Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 71725266 |
| Pages (from-to) | 215-226 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Automation Science and Engineering |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2016 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
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Sparse modeling and recursive prediction of space-time dynamics in stochastic sensor networks. / Chen, Yun; Yang, Hui.
In: IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 1, 71725266, 01.2016, p. 215-226.Research output: Contribution to journal › Article
TY - JOUR
T1 - Sparse modeling and recursive prediction of space-time dynamics in stochastic sensor networks
AU - Chen, Yun
AU - Yang, Hui
PY - 2016/1
Y1 - 2016/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85006158427&partnerID=8YFLogxK
U2 - 10.1109/TASE.2015.2459068
DO - 10.1109/TASE.2015.2459068
M3 - Article
AN - SCOPUS:85006158427
VL - 13
SP - 215
EP - 226
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
SN - 1545-5955
IS - 1
M1 - 71725266
ER -