关键词: 传感器网络, 动态概率模型, 粒子滤波, 高斯粒子滤波, 高斯和粒子滤波

Abstract:

Sensor data is modeled and Gaussian sum particle filter method is used for probabilistic inference based on model-driven methods for saving energy. First, the correlations on different sensor nodes and constructed probabilistic models from historical data are exploited. Then, the particle filter method is adopted to infer values for one sensor node from values acquired from real world by other sensor nodes, which saves energy of sensors efficiently. Finally, according to the fact that the sensor data generally satisfies Gaussian distribution, Gaussian particle filters and Gaussian sum particle filters are utilized for probabilistic inference respectively. Experiments show that the proposed Gaussian sum particle filter method is of high accuracy and efficiency.

Key words: wireless sensor networks, dynamic probabilistic models, particle filter, Gaussian particle filter, Gaussian sum particle filter