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Communication-Efficient Decentralized Cooperative Data Analytics in Sensor Networks

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Machine Learning and Intelligent Communications (MLICOM 2018)

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Abstract

This paper presents a novel approach enabling communication-efficient decentralized data analytics in sensor networks. The proposed method aims to solve the decentralized consensus problem in a network such that all the nodes try to estimate the parameters of the global model and they should reach an agreement on the value of the model eventually. Our algorithm leverages broadcasting communication and is performed in a asynchronous manner in the sense that each node can update its estimate independent of others. All the nodes in the network can run the same algorithm in parallel and no synchronization is required. Numerical experiments demonstrate that the proposed algorithm outperforms the benchmark, and it is a promising approach for big data analytics in sensor networks.

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Authors and Affiliations

  1. University of South Carolina Upstate, Spartanburg, SC, 29303, USA

    Liang Zhao

  2. Jiangnan University, Wuxi, 214122, Jiangsu, China

    Zhihua Li & Shujie Guo

Authors
  1. Liang Zhao
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  2. Zhihua Li
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  3. Shujie Guo
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Corresponding author

Correspondence to Zhihua Li .

Editor information

Editors and Affiliations

  1. Zhejiang University of Technology, Hangzhou Shi, China

    Limin Meng

  2. Department of Networks, Simula Research Laboratory, Oslo, Norway

    Yan Zhang

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhao, L., Li, Z., Guo, S. (2018). Communication-Efficient Decentralized Cooperative Data Analytics in Sensor Networks. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_66

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  • DOI: https://doi.org/10.1007/978-3-030-00557-3_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00556-6

  • Online ISBN: 978-3-030-00557-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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