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Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings

Research Article

Fog-Based Data Offloading in UWSNs with Discounted Rewards: A Contextual Bandit

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  • @INPROCEEDINGS{10.1007/978-3-030-99200-2_38,
        author={Yuchen Shan and Hui Wang and Zihao Cao and Yujie Sun and Ting Li},
        title={Fog-Based Data Offloading in UWSNs with Discounted Rewards: A Contextual Bandit},
        proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings},
        proceedings_a={CHINACOM},
        year={2022},
        month={4},
        keywords={Contextual bandit Collaborative offloading Dynamic fog computing Urban wireless sensor network},
        doi={10.1007/978-3-030-99200-2_38}
    }
    
  • Yuchen Shan
    Hui Wang
    Zihao Cao
    Yujie Sun
    Ting Li
    Year: 2022
    Fog-Based Data Offloading in UWSNs with Discounted Rewards: A Contextual Bandit
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-99200-2_38
Yuchen Shan1, Hui Wang1,*, Zihao Cao1, Yujie Sun1, Ting Li1
  • 1: School of Mathematics and Computer Science
*Contact email: hwang@zjnu.cn

Abstract

Urban wireless sensor networks (UWSNs) are an important application scenario for the Internet of Things (IoT). Nevertheless, applications based on urban environments are often computationally intensive, and sensor nodes are resource-constrained and heterogeneous. Fog computing has the potential to liberate the computation-intensive mobile nodes through data offloading. Therefore, reliable data collection and scalable coordination based on fog computing are seen as a challenge. In this paper, the challenge of data offloading is modeled as a contextual bandit problem—an important extension of the multi-armed bandit. First, the heterogeneity of the sensor nodes is used as contextual information, allowing the network to complete data collection at a small computational cost. Second, an ever-changing environmental scenario is considered in which the distribution of re-wards is not fixed, but varies over time. Based on this non-stationary bandit model, we propose a contextual bandit algorithm NCB-rDO in order to improve the success rate of data offloading, which solves the problem of data loss when the contextual information changes suddenly. Experimental results demonstrate the effectiveness and robustness of this data offloading algorithm.

Keywords
Contextual bandit Collaborative offloading Dynamic fog computing Urban wireless sensor network
Published
2022-04-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99200-2_38
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