Abstract
One of the major challenges in wireless environments is the provision of Quality of service (QoS) guarantees that different applications demand considering the highly dynamic nature of these environments. User mobility prediction represents a key factor for providing a seamless delivery of multimedia applications over wireless networks. Most of the existing approaches for mobility prediction presume that users travel in a-priori known pattern with some regularity; an assumption that may not always hold (e.g., a tourist in a foreign city). This paper presents a novel architecture of a mobility prediction agent (MPA) that accurately performs mobility prediction using knowledge of user’s preferences, goals, and spatial information without imposing any assumptions about the availability of his movements history. Using concepts of evidential reasoning of Dempster-Shafer’s theory, the MPA captures the uncertainty of the user’s navigation behavior by gathering pieces of evidence concerning different groups of candidate future locations. These groups are then refined to predict the user’s future location when evidence accumulate using Dempster rule of combination.
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References
Kleinrock, L.: Nomadicity: Anytime, Anywhere In A Disconnected World. Mobile Networks and Applications 1(4), 351–357 (1996)
Samaan, N., Karmouch, A.: Prediction-Based Policy Adaptation for QoS Management in Wireless Networks. In: POLICY 2003, Italy (June 2003)
Tabbane, S.: An alternative strategy for location tracking. IEEE Journal on Selected Areas in Communications 13 (June 1995)
Liu, G., Maguire, G.: A Class of Mobile Motion Prediction Algorithms for WirelessMobile Computing and Communication. ACM International Journal on Wireless Networks (1996)
Liu, G., Maguire, G.: A predictive mobility management algorithm for wireless mobile computing and communications. In: 4th IEEE Int. Conf. on Universal Personal Communications, November 1995, pp. 268–272 (1995)
Orwant, J.: Doppelganger goes to school: Machine learning for user modeling. Master’s thesis, MIT Media Laboratory (September 1993)
Bhattacharya, A., Das, S.K.: Lezi-update: An information-theoretic approach to track mobile users in PCS networks. In: Mobile Computing and Networking, pp. 1–12 (1999)
Ashbrook, D., Starner, T.: Learning Significant Locations and Predicting User Movement with GPS. In: Proc. of ISWC 2002, Seattle, WA (October 2002)
Schmandt, C., Marmasse, N.: A User-Centered Location Model. Personal and Ubiquitous Computing 6(5-6), 318–321 (2002)
Kumar, V., Venkataram, P.: A Prediction based Location Management using Multi-Layer Neural Networks. Jl. of IISc. 82(1), 7–21 (2002)
Chan, J., Zhou, S., Seneviratne, A.: A Qos Adaptive Mobility Prediction Scheme for Wireless Networks. In: IEEE GLOBECOM 1998, November 1998 (October 1998)
Soh, W.-S., Kim, H.S.: QoS Provisioning in Cellular Networks Based on Mobility Prediction Techniques. IEEE Communications Magazine, 86–92 (January 2003)
Schmidt-Belz, B., Makelainen, M., Nick1, A., Poslad, S.: Intelligent brokering of tourism services for mobile users. In: ENTER 2002 (2002)
Dempster, P., A.: AGeneralization of Bayseian Inference. Journal of Royal Statistics Society Series 30, 205–247 (1968)
Shafer, G.: A Mathematical theory of evidence ch. 3. Princeton Universal Press, Princeton (1975)
Kettani, D., Moulin, B.: A Spatial Model Based on the Notions of Spatial Conceptual Map and of Object’s Influence Areas. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 401–416. Springer, Heidelberg (1999)
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Samaan, N., Karmouch, A. (2003). An Evidence-Based Mobility Prediction Agent Architecture. In: Horlait, E., Magedanz, T., Glitho, R.H. (eds) Mobile Agents for Telecommunication Applications. MATA 2003. Lecture Notes in Computer Science, vol 2881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39646-8_22
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DOI: https://doi.org/10.1007/978-3-540-39646-8_22
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