Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Clustering-Based Edge Compression Method with Application to Electromagnetic Object Recognition

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2294473,
        author={Ziyan  Yan and qibin  zheng and yun  lin and Qingjiang  Shi},
        title={Clustering-Based Edge Compression Method with Application to Electromagnetic Object Recognition},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={deep learning object detection edge compression clustering},
        doi={10.4108/eai.27-8-2020.2294473}
    }
    
  • Ziyan Yan
    qibin zheng
    yun lin
    Qingjiang Shi
    Year: 2020
    Clustering-Based Edge Compression Method with Application to Electromagnetic Object Recognition
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2294473
Ziyan Yan1,*, qibin zheng1, yun lin2, Qingjiang Shi1
  • 1: Tongji University
  • 2: Harbin Engineering University
*Contact email: 1831577@tongji.edu.cn

Abstract

Collaborative intelligence has attracted more and more attention. By properly portioning deep neural networks (DNN) and distributing the DNN calculation to the edge and cloud, we could reduce the prediction delay and power consumption to meet the actual application requirements. Toward this direction, this paper proposes an edge compression method based on clustering to address the issue of high data communication cost and time delay between the edge and cloud. Specifically, by using K-means clustering algorithm, this method compresses the output layer of the edge DNN, reducing the amount of transmission data and thus the delay and energy consumption. Based on the compression-based edge-cloud collaboration paradigm, we propose a distributed inference scheme for electromagnetic object recognition. The simulation results show that the proposed method can greatly reduce communication cost while maintaining the prediction performance.