Abstract
WLANs are currently being considered for use in the context of a larger geographical area such as a city or a campus due to their convenience, cost efficiency, and ease of integration with other networks. Due to support large numbers of portable devices and their dynamic relocation, wide WLANs must face problems of location management and network resource allocation. In order to solve these challenges, future mobility information of all mobile users in the network is required to accurate estimation of network resource demands at future time toward more efficient network resource management. Therefore, mobility prediction has played a crucial role in the resource management of wide WLANs and it has attracted recently a great deal of research interests. However, since most of the current approaches are based on personal movement profile for predicting the next location of mobile users, these techniques may fail to make a prediction for new users or ones with movements on novel paths. In this paper, we propose a prediction model which is based on group mobility behaviors to deal with such the lack of information of individual movement histories. Our proposed prediction approach makes use of clustering techniques in data mining to classify mobility patterns of users into groups. Experiments will be performed to demonstrate that using group mobility behaviors may significantly enhance the accuracy of the mobility prediction.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-05939-6_37
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References
Kotz, D., Essien, K.: Analysis of a Campus-wide Wireless Network. In: Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (ACM MOBICOM’02), pp. 107–118 (2002)
Yavas, G., Katsaros, D., Ulusoy, O., Manolopoulos, Y.: A data mining approach for location prediction in mobile environments. J. Data Knowl. Eng. 54, 121–146 (2005)
Kang, J., Yong, H.-S.: A Frequent pattern based prediction model for moving objects. Int. J. Comput. Sci. Netw. Secur. 10(3), 200–205 (2010)
Lee, J.W., Paek, O.H., Ryu, K.H.: Temporal moving pattern mining for location-based service. J. Syst. Softw. 73, 481–490 (2004). (Elsevier)
Hung, C., Peng, W.: Clustering object moving patterns for prediction-based object tracking sensor networks. In: Proceedings of the 18th ACM conference on Information and knowledge management, pp. 1633–1636, USA (2009)
Avasthi, S., Dwivedi, A.: Prediction of mobile user behavior using clustering. Int. J. Sci. Res. Publ. 3(2), 1–5 (2013)
Bergh, A.E., Ventura, N.: Prediction assisted fast handovers for mobile IPv6. IEEE MILCOM 2006 Unclassified Technical Sessions, Washington D.C. (2006)
Duong, T.V., Tran, D.Q., Tran, C.H.: Spatiotemporal data mining for mobility prediction in wireless network. In: Proceedings of the National Conference of Fundamental and Applied IT Research (FAIR), pp. 224–235, Vietnam (2011)
Wanalertlak, W., Lee, B., Yu, C., Kim, M., Park, S.M., Kim, W.T.: Behavior-based mobility prediction for seamless handoff in mobile wireless networks. Wirel. Netw. 17, 645–658 (2011). (Springer Science + Business Media)
Duong, T.V., Tran, D.Q.: An effective approach for mobility prediction in wireless network based on temporal weighted mobility rule. Int. J. Comput. Sci. Telecommun. 3(2), 29–36 (2012)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090, USA (2011)
Somayeh, D.: Exploring movement similarity analysis of moving objects. Doctor thesis, University of Zurich, Switzerland (2011)
Yao, X.: Research issues in spatio-temporal data mining. In: Workshop on Geospatial Visualization and Knowledge Discovery, University Consortium for Geographic Information Science, Virginia (2003)
Shekhar, S., Vatsavai, R.R., Celik, M.: Spatial and Spatiotemporal Data Mining: Recent Advances. Symposium, Next Generation of Data Mining, pp. 549–584 (2008)
Duong, T.V., Tran, D.Q., Tran, C.H.: A weighted combination similarity measure for mobility patterns in wireless networks. Int. J. Comput. Netw. Commun. 4(3), 21–35 (2012)
Huang, K., Yu, T.H., Kao, T.: Analyzing structural changes using clustering techniques. Int. J. Innovative Comput. Inf. Control 4(5), 1195–1202 (2008)
Ranjan, M., Anna, D.P., Arka, P.G:. A systematic evaluation of different methods for initializing the K-means clustering algorithm. IEEE Trans. Knowl. Data Eng. 38, 522–537 (2010)
Hartigan, J.A., Wong, A.: A k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)
Berkhin, P.: A survey of clustering data mining techniques. Grouping Multidimensional Data in Grouping Multidimensional Data, pp. 25–71 (2006)
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© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Van T. Duong, T., Tran, D.Q., Tran, C.H. (2014). Data Mining Assisted Resource Management in Wide WLANs. In: Vinh, P., Alagar, V., Vassev, E., Khare, A. (eds) Context-Aware Systems and Applications. ICCASA 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-05939-6_32
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DOI: https://doi.org/10.1007/978-3-319-05939-6_32
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