Sparse Modeling and Recursive Prediction of Space-Time Dynamics in Stochastic Sensor Networks
Sparse Modeling and Recursive Prediction of Space-Time Dynamics in Stochastic Sensor Networks
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DOI：
10.1109/tase.2015.2459068
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发表时间：
2016-01-01
影响因子：
5.6
通讯作者：
Yang, Hui
中科院分区：
文献类型：
--
作者：
Chen, Yun;Yang, Hui
无线传感器网络已成为环境传感器网络、战场监视网络、人体区域传感器网络等复杂系统时空动态监测的关键技术。然而，传感器故障在传统传感系统中并不罕见。因此，我们建议设计随机传感器网络，以允许网络中不同位置的传感器子集间歇性地传输动态信息。为了充分发挥随机传感器网络的潜力，需要开发新的信息处理算法来支持设计，并利用不确定信息进行决策。提出了一种基于稀疏粒子滤波的随机传感器网络大数据时空动态建模新方法。值得注意的是，我们开发了一个稀疏核加权回归模型来实现空间模式的简洁表示。然后，将空间模型参数转换为降维空间，当有新的传感器观测值时，利用递归贝叶斯估计对空间模型参数进行时序更新。因此，空间过程和时间过程是密切相互作用的。在实际数据和不同场景下随机传感器网络（即空间、时间和时空动态网络）的实验结果表明，稀疏粒子滤波在支持随机设计和利用不确定信息建模复杂系统的时空动态方面是有效的。
Wireless sensor network has emerged as a key technology for monitoring space-time dynamics of complex systems, e.g., environmental sensor network, battlefield surveillance network, and body area sensor network. However, sensor failures are not uncommon in traditional sensing systems. As such, we propose the design of stochastic sensor networks to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently. Realizing the full potential of stochastic sensor network hinges on the development of novel information-processing algorithms to support the design and exploit the uncertain information for decision making. This paper presents a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. Notably, we developed a sparse kernel-weighted regression model to achieve a parsimonious representation of spatial patterns. Further, the parameters of spatial model are transformed into a reduced-dimension space, and thereby sequentially updated with the recursive Bayesian estimation when new sensor observations are available over time. Therefore, spatial and temporal processes closely interact with each other. Experimental results on real-world data and different scenarios of stochastic sensor networks (i.e., spatially, temporally, and spatiotemporally dynamic networks) demonstrated the effectiveness of sparse particle filtering to support the stochastic design and harness the uncertain information for modeling space-time dynamics of complex systems.