Spatio-temporal event detection using probabilistic graphical models (PGMs)

Abstract

Event detection concerns identifying occurrence of interesting events which are meaningful and understandable. In dynamic fields, as time passes the attribute of phenomenon varies in spatial locations. Detecting events in dynamic fields requires an approach to deal with the highly granular data arriving in real time. This paper proposes a spatiotemporal event detection algorithm in dynamic fields which are monitored by wireless sensor networks (WSNs). The algorithm provides a method using probabilistic graphical models (PGMs) in WSNs to cope with the uncertainty of sensor readings. The algorithm incorporates the ability of Markov chains in temporal dependency modelling and Markov random fiel..