International Workshop on Ubiquitous Body Sensor Networks

Research Article

Prediction-based data transmission for energy conservation in wireless body sensors

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  • @INPROCEEDINGS{10.4108/ICST.WICON2010.8543,
        author={Feng Xia and Zhenzhen Xu and Lin Yao and Weifeng Sun and Mingchu Li},
        title={Prediction-based data transmission for energy conservation in wireless body sensors},
        proceedings={International Workshop on Ubiquitous Body Sensor Networks},
        publisher={IEEE},
        proceedings_a={UBSN},
        year={2010},
        month={4},
        keywords={Body sensor networks Data communication Energy conservation Humans Medical services Monitoring Prediction algorithms Sensor phenomena and characterization Wearable sensors Wireless sensor networks},
        doi={10.4108/ICST.WICON2010.8543}
    }
    
  • Feng Xia
    Zhenzhen Xu
    Lin Yao
    Weifeng Sun
    Mingchu Li
    Year: 2010
    Prediction-based data transmission for energy conservation in wireless body sensors
    UBSN
    IEEE
    DOI: 10.4108/ICST.WICON2010.8543
Feng Xia1,*, Zhenzhen Xu1, Lin Yao1, Weifeng Sun1, Mingchu Li1
  • 1: School of Software, Dalian University of Technology, Dalian 116620, China
*Contact email: f.xia@ieee.org

Abstract

Wireless body sensors are becoming popular in healthcare applications. Since they are either worn or implanted into human body, these sensors must be very small in size and light in weight. The energy consequently becomes an extremely scarce resource, and energy conservation turns into a first class design issue for body sensor networks (BSNs). This paper deals with this issue by taking into account the unique characteristics of BSNs in contrast to conventional wireless sensor networks (WSNs) for e.g. environment monitoring. A prediction-based data transmission approach suitable for BSNs is presented, which combines a dual prediction framework and a low-complexity prediction algorithm that takes advantage of PIF (proportional-integral-derivative) control. Both the framework and the algorithm are generic, making the proposed approach widely applicable. The effectiveness of the approach is verified through simulations using real-world health monitoring datasets.