Soft Computing

, Volume 20, Issue 7, pp 2641–2664 | Cite as

Using multi-objective metaheuristics for the optimal selection of positioning systems

  • Massimo Ficco
  • Roberto Pietrantuono
  • Stefano Russo
Methodologies and Application

Abstract

The interworking between cellular and wireless local area networks, as well as the spreading of mobile devices equipped with several positioning technologies pave the ground to new and more favorable indoor/outdoor location-based services (LBSs). Thus, wireless internet service providers are required to take several positioning methods into account at the same time, to leverage the different features of existing technologies. This would allow providing LBSs satisfying the user-required quality of position in terms of accuracy, privacy, power consumption, and often, conflicting features. Therefore, this paper presents GlobalPreLoc, a multi-objective strategy for the dynamic and optimal selection of positioning technologies. The strategy exploits a pattern-mining algorithm for future position prediction combined with conventional multi-objective evolutionary algorithms, for choosing continuously the best location providers, accounting for the user requirements, the terminal capabilities, and the surrounding positioning infrastructures. To practically implement the strategy, we also designed an architecture based on secure user plane location specification to provide indoor and