Predictive Location Tracking in Cellular and in Ad Hoc Wireless Networks

The proliferation of cellular and ad hoc networks and the penetration of Internet services are changing many aspects of ubiquitous mobile computing. Constantly increasing mobile client populations utilize diverse mobile devices to access the wireless medium and various heterogeneous applications (e.g., streaming video, Web) are being developed to satisfy the eager client requirements. The realization of such a demanding environment requires addressing many technical issues, related to radio management, networking, data management and so on. Most of the challenging issues and problems in this area are due to the fact that the underlying environment is extremely resource-starving and inherently uncertain. For instance, the wireless communication channels are bandwidth-limited and error prone. The uncertainty due to node (user) mobility has fundamental impacts, since it induces uncertainly in the network topology and hence causes problems in routing and in data delivery. Additionally, traffic load and resource demands in cellular and in ad hoc wireless networks are also uncertain, depending a lot on the user trajectories. In this harsh environment, seamless and ubiquitous connectivity is a fundamental goal. This goal calls for smart techniques for determining the current and future location of a mobile. The ability to determine the mobile client’s (future) location can significantly improve the wireless network’s overall performance. Consider for

[1]  Yufei Tao,et al.  Continuous Nearest Neighbor Search , 2002, VLDB.

[2]  Yannis Manolopoulos,et al.  Prediction in wireless networks by Markov chains , 2009, IEEE Wireless Communications.

[3]  Yufei Tao,et al.  The Bdual-Tree: indexing moving objects by space filling curves in the dual space , 2008, The VLDB Journal.

[4]  Christian S. Jensen,et al.  Indexing the Positions of Continuously Moving Objects , 2000, SIGMOD Conference.

[5]  Dana Ron,et al.  The power of amnesia: Learning probabilistic automata with variable memory length , 1996, Machine Learning.

[6]  Pankaj K. Agarwal,et al.  Indexing Moving Points , 2003, J. Comput. Syst. Sci..

[7]  Pankaj K. Agarwal,et al.  STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects , 2002, ALENEX.

[8]  Barry Smyth,et al.  Mobile web surfing is the same as web surfing , 2006, Commun. ACM.

[9]  Özgür Ulusoy,et al.  A Quadtree-Based Dynamic Attribute Indexing Method , 1998, Comput. J..

[10]  Sajal K. Das,et al.  LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks , 2002, Wirel. Networks.

[11]  Kyriakos Mouratidis,et al.  Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring , 2005, SIGMOD '05.

[12]  Beng Chin Ooi,et al.  Query and Update Efficient B+-Tree Based Indexing of Moving Objects , 2004, VLDB.

[13]  Dik Lun Lee,et al.  Semantic Caching in Location-Dependent Query Processing , 2001, SSTD.

[14]  Alberto Apostolico,et al.  Optimal Amnesic Probabilistic Automata or How to Learn and Classify Proteins in Linear Time and Space , 2000, J. Comput. Biol..

[15]  Chin-Wan Chung,et al.  Selectivity estimation for spatio-temporal queries to moving objects , 2002, SIGMOD '02.

[16]  Hanan Samet,et al.  Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates , 2003, VLDB.

[17]  Stathes Hadjiefthymiades,et al.  Enhanced path prediction for network resource management in wireless LANs , 2003, IEEE Wireless Communications.

[18]  Christian S. Jensen,et al.  Nearest neighbor and reverse nearest neighbor queries for moving objects , 2002, Proceedings International Database Engineering and Applications Symposium.

[19]  Hanan Samet,et al.  The Design and Analysis of Spatial Data Structures , 1989 .

[20]  Ken C. K. Lee,et al.  Nearest Surrounder Queries , 2006, IEEE Transactions on Knowledge and Data Engineering.

[21]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.

[22]  David K. Y. Yau,et al.  On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication , 2006, IEEE Trans. Mob. Comput..

[23]  Pankaj K. Agarwal,et al.  Time Responsive External Data Structures for Moving Points , 2001, WADS.

[24]  Charu C. Aggarwal,et al.  On nearest neighbor indexing of nonlinear trajectories , 2003, PODS '03.

[25]  Yannis Manolopoulos,et al.  A Data Mining Algorithm for Generalized Web Prefetching , 2003, IEEE Trans. Knowl. Data Eng..

[26]  S. Tabbane,et al.  Location management methods for third-generation mobile systems , 1997, IEEE Commun. Mag..

[27]  P. Krishnan,et al.  Optimal prefetching via data compression , 1996, JACM.

[28]  Jignesh M. Patel,et al.  STRIPES: an efficient index for predicted trajectories , 2004, SIGMOD '04.

[29]  Yufei Tao,et al.  Venn sampling: a novel prediction technique for moving objects , 2005, 21st International Conference on Data Engineering (ICDE'05).

[30]  Xin Chen,et al.  A Popularity-Based Prediction Model for Web Prefetching , 2003, Computer.

[31]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[32]  Hiroyuki Kitagawa,et al.  Continual neighborhood tracking for moving objects using adaptive distances , 2002, Proceedings International Database Engineering and Applications Symposium.

[33]  George Kollios,et al.  Performance evaluation of spatio-temporal selectivity estimation techniques , 2003, 15th International Conference on Scientific and Statistical Database Management, 2003..

[34]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[35]  Ken C. K. Lee,et al.  Tracking Nearest Surrounders in Moving Object Environments , 2006, 2006 ACS/IEEE International Conference on Pervasive Services.

[36]  Ravi Jain,et al.  Evaluating location predictors with extensive Wi-Fi mobility data , 2004, INFOCOM.

[37]  Christos Faloutsos,et al.  Prediction and indexing of moving objects with unknown motion patterns , 2004, SIGMOD '04.

[38]  Abraham Lempel,et al.  Compression of individual sequences via variable-rate coding , 1978, IEEE Trans. Inf. Theory.

[39]  Christian S. Jensen,et al.  Indexing of moving objects for location-based services , 2002, Proceedings 18th International Conference on Data Engineering.

[40]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[41]  Yannis Manolopoulos,et al.  Fast Nearest-Neighbor Query Processing in Moving-Object Databases , 2003, GeoInformatica.

[42]  Tong Liu,et al.  Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks , 1998, IEEE J. Sel. Areas Commun..

[43]  Douglas Comer,et al.  Ubiquitous B-Tree , 1979, CSUR.

[44]  Yufei Tao,et al.  Location-based spatial queries , 2003, SIGMOD '03.

[45]  Dimitrios Gunopulos,et al.  Nearest Neighbor Queries in a Mobile Environment , 1999, Spatio-Temporal Database Management.

[46]  John G. Cleary,et al.  Unbounded Length Contexts for PPM , 1997 .

[47]  Neri Merhav,et al.  Universal prediction of individual sequences , 1992, IEEE Trans. Inf. Theory.

[48]  Kyriakos Mouratidis,et al.  Continuous nearest neighbor monitoring in road networks , 2006, VLDB.

[49]  Victor C. M. Leung,et al.  Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks , 2002, Comput. Networks.

[50]  Jeffrey Scott Vitter,et al.  Efficient searching with linear constraints , 1998, J. Comput. Syst. Sci..

[51]  Zhensheng Zhang,et al.  Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges , 2006, IEEE Communications Surveys & Tutorials.

[52]  Jimeng Sun,et al.  The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries , 2003, VLDB.

[53]  Beng Chin Ooi,et al.  Efficient indexing of the historical, present, and future positions of moving objects , 2005, MDM '05.

[54]  Yufei Tao,et al.  Time-parameterized queries in spatio-temporal databases , 2002, SIGMOD '02.

[55]  Ian F. Akyildiz,et al.  A resource estimation and call admission algorithm for wireless multimedia networks using the shadow cluster concept , 1997, TNET.

[56]  Archan Misra,et al.  An information-theoretic framework for optimal location tracking in multisystem 4G wireless networks , 2004, IEEE INFOCOM 2004.

[57]  Dimitrios Gunopulos,et al.  On indexing mobile objects , 1999, PODS '99.

[58]  Maria Papadopouli,et al.  Analysis of wireless information locality and association patterns in a campus , 2004, IEEE INFOCOM 2004.

[59]  Ian H. Witten,et al.  Data Compression Using Adaptive Coding and Partial String Matching , 1984, IEEE Trans. Commun..

[60]  Yufei Tao,et al.  Validity Information Retrieval for Spatio-Temporal Queries: Theoretical Performance Bounds , 2003, SSTD.

[61]  Jimeng Sun,et al.  Selectivity estimation for predictive spatio-temporal queries , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[62]  Frans M. J. Willems,et al.  The context-tree weighting method: basic properties , 1995, IEEE Trans. Inf. Theory.