Title :
Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks
Author :
Peters, Gunnar ; Nevat, Ido ; Shaowei Lin ; Matsui, Takashi
Author_Institution :
Dept. of Stat. Sci., Univ. Coll. London, London, UK
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.
Keywords :
decision making; stochastic processes; wireless sensor networks; WSN; decision making; modelling threshold exceedence; monitored process; observed features; spatial stochastic processes; wireless sensor network; Estimation; Mathematical model; Monitoring; Polynomials; Standards; Stochastic processes; Wireless sensor networks; Extreme Value Theory; Generalized Pareto models; Quantile regression; Sensor Network; Stochastic processes;
Conference_Titel :
Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4799-2842-2
DOI :
10.1109/ISSNIP.2014.6827635
