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Learning-Based Privacy-Aware Maritime IoT Communications

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Reinforcement Learning for Maritime Communications

Part of the book series: Wireless Networks ((WN))

Abstract

Mobile edge computing helps maritime IoT devices with energy harvesting to provide satisfactory experiences for computation-intensive applications in maritime communication systems, such as real-time cargo status notification, emergency rescue in maritime affairs, and accurate early warning. In this chapter, we present an RL-based privacy-aware offloading scheme to help maritime IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a maritime IoT device to choose the offloading rate that improves the computation performance, protects user privacy, and saves the energy of the IoT devices without being aware of the privacy leakage, energy consumption, and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. Furthermore, a post-decision state learning method uses the known channel state model to improve the offloading performance. The performance bound of this scheme is provided regarding the privacy level, the energy consumption, and the computation latency for three typical ship offloading scenarios.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Engineering, Xiamen University, Xiamen, Fujian, China

    Liang Xiao & Helin Yang

  2. Wireless Communication Networks, University of Waterloo, Waterloo, ON, Canada

    Weihua Zhuang

  3. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China

    Minghui Min

Authors
  1. Liang Xiao
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  2. Helin Yang
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  3. Weihua Zhuang
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  4. Minghui Min
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Xiao, L., Yang, H., Zhuang, W., Min, M. (2023). Learning-Based Privacy-Aware Maritime IoT Communications. In: Reinforcement Learning for Maritime Communications. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-32138-2_3

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  • DOI: https://doi.org/10.1007/978-3-031-32138-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32137-5

  • Online ISBN: 978-3-031-32138-2

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