Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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. 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)

  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)

  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. 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. 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)

    Article  Google Scholar 

  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. 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. 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)

  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. 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. 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)

    Article  Google Scholar 

  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)

  17. Kubach, U.: A map-based, context-aware hoarding mechanism. In: Berichtskolloquium des Graduiertenkollegs Parallele und Verteilte Systeme, University of Stuttgart, Germany (2000)

  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. 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)

  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)

  21. Holma, H., Toskala, A.: WCDMA for UMTS: radio access for third generation mobile communications. Trans. Netw. 9(6), 790–800 (2001)

    Article  Google Scholar 

  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)

  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. 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. 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. 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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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)

  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

    Article  MATH  MathSciNet  Google Scholar 

  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. 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

    Article  Google Scholar 

  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)

  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

    Article  Google Scholar 

  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. 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. 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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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. 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. 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)

  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)

  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

    Article  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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)

  49. Wardlaw, M.I.: Intelligence and mobility for BT’s next generation networks. BT Technol. J. 23(1), 28–47 (2005)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  53. Johnson, N.L., Kemp, A.W., Kotz, S.: Univariate Discrete Distributions, p. 29, 3rd edn. Wiley-Interscience, New York (2005)

Download references

Author information

Authors and Affiliations

  1. Faculty of Technology, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Mohammad Sharif Daoud & Aladdin Ayesh

  2. College of Computer Engineering and Sciences, Salman Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Mustafa Al-Fayoumi

  3. Sheffield Business School, Sheffield Hallam University, Howard Street, Sheffield, S1 1WB, UK

    Adrian A. Hopgood

Authors
  1. Mohammad Sharif Daoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aladdin Ayesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mustafa Al-Fayoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adrian A. Hopgood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Sharif Daoud.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Daoud, M.S., Ayesh, A., Al-Fayoumi, M. et al. Location Prediction Based on a Sector Snapshot for Location-Based Services. J Netw Syst Manage 22, 23–49 (2014). https://doi.org/10.1007/s10922-012-9258-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-012-9258-9

Keywords

Search

Navigation