Journal of Network and Systems Management

, Volume 22, Issue 1, pp 23–49

Location Prediction Based on a Sector Snapshot for Location-Based Services

  • Mohammad Sharif Daoud
  • Aladdin Ayesh
  • Mustafa Al-Fayoumi
  • Adrian A. Hopgood
Article

Abstract

In location-based services (LBSs), the service is provided based on the users’ locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique.

Keywords

Mobility Mobile displacement Cell-based Map-based Markov chain model GPS UMTS 

References

  1. 1.
    Passerini, K., Patten, K., Bartolacci, M.R., Fjermestad, J.: Reflections and trends in the expansion of cellular wireless services in the U.S. and China. Commun. ACM 50(10), 25–28 (2007)CrossRefGoogle Scholar
  2. 2.
    Sun, Y., Belding-Royer, E.M., Gao, X., Kempf, J.: Real-time traffic support in heterogeneous mobile networks. Wirel. Netw. 13(4), 431–445 (2007)CrossRefGoogle Scholar
  3. 3.
    Wadear, R., Fagoonee, L.: Beyond third generation (B3G) mobile communication: challenges, broadband access and Europe. In: Mobility ’06: Proceedings of the 3rd International Conference on Mobile Technology, Applications & Systems, p. 5. ACM, New York, NY (2006)Google Scholar
  4. 4.
    Induruwa, A.: Mobile phone forensics: an overview of technical and legal aspects. Int. J. Electron. Secur. Digit. Forensic 2(2), 169–181 (2009). doi:10.1504/IJESDF.2009.024901
  5. 5.
    Fitzek, F., Schulte, G., Reisslein, M.: System architecture for billing of multi-player games in a wireless environment using GSM/UMTS and WLAN services. In: NetGames ’02: Proceedings of the 1st Workshop on Network and System Support for Games, pp. 58–64. ACM, New York, NY (2002)Google Scholar
  6. 6.
    Iftikhar, M., Landfeldt, B., Caglar, M.: Traffic engineering and QoS control between wireless diffserv domains using PQ and LLQ. In: MobiWac ’07: Proceedings of the 5th ACM International Workshop on Mobility Management and Wireless Access, pp. 120–129. ACM, New York, NY (2007)Google Scholar
  7. 7.
    Masri, S.A., Hunaiti, Z.: The impact of zoning concept on data-flow management within LBS system components. IJHCR 1(1), 43–63 (2010). doi:10.4018/jhcr.2010090903 Google Scholar
  8. 8.
    Escalle, P.G., Giner, V.C., Oltra J.M.: Reducing location updates and paging costs in a pcs network. IEEE Transactions on Wireless Communications 1(1), 200–209 (2002)Google Scholar
  9. 9.
    Zheng, J., Zhang, Y., Wang, L., Chen, J.: Adaptive location update area design for wireless cellular networks under 2D Markov walk model. Comput. Commun. 30(9), 2060–2069 (2007)CrossRefGoogle Scholar
  10. 10.
    Barbar, D.: Mobile computing and databases-a survey. IEEE Trans. Knowl. Data Eng. 11, 108–117 (1999). doi:10.1109/69.755619 Google Scholar
  11. 11.
    Wu, S.Y., Wu, K.T.: Effective location based services with dynamic data management in mobile environments. Wirel. Netw. 12(3), 369–381 (2006). doi:10.1007/s11276-005-5280-0
  12. 12.
    Dunham, M.H., Kumar, V.: Location dependent data and its management in mobile databases. In: DEXA ’98: Proceedings of the 9th International Workshop on Database and Expert Systems Applications, p. 414. IEEE Computer Society, Washington, DC (1998)Google Scholar
  13. 13.
    Seydim, A.Y., Dunham, M.H., Kumar, V.: Location dependent query processing. In: MobiDe ’01: Proceedings of the 2nd ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 47–53. ACM, New York, NY (2001). doi:10.1145/376868.376895
  14. 14.
    Francois, J.-M., Leduc, G.: Mobility prediction’s influence on QoS in wireless networks: a study on a call admission algorithm. In: WIOPT ’05: Proceedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp. 238–247. IEEE Computer Society, Washington, DC (2005). doi:10.1109/WIOPT.2005.29
  15. 15.
    Das, S.K., Sen, S.K.: Adaptive location prediction strategies based on a hierarchical network model in a cellular mobile environment. Comput. J. 42(6), 473–486 (1999)CrossRefGoogle Scholar
  16. 16.
    Biesterfeld, J., Ennigrou, E., Jobmann, K.: Location prediction in mobile networks with neural networks. In: The International Workshop on Applications of Neural Networks to Telecommunications, pp. 207–214 (1997)Google Scholar
  17. 17.
