Abstract
Next location prediction anticipates a person’s movement based on the history of previous sojourns. It is useful for proactive actions taken to assist the person in an ubiquitous environment. This paper evaluates next location prediction methods: dynamic Bayesian network, multi-layer perceptron, Elman net, Markov predictor, and state predictor. For the Markov and state predictor we use additionally an optimization, the confidence counter. The criterions for the comparison are the prediction accuracy, the quantity of useful predictions, the stability, the learning, the relearning, the memory and computing costs, the modelling costs, the expandability, and the ability to predict the time of entering the next location. For evaluation we use the same benchmarks containing movement sequences of real persons within an office building.
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References
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)
Bhattacharya, A., Das, S.K.: LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks. Wireless Networks 8, 121–135 (2002)
Chen, I.-C.K., Coffey, J.T., Mudge, T.N.: Analysis of Branch Prediction via Data Compression. In: ASPLOS VII, Cambridge, Massachusetts, USA, October 1996, pp. 128–137 (1996)
Gopalratnam, K., Cook, D.J.: Active LeZi: An Incremental Parsing Algorithm for Sequential Prediction. In: Sixteenth International Florida Artificial Intelligence Research Society Conference, St. Augustine, Florida, USA, May 2003, pp. 38–42 (2003)
Kaowthumrong, K., Lebsack, J., Han, R.: Automated Selection of the Active Device in Interactive Multi-Device Smart Spaces. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, Springer, Heidelberg (2002)
Mayrhofer, R.: An Architecture for Context Prediction. In: Advances in Pervasive Computing. Austrian Computer Society (OCG) (April 2004)
Mozer, M.C.: The Neural Network House: An Environment that Adapts to its Inhabitants. In: AAAI Spring Symposium on Intelligent Environments, pp. 110–114. AAAI Press, Menlo Park (1998)
Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low-Level Sensors. In: 5th International Conference on Ubiquitous Computing, Seattle, WA, USA, pp. 73–89 (2003)
Petzold, J.: Augsburg Indoor Location Tracking Benchmarks. Context Database, Institute of Pervasive Computing, University of Linz, Austria (January 2005), http://www.soft.uni-linz.ac.at/Research/Context_Database/index.php
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Global and Local Context Prediction. In: Artificial Intelligence in Mobile Systems 2003 (AIMS 2003), Seattle, WA, USA (October 2003)
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Confidence Estimation of the State Predictor Method. In: 2nd European Symposium on Ambient Intelligence, Eindhoven, The Netherlands, November 2004, pp. 375–386 (2004)
Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of Indoor Movements Using Bayesian Networks. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, Springer, Heidelberg (2005)
Ross, S.M.: Introduction to Probability Models. Academic Press, London (1985)
Trumler, W., Bagci, F., Petzold, J., Ungerer, T.: Smart Doorplate. In: First International Conference on Appliance Design (1AD), Bristol, GB (May 2003), Reprinted in Pers Ubiquit Comput. 7, 221–226 (2003)
Vintan, L., Gellert, A., Petzold, J., Ungerer, T.: Person Movement Prediction Using Neural Networks. In: First Workshop on Modeling and Retrieval of Context, Ulm, Germany (September 2004)
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Petzold, J., Bagci, F., Trumler, W., Ungerer, T. (2006). Comparison of Different Methods for Next Location Prediction. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds) Euro-Par 2006 Parallel Processing. Euro-Par 2006. Lecture Notes in Computer Science, vol 4128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823285_96
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DOI: https://doi.org/10.1007/11823285_96
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