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 Bayesian network predictor performs in the same accuracy range as the neural network and the state predictor.
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Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T. (2005). Prediction of Indoor Movements Using Bayesian Networks. In: Strang, T., Linnhoff-Popien, C. (eds) Location- and Context-Awareness. LoCA 2005. Lecture Notes in Computer Science, vol 3479. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11426646_20
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DOI: https://doi.org/10.1007/11426646_20
Publisher Name: Springer, Berlin, Heidelberg
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