Intelligent instance selection of data streams for smart sensor applications

Magdiel Galan, Huan Liu, Kari Torkkola

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

6 Citations (Scopus)

Abstract

The purpose of our work is to mine streaming data from a variety of hundreds of automotive sensors in order to develop methods to minimize driver distraction from in-vehicle communications and entertainment systems such as audio/video devices, cellphones, PDAs, Fax, eMail, and other messaging devices. Our endeavor is to create a safer driving environment, by providing assistance in the form of warning, delaying, or re-routing, incoming signals if the assistance system detects that the driver is performing, or is about to perform, a critical maneuver, such as passing, changing lanes, making a turn, or during a sudden evasive maneuver. To accomplish this, our assistance system relies on maneuver detection by continuously evaluating various embedded vehicle sensors, such as speed, steering, acceleration, lane distance, and many others, combined into representing an instance of the "state" of the vehicle. One key issue is how to effectively and efficiently monitor many sensors with constant data streams. Data streams have their unique characteristics and may produce data that is not relevant or pertinent to a maneuver. We propose an adaptive sampling method that takes advantage of these unique characteristics and develop algorithms that attempt to select relevant and important instances to determine which sensors to monitor and how to provide quick and effective responses to this type of mission critical situations. This work can be extended to many similar sensor applications with data streams.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsK.L. Priddy
Pages108-119
Number of pages12
Volume5803
DOIs
StatePublished - 2005
EventIntelligent Computing: Theory and Applications III - Orlando, FL, United States
Duration: Mar 28 2005Mar 29 2005

Other

OtherIntelligent Computing: Theory and Applications III
CountryUnited States
CityOrlando, FL
Period3/28/053/29/05

Fingerprint

Smart sensors
maneuvers
sensors
Sensors
vehicles
facsimile communication
Facsimile
warning
Personal digital assistants
Electronic mail
communication
sampling
Sampling
Communication

Keywords

  • Adaptive Sampling
  • Data Mining
  • Data Streams
  • Instance Selection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Galan, M., Liu, H., & Torkkola, K. (2005). Intelligent instance selection of data streams for smart sensor applications. In K. L. Priddy (Ed.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 5803, pp. 108-119). [14] https://doi.org/10.1117/12.605855

Intelligent instance selection of data streams for smart sensor applications. / Galan, Magdiel; Liu, Huan; Torkkola, Kari.

Proceedings of SPIE - The International Society for Optical Engineering. ed. / K.L. Priddy. Vol. 5803 2005. p. 108-119 14.

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

Galan, M, Liu, H & Torkkola, K 2005, Intelligent instance selection of data streams for smart sensor applications. in KL Priddy (ed.), Proceedings of SPIE - The International Society for Optical Engineering. vol. 5803, 14, pp. 108-119, Intelligent Computing: Theory and Applications III, Orlando, FL, United States, 3/28/05. https://doi.org/10.1117/12.605855
Galan M, Liu H, Torkkola K. Intelligent instance selection of data streams for smart sensor applications. In Priddy KL, editor, Proceedings of SPIE - The International Society for Optical Engineering. Vol. 5803. 2005. p. 108-119. 14 https://doi.org/10.1117/12.605855
Galan, Magdiel ; Liu, Huan ; Torkkola, Kari. / Intelligent instance selection of data streams for smart sensor applications. Proceedings of SPIE - The International Society for Optical Engineering. editor / K.L. Priddy. Vol. 5803 2005. pp. 108-119
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