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Towards a discipline of geospatial distributed event based systems

Published: 16 July 2012 Publication History

Abstract

A geospatial system is one in which the state space includes one, two or three-dimensional space and time. A geospatial event is one in which an event impacts points in space over time. Examples of geospatial events include floods, tsunamis, earthquakes, and emission of toxic plumes. This paper discusses aspects of the theory of geospatial distributed event based systems (GDEBS). The paper describes algorithms for rapid detection of geospatial events which can be used on Cloud computing architectures, in which many servers collaborate to detect events by analyzing data streams from large numbers of sensors. Sensor noise and timing errors may result in false detection or missed detection as well as incorrect identification of event attributes such as the location of the event source. The paper presents mathematical analyses and simulations dealing with rapid event detection for geospatial events of varying speeds in the presence of substantial sensor noise and timing error. The paper also describes some of the algorithmic and machine-learning techniques for improving event detection in the Cloud with large numbers of noisy sensors. Experience with GDEBS using a seismic network is described.

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Cited By

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  • (2016)Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor NetworksACM Transactions on Sensor Networks10.1145/288296612:2(1-59)Online publication date: 15-Apr-2016
  • (2014)Community sense and response systemsCommunications of the ACM10.1145/262263357:7(66-75)Online publication date: 1-Jul-2014
  • (2013)A fresh perspectiveProceedings of the 12th international conference on Information processing in sensor networks10.1145/2461381.2461387(7-18)Online publication date: 8-Apr-2013

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cover image ACM Conferences
DEBS '12: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
July 2012
410 pages
ISBN:9781450313155
DOI:10.1145/2335484
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 July 2012

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Author Tags

  1. Bayesian estimation
  2. event detection
  3. geospatial data
  4. seismology
  5. sensor networks

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Cited By

View all
  • (2016)Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor NetworksACM Transactions on Sensor Networks10.1145/288296612:2(1-59)Online publication date: 15-Apr-2016
  • (2014)Community sense and response systemsCommunications of the ACM10.1145/262263357:7(66-75)Online publication date: 1-Jul-2014
  • (2013)A fresh perspectiveProceedings of the 12th international conference on Information processing in sensor networks10.1145/2461381.2461387(7-18)Online publication date: 8-Apr-2013

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