Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks
Gareth William Peters, Ido Nevat, Shaowei Lin, Tomoko Matsui · 2014
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.