Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Communication-Efficient Decentralized Cooperative Data Analytics in Sensor Networks

  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_66,
        author={Liang Zhao and Zhihua Li and Shujie Guo},
        title={Communication-Efficient Decentralized Cooperative Data Analytics in Sensor Networks},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Big data Data analytics Decentralized computing Sensor networks Asynchronous algorithm},
        doi={10.1007/978-3-030-00557-3_66}
    }
    
  • Liang Zhao
    Zhihua Li
    Shujie Guo
    Year: 2018
    Communication-Efficient Decentralized Cooperative Data Analytics in Sensor Networks
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_66
Liang Zhao1,*, Zhihua Li2,*, Shujie Guo2,*
  • 1: University of South Carolina Upstate
  • 2: Jiangnan University
*Contact email: lzhao2@uscupstate.edu, zhli@jiangnan.edu.cn, 1149165216@qq.com

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.