    Kubach, U.: A map-based, context-aware hoarding mechanism. In: Berichtskolloquium des Graduiertenkollegs Parallele und Verteilte Systeme, University of Stuttgart, Germany (2000)Google Scholar
  18. 18.
    Vijay Kumar, B.P., Venkataram, P.: Prediction-based location management using multilayer neural networks. J. Indian Inst. Sci. 82, 7–21 (2002)Google Scholar
  19. 19.
    Shah, S.H., Nahrstedt, K.: Predictive location-based QoS routing in mobile ad hoc networks. In: IEEE International Conference on Communications (ICC ’02), vol. 2, pp. 1022–1027, New York, NY (2002)Google Scholar
  20. 20.
    Kubach, U., Rothermel, K.: An adaptive, location-aware hoarding mechanism. In: Proceedings. ISCC 2000. Fifth IEEE Symposium on Computers and Communications, pp. 615–620, Antibes-Juan les Pins, France (2000)Google Scholar
  21. 21.
    Holma, H., Toskala, A.: WCDMA for UMTS: radio access for third generation mobile communications. Trans. Netw. 9(6), 790–800 (2001)CrossRefGoogle Scholar
  22. 22.
    Liou, S.-C., Lu, H.-C.: Applied neural network for location prediction and resources reservation scheme in wireless networks. In: International Conference on Communication Technology Proceedings, ICCT 2003, IEEE, vol. 2, pp. 958–961 (2003)Google Scholar
  23. 23.
    Capka, J., Boutaba, R.: Mobility Prediction in Wireless Networks Using Neural Networks, vol. 3271, pp. 320--333. Springer, Berlin, Heidelberg (2004)Google Scholar
  24. 24.
    Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: Proceedings of the fifth ACM international conference on Web search and data mining (WSDM ’12), pp. 723–732. ACM, New York, NY (2012). doi:10.1145/2124295.2124380
  25. 25.
    Daoud, M.Sh., Ayesh, A., Hopgood, A.A., Al-Fayoumi, M.: A new splitting-based displacement prediction approach for location-based services. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 392–397 (2011). doi:10.1109/ICSMC.2011.6083697
  26. 26.
    Ren, M., Karimi, H.: A fuzzy logic map matching for wheelchair navigation. GPS Solutions 15, 1–10 (2011). doi:10.1007/s10291-011-0229-5 CrossRefGoogle Scholar
  27. 27.
    Ren, M., Karimi, H.A.: A hidden Markov model-based map-matching algorithm for wheelchair navigation. J. Navig. 62(3), 383–395 (2009). doi:10.1017/S0373463309005347 CrossRefGoogle Scholar
  28. 28.
    Ren, M., Karimi, H.A.: A chain-code-based map matching algorithm for wheelchair navigation. Trans. GIS 13(2), 197–214 (2009). doi:10.1111/j.1467-9671.2009.01147.x CrossRefGoogle Scholar
  29. 29.
    Karimi, H.A., Liu, X.: A predictive location model for location-based services. In: GIS ’03: Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems, pp. 126–133. ACM, New York, NY (2003)Google Scholar
  30. 30.
    Wu, C.-F., Lee, L.-T., Tao, D.-F.: An HMM prediction and throttling-based call admission control scheme for wireless multimedia networks. Comput. Math. Appl. 54(3), 364–378 (2007). doi:10.1016/j.camwa.2007.01.035 CrossRefMATHMathSciNetGoogle Scholar
  31. 31.
    Sun, M.H., Blough, D.M.: Mobility prediction using future knowledge. In: Proceedings of the 10th ACM Symposium on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM ’07), pp. 235--239. ACM, New York, NY (2007). doi:10.1145/1298126.1298167
  32. 32.
    Soh, W.-S., Kim, H.S.: A predictive bandwidth reservation scheme using mobile positioning and road topology information. IEEE/ACM Trans. Netw. 14(5), 1078–1091 (2006). doi:10.1109/TNET.2006.882899 CrossRefGoogle Scholar
  33. 33.
    François, J.-M., Leduc, G., Martin, S.: Evaluation d’une méthode de prédiction des déplacements de terminaux dans les réseaux mobiles. In: Actes de Colloque Francophone sur l'Ingénierie des Protocoles, pp. 189–202. Paris, France (2003)Google Scholar
  34. 34.
    Liu, G., Maguire, G.: A class of mobile motion prediction algorithms for wireless mobile computing and communications. Mobile Netw. Appl. 1(2), 113–121 (1996). doi:10.1007/BF01193332 CrossRefGoogle Scholar
  35. 35.
    Bellahsene, S., Kloul, L.: A new Markov-based mobility prediction algorithm for mobile networks. In: Proceedings of the 7th European Performance Engineering Conference on Computer Performance Engineering (EPEW ’10), pp. 37–50. Springer-Verlag, Berlin, Heidelberg (2010). http://dl.acm.org/citation.cfm?id=1926981.1926986
  36. 36.
    Bellahsene, S., Kloul, L., Barth, D.: A hierarchical prediction model for two nodes-based IP mobile networks. In: Proceedings of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM ’09), pp. 173–180. ACM, New York, NY (2009). doi:10.1145/1641804.1641835
  37. 37.
    Pang, A.-C., Lin, Y.-B., Tsai, H.-M., Agrawal, P.: Serving radio network controller relocation for UMTS all-IP network. IEEE J. Sel. Areas Commun. 22(4), 617–629 (2004). doi:10.1109/JSAC.2004.825962 CrossRefGoogle Scholar
  38. 38.
    Lin, P., Lin, Y.-B., Chlamtac, I.: Overflow control for UMTS high-speed downlink packet access. IEEE Trans. Wirel. Commun. 3(2), 524–532 (2004). doi:10.1109/TWC.2003.821152 CrossRefGoogle Scholar
  39. 39.
    Migallón, H., Migallón, V., Penadés, J.: Alternating two-stage methods for consistent linear systems with applications to the parallel solution of Markov chains. Adv. Eng. Softw. 41(1), 13–21 (2010). doi:10.1016/j.advengsoft.2008.12.021
  40. 40.
    Buchholz, P.: Structured analysis techniques for large Markov chains. In: SMCtools ’06: Proceeding from the 2006 Workshop on Tools for Solving Structured Markov Chains, p. 2. ACM, New York, NY (2006). doi:10.1145/1190366.1190367
  41. 41.
    Brooks, S.P., Roberts, G.O.: Convergence assessment techniques for Markov chain Monte Carlo. Stat. Comput. 8(4), 319–335 (1998). doi:10.1023/A:1008820505350
  42. 42.
    Andreyevich, A.: “Rasprostranenie zakona bol’shih chisel na velichiny, zavisyaschie drug ot druga”. Izvestiya Fiziko-matematicheskogo obschestva pri Kazanskom universitete. In: 2-ya seriya, vol. 15, pp. 135–156 (1906)Google Scholar
  43. 43.
    Andreyevich, A.: Extension of the limit theorems of probability theory to a sum of variables connected in a chain, reprinted in Appendix B of R. Howard. In: Dynamic Probabilistic Systems, vol. 1: Markov Chains. John Wiley and Sons (1971)Google Scholar
  44. 44.
    Markovski, J., Sokolova, A., Trčka, N., de Vink, E.P.: Compositionality for Markov reward chains with fast and silent transitions. Perform. Eval. 66(8), 435–452 (2009). doi:10.1016/j.peva.2009.01.001 CrossRefGoogle Scholar
  45. 45.
    Bahl, P., Padmanabhan, V.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of IEEE Infocom, pp. 775–784 (2000)Google Scholar
  46. 46.
    Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with GPS. In: Proceedings of IEEE Sixth International Symposium on Wearable Computing, pp. 101–108 (2002)Google Scholar
  47. 47.
    Moustafa, M., Habib, I., Naghshineh, M.: GAME based dynamic resource scheduling in QoS aware radio access networks. Soft Comput. 9(2), 101–115 (2005)CrossRefGoogle Scholar
  48. 48.
    Harmatos, J.: Planning of UMTS Core Networks. In: 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 2, pp. 740–744 (2002)Google Scholar
  49. 49.
    Wardlaw, M.I.: Intelligence and mobility for BT’s next generation networks. BT Technol. J. 23(1), 28–47 (2005)CrossRefGoogle Scholar
  50. 50.
    Al-Manthari, B., Nasser, N., Hassanein, H.: Fair channel quality-based scheduling scheme for HSDPA system. In: AICCSA '06: Proceedings of the IEEE International Conference on Computer Systems and Applications, pp. 221–227. IEEE Computer Society, Washington, DC (2006). doi:10.1109/AICCSA.2006.205093
  51. 51.
    3GPP: Third Generation Partnership Project: High Speed Downlink Packet Access (HSDPA); Overall Description 3GPP TS 25.308, release 5 (2003). http://www.3gpp.org
  52. 52.
    Hwang, H., Chang, M., Tseng, C.: A direction based location update scheme with a line paging strategy for PCS Networks. IEEE Commun. Lett. 4, 149–151 (2000)CrossRefGoogle Scholar
  53. 53.
    Johnson, N.L., Kemp, A.W., Kotz, S.: Univariate Discrete Distributions, p. 29, 3rd edn. Wiley-Interscience, New York (2005)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mohammad Sharif Daoud
    • 1
  • Aladdin Ayesh
    • 1
  • Mustafa Al-Fayoumi
    • 2
  • Adrian A. Hopgood
    • 3
  1. 1.Faculty of TechnologyDe Montfort UniversityThe Gateway, LeicesterUK
  2. 2.College of Computer Engineering and SciencesSalman Bin Abdulaziz UniversityAl-KharjSaudi Arabia
  3. 3.Sheffield Business SchoolSheffield Hallam UniversitySheffieldUK

Personalised recommendations