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ToI-Based Data Utility Maximization for UAV-Assisted Wireless Sensor Networks

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Algorithmic Aspects in Information and Management (AAIM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15179))

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Abstract

Using Unmanned Aerial Vehicles as mobile base stations is a promising way to collect data from sensor nodes, especially for large-scale wireless sensor networks. Previous works mainly focus on improving the freshness of the collected data or the energy efficiency by scheduling UAVs. Considering the fact that the sensing data in some applications is time-sensitive, that is, the value of the sensing data is based on its Timeliness of Information (ToI), which decays over time. Therefore, in this paper, we investigate the UAV Trajectory optimization problem for Maximizing the ToI-based data utility (TMT). We propose an improved deep reinforcement learning-based algorithm to address the problem, and the experience results demonstrate the effectiveness of our designs.

This work is supported by the National Natural Science Foundation of China (with grants No. 62202016 and No. 62202055).

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Author information

Authors and Affiliations

  1. School of Computer Science, Beijing University of Technology, Beijing, China

    Qing Zhao, Zhen Li, Jianqiang Li & Xingjian Ding

  2. Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, China

    Jianxiong Guo

  3. School of Information, Renmin University of China, Beijing, China

    Deying Li

Authors
  1. Qing Zhao
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  2. Zhen Li
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  3. Jianqiang Li
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  4. Jianxiong Guo
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  5. Xingjian Ding
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  6. Deying Li
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Corresponding author

Correspondence to Xingjian Ding .

Editor information

Editors and Affiliations

  1. Santa Clara University, Santa Clara, CA, USA

    Smita Ghosh

  2. Zhejiang Normal University, Jinhua, China

    Zhao Zhang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhao, Q., Li, Z., Li, J., Guo, J., Ding, X., Li, D. (2024). ToI-Based Data Utility Maximization for UAV-Assisted Wireless Sensor Networks. In: Ghosh, S., Zhang, Z. (eds) Algorithmic Aspects in Information and Management. AAIM 2024. Lecture Notes in Computer Science, vol 15179. Springer, Singapore. https://doi.org/10.1007/978-981-97-7798-3_7

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  • DOI: https://doi.org/10.1007/978-981-97-7798-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-7797-6

  • Online ISBN: 978-981-97-7798-3

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