无线传感器网络的系统化自适应建模

无线传感器网络的系统能耗制约着全网络的综合应用能力，其中节点有限的能量从根本上影响着传感器网络效能。针对无线传感器网络的全局能耗问题，提出了基于径向基函数神经网络以及状态空间表达的系统化建模方法。考虑到无线传感器网络的拓扑结构与分级关系, 采用径向基函数神经网络自适应实时规划系统。鉴于各传感器节点对数据的不同处理方式与能耗密切相关, 对全系统能耗建立系统化矩阵模型。仿真分析表明该模型可根据实际应用背景调整设置完成全局优化。

Adaptive Systemic Modeling for Wireless Sensor Networks

Global energy consumption in wireless sensor networks restricts the application of the entire networks, including the impact of limited energy capacity of a single node to the system fundamentally. This paper presents a systemic modeling approach for wireless sensor network based on radial basis function neural networks and status-sphere expression. In consideration about the topology and hierarchical structure of WSN, it introduces real-time adjusting of radial basis function neural networks, and establishes matrix model for systematic energy consumption adaptively. Results prove that this model performs effective global optimization by adjusting parameters according to real application circumstances.