计算机应用 ›› 2019, Vol. 39 ›› Issue (2): 513-517.DOI: 10.11772/j.issn.1001-9081.2018071478

• 计算机软件技术 • 上一篇    下一篇

基于惩罚误差矩阵的同步预测无线体域网节能方法

郑卓然1,2, 郑向伟1,2, 田杰1,2   

  1. 1. 山东师范大学 信息科学与工程学院, 济南 250358;
    2. 山东省分布式计算机软件新技术重点实验室(山东师范大学), 济南 250014
  • 收稿日期:2018-07-10 修回日期:2018-08-21 出版日期:2019-02-10 发布日期:2019-02-15
  • 通讯作者: 郑向伟
  • 作者简介:郑卓然(1992-),男,山东枣庄人,硕士研究生,CCF会员,主要研究方向:无线传感网络;郑向伟(1971-),男,山东泰安人,教授,博士,CCF会员,主要研究方向:计算智能、云计算;田杰(1985-),女,山东聊城人,博士,讲师,主要研究方向:无线传感器网络、无线资源管理。
  • 基金资助:
    国家自然科学基金资助项目(61373149);山东省自然科学基金青年基金项目(ZR2017QF008)。

Energy-saving method for wireless body area network based on synchronous prediction with penalty error matrix

ZHENG Zhuoran1,2, ZHENG Xiangwei1,2, TIAN Jie1,2   

  1. 1. School of Information Science and Engineering, Shandong Normal University, Jinan Shandong 250358, China;
    2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology(Shandong Normal University), Jinan Shandong 250014, China
  • Received:2018-07-10 Revised:2018-08-21 Online:2019-02-10 Published:2019-02-15
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61373149), the Youth Foundation of Shandong Natural Science Foundation (ZR2017QF008).

摘要: 针对传统无线体域网(WBAN)预测模型对感知数据预测精度低、计算量大、能耗高的问题,提出一种基于惩罚误差矩阵的自适应三次指数平滑算法。首先在感知节点与路由节点之间建立轻量级预测模型,其次采用地毯式搜索方式对预测模型进行参数优化处理,最后采用惩罚误差矩阵对预测模型参数作进一步的细粒化处理。实验结果表明,与ZigBee协议相比,在1000时隙范围内,所提方法可节省12%左右的能量;而采用惩罚误差矩阵与地毯式搜索方式相比,预测精度提高了3.306%。所提方法在有效降低计算复杂度的同时能进一步降低WBAN的能耗。

关键词: 无线体域网, 惩罚误差矩阵, 轻量级预测模型, 地毯式搜索, 体域网

Abstract: To solve the problem that traditional Wireless Body Area Network (WBAN) prediction model has low prediction accuracy, large computational complexity and high energy consumption, an adaptive cubic exponential smoothing algorithm based on penalty error matrix was proposed. Firstly, a lightweight prediction model was established between the sensing node and the routing node. Secondly, blanket search was used to optimize the parameters of the prediction model. Finally, penalty error matrix was used to further refine the parameters of the prediction model. The experimental results showed that compared with the ZigBee protocol, the proposed method saved about 12% energy in 1000 time slot range; compared with blanket search method, the prediction accuracy was improved by 3.306% by using penalty error matrix. The proposed algorithm can effectively reduce the computational complexity and further reduce the energy consumption of WBAN.

Key words: Wireless Body Area Network (WBAN), penalty error matrix, lightweight prediction model, blanket search, body area network

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