基于惩罚误差矩阵的同步预测无线体域网节能方法
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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Zig Bee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。
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 Zig Bee protocol,the proposed method saved about 12% energy in 1 000 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.
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 Zig Bee protocol,the proposed method saved about 12% energy in 1 000 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.
引文
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[12]张皛,邬春学,陈凯明.基于负载均衡的Zig Bee动态路由优化算法[J].计算机工程,2016,42(3):138-142.(ZHANG X,WU C X,CHEN K M.Zig Bee dynamic routing optimization algorithm based on load balance[J].Computer Engineering,2016,42(3):138-142.)
[13]王国权,王森,刘华勇,等.基于自适应的动态三次指数平滑法的风电场风速预测[J].电力系统保护与控制,2014(15):117-122.(WANG G Q,WANG S,LIU H Y,et al.Self-adaptive and dynamic cubic ES method for wind speed forecasting[J].Power System Protection and Control,2014(15):117-122.)
[2]CHEN S-L,LEE H-Y,CHEN C-A,et al.Wireless body sensor network with adaptive low-power design for biometrics and healthcare applications[J].IEEE Systems Journal,2010,3(4):398-409.
[3]PENG Y,WANG X,GUO L,et al.An efficient network codingbased fault-tolerant mechanism in WBAN for smart healthcare monitoring systems[J].Applied Sciences,2017,7(8):817.
[4]KHATUN F,HEYWOOD A E,HANIFI S M A,et al.Gender differentials in readiness and use of m Health services in a rural area of Bangladesh[J].BMC Health Services Research,2017,17(1):573.
[5]CHUNG K,PARK R C.PHR open platform based smart health service using distributed object group framework[J].Cluster Computing,2016,19(1):505-517.
[6]GREEN M L,RUFF T R.Why do residents fail to answer their clinical questions?A qualitative study of barriers to practicing evidence-based medicine[J].Academic Medicine Journal of the Association of American Medical Colleges,2005,80(2):176-182.
[7]FRANTZIDIS C A,GILOU S,BILLIS A,et al.Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens[J].Healthcare Technology Letters,2017,3(1):41-45.
[8]RANI S,MALHOTRA S,SANGWAN V.Detailed study of RS-232serial interface[J].Elena F Pérez Carrillo,2014:417-426.
[9]KIM Y,LEE S,LEE S.Coexistence of Zig Bee-based WBAN and Wi Fi for health telemonitoring systems[J].IEEE Journal of Biomedical&Health Informatics,2015,20(1):222-230.
[10]王汝言,翟美玲,吴大鹏.就带有同步预测的WBAN时序数据融合算法[J].通信学报,2015,36(6):13-21.(WANG RY,ZHAI M L,WU D P,Time series data aggregation algorithm with synchronous prediction for WBAN[J].Journal on Communications,2015,36(6):13-21.)
[11]梁正友,姚玉梅.IEEE 802.15.6中能量有效的无线体域网拓扑结构优化研究[J].通信学报,2016,37(6):1-10.(LIANGZ Y,YAO Y M.Study of energy efficient WBAN topology optimization in IEEE 802.15.6[J].Journal on Communications,2016,37(6):13-21.)
[12]张皛,邬春学,陈凯明.基于负载均衡的Zig Bee动态路由优化算法[J].计算机工程,2016,42(3):138-142.(ZHANG X,WU C X,CHEN K M.Zig Bee dynamic routing optimization algorithm based on load balance[J].Computer Engineering,2016,42(3):138-142.)
[13]王国权,王森,刘华勇,等.基于自适应的动态三次指数平滑法的风电场风速预测[J].电力系统保护与控制,2014(15):117-122.(WANG G Q,WANG S,LIU H Y,et al.Self-adaptive and dynamic cubic ES method for wind speed forecasting[J].Power System Protection and Control,2014(15):117-122.)