to improve the uplink communication performance.
In the independently administered UAV-assisted sensor networks, the task of maximizing the uplink throughput performance can be formulated as a sequential stochastic decision process. The key challenge of this formulation lies in choosing suitable online learning methods and selecting network parameters that help to learn and improve the throughput performance effectively. To this end, this work gives a principled approach to studying the effects of various learning methods and ways to incorporate network parameters to improve the throughput performance. The resultant online learning algorithm is demonstrated to exhibit superior performance in the extensive simulation-based evaluation study.

References

[1]
Osama M. Bushnaq, Abdulkadir Celik, Hesham Elsawy, Mohamed-Slim Alouini, and Tareq Y. Al-Naffouri. 2019. Aeronautical Data Aggregation and Field Estimation in IoT Networks: Hovering and Traveling Time Dilemma of UAVs. IEEE Transactions on Wireless Communications 18, 10 (2019), 4620--4635.
[2]
Ricardo C. Carrano, Diego Passos, Luiz C. S. Magalhaes, and Celio V. N. Albuquerque. 2014. Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials 16, 1 (2014), 181--194.
[3]
Wanyi Li, Li Wang, and Aiguo Fei. 2019. Minimizing Packet Expiration Loss With Path Planning in UAV-Assisted Data Sensing. IEEE Wireless Communications Letters 8, 6 (2019), 1520--1523.
[4]
Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen, et al. 2018. A Tutorial on Thompson Sampling. Foundations and Trends® in Machine Learning 11, 1 (2018), 1--96.
[5]
Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press.
[6]
Zhiqing Wei, Mingyue Zhu, Ning Zhang, Lin Wang, Yingying Zou, Zeyang Meng, Huici Wu, and Zhiyong Feng. 2022. UAV-Assisted Data Collection for Internet of Things: A Survey. IEEE Internet of Things Journal 9, 17 (2022), 15460--15483.
[7]
Changsheng You and Rui Zhang. 2019. 3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting. IEEE Transactions on Wireless Communications 18, 6 (2019), 3192--3207.
[8]
Yongs Zeng, Qingqing Wu, and Rui Zhang. 2019. Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond. Proc. IEEE 107, 12 (2019), 2327--2375.
[9]
Cheng Zhan and Yong Zeng. 2019. Completion Time Minimization for Multi-UAV-Enabled Data Collection. IEEE Transactions on Wireless Communications 18, 10 (2019), 4859--4872.
[10]
Cheng Zhan, Yong Zeng, and Rui Zhang. 2018. Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network. IEEE Wireless Communications Letters 7, 3 (2018), 328--331.
[11]
Qing Zhao. 2020. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan and Claypool.

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cover image ACM Conferences
DroNet '23: Proceedings of the Ninth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications
June 2023
54 pages
ISBN:9798400702105
DOI:10.1145/3597060
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 19 June 2023

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Author Tags

  1. UAV
  2. uplink throughput
  3. MAB
  4. duty cycling
  5. online learning
  6. performance evaluation

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DroNet '23
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DroNet '23 Paper Acceptance Rate 8 of 8 submissions, 100%;
Overall Acceptance Rate 29 of 50 submissions, 58%

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