Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks

Gareth Peters; Ido Nevat; Shaowei Lin; Tomoko Matsui

doi:10.1109/ISSNIP.2014.6827635

Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks

Gareth Peters, Ido Nevat, Shaowei Lin, Tomoko Matsui

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

We develop a new framework for explicitly modelling the threshold exceedence levels of the spatial stochastic process being monitored by a sensor network. Our framework also allows incorporating additional observed features as explanatory factors for the behaviour of the spatial stochastic process, and in particular the probability of exceedence of a user defined threshold level in any given region of space. Such a model has many practical applications for accurate decision making under uncertainty when the monitored process exceeds user specified critical thresholds.

Original language	English
Title of host publication	2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)
Publisher	IEEE
Pages	1-7
Number of pages	7
ISBN (Electronic)	9781479928439
DOIs	https://doi.org/10.1109/ISSNIP.2014.6827635
Publication status	Published - 9 Jun 2014
Event	2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing - Singapore, Singapore Duration: 21 Apr 2014 → 24 Apr 2014

Conference

Conference	2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing
Country/Territory	Singapore
City	Singapore
Period	21/04/14 → 24/04/14

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10.1109/ISSNIP.2014.6827635

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title = "Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks",

abstract = "We develop a new framework for explicitly modelling the threshold exceedence levels of the spatial stochastic process being monitored by a sensor network. Our framework also allows incorporating additional observed features as explanatory factors for the behaviour of the spatial stochastic process, and in particular the probability of exceedence of a user defined threshold level in any given region of space. Such a model has many practical applications for accurate decision making under uncertainty when the monitored process exceeds user specified critical thresholds.",

author = "Gareth Peters and Ido Nevat and Shaowei Lin and Tomoko Matsui",

year = "2014",

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booktitle = "2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)",

publisher = "IEEE",

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note = "2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing ; Conference date: 21-04-2014 Through 24-04-2014",

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Peters, G, Nevat, I, Lin, S & Matsui, T 2014, Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks. in 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, pp. 1-7, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, Singapore, 21/04/14. https://doi.org/10.1109/ISSNIP.2014.6827635

Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks. / Peters, Gareth; Nevat, Ido; Lin, Shaowei et al.
2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2014. p. 1-7.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks

AU - Peters, Gareth

AU - Nevat, Ido

AU - Lin, Shaowei

AU - Matsui, Tomoko

PY - 2014/6/9

Y1 - 2014/6/9

N2 - We develop a new framework for explicitly modelling the threshold exceedence levels of the spatial stochastic process being monitored by a sensor network. Our framework also allows incorporating additional observed features as explanatory factors for the behaviour of the spatial stochastic process, and in particular the probability of exceedence of a user defined threshold level in any given region of space. Such a model has many practical applications for accurate decision making under uncertainty when the monitored process exceeds user specified critical thresholds.

AB - We develop a new framework for explicitly modelling the threshold exceedence levels of the spatial stochastic process being monitored by a sensor network. Our framework also allows incorporating additional observed features as explanatory factors for the behaviour of the spatial stochastic process, and in particular the probability of exceedence of a user defined threshold level in any given region of space. Such a model has many practical applications for accurate decision making under uncertainty when the monitored process exceeds user specified critical thresholds.

U2 - 10.1109/ISSNIP.2014.6827635

DO - 10.1109/ISSNIP.2014.6827635

M3 - Conference contribution

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EP - 7

BT - 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

PB - IEEE

T2 - 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing

Y2 - 21 April 2014 through 24 April 2014

ER -

Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks

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