Prediction of Indoor Movements Using Bayesian Networks

Abstract. This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building visiting offices in a regular fashion over some period of time. We model the scenario by a dynamic Bayesian network and evaluate accuracy of next room prediction and of duration of stay, training and retraining performance, as well as memory and performance requirements of a Bayesian network predictor. The results are compared with further context predictor approaches - a state predictor and a multi-layer perceptron predictor using exactly the same evaluation set-up and benchmarks. The publicly available Augsburg Indoor Location Tracking Benchmarks are applied as predictor loads. Our results show that the Bayesian network predictor reaches a next location prediction accuracy of up to 90% and a duration prediction accuracy of up to 87% with variations depending on the person and specific predictor set-up. The Ba...

• Type: Conference
• Year: 2005
• Where: LOCA
• Authors: Jan Petzold, Andreas Pietzowski, Faruk Bagci, Wolfgang Trumler, Theo Ungerer
• Keywords: Bayesian Network Predictor, LOCA 2005, Prediction, State Predictor