基于惩罚误差矩阵的同步预测无线体域网节能方法

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。

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

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.