Sparse particle filtering for modeling space-time dynamics in stochastic sensor network

Yun Chen, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wireless sensor network has emerged as a key technology for monitoring space-time dynamics of complex systems, e.g., wearable electrocardiogram (ECG) sensor network. However, traditional ECG sensor networks demand reliable sensor readings through sensor-skin contacts, which greatly deteriorate the system comfortability and wearability. In order to realize a highly wearable sensing system, we propose a new strategy of stochastic sensor networks that relax the hardware constraints and allow a random subset of sensors to transmit information intermittently at dynamically varying locations within the network of sensors. Nonetheless, stochastic sensor network gives rise to spatially-temporally big data. Space-time interactions bring substantial complexity in the scope of the modeling. Hence, stochastic sensor network calls upon the development of novel analytical methods and tools to support the design and harness 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 distributed and stochastic sensor network. First, we utilized the parametric kernel-weighted regression model to represent spatial patterns. Further, the parameters of spatial model are transformed into a reduced-dimension space, and then sequentially updated with the recursive Bayesian estimation when new sensor observations are available over time. As such, spatial and temporal processes closely interact with each other. Experimental results on real-world data and different scenarios of stochastic sensor 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 languageEnglish (US)
Title of host publicationIIE Annual Conference and Expo 2015
PublisherInstitute of Industrial Engineers
Pages2484-2493
Number of pages10
ISBN (Electronic)9780983762447
StatePublished - Jan 1 2015
EventIIE Annual Conference and Expo 2015 - Nashville, United States
Duration: May 30 2015Jun 2 2015

Publication series

NameIIE Annual Conference and Expo 2015

Other

OtherIIE Annual Conference and Expo 2015
CountryUnited States
CityNashville
Period5/30/156/2/15

Fingerprint

Sensor networks
Sensors
Electrocardiography
Large scale systems
Computer hardware
Wireless sensor networks
Dynamic models
Skin
Decision making
Monitoring

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Chen, Y., & Yang, H. (2015). Sparse particle filtering for modeling space-time dynamics in stochastic sensor network. In IIE Annual Conference and Expo 2015 (pp. 2484-2493). (IIE Annual Conference and Expo 2015). Institute of Industrial Engineers.
Chen, Yun ; Yang, Hui. / Sparse particle filtering for modeling space-time dynamics in stochastic sensor network. IIE Annual Conference and Expo 2015. Institute of Industrial Engineers, 2015. pp. 2484-2493 (IIE Annual Conference and Expo 2015).
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Chen, Y & Yang, H 2015, Sparse particle filtering for modeling space-time dynamics in stochastic sensor network. in IIE Annual Conference and Expo 2015. IIE Annual Conference and Expo 2015, Institute of Industrial Engineers, pp. 2484-2493, IIE Annual Conference and Expo 2015, Nashville, United States, 5/30/15.

Sparse particle filtering for modeling space-time dynamics in stochastic sensor network. / Chen, Yun; Yang, Hui.

IIE Annual Conference and Expo 2015. Institute of Industrial Engineers, 2015. p. 2484-2493 (IIE Annual Conference and Expo 2015).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chen Y, Yang H. Sparse particle filtering for modeling space-time dynamics in stochastic sensor network. In IIE Annual Conference and Expo 2015. Institute of Industrial Engineers. 2015. p. 2484-2493. (IIE Annual Conference and Expo 2015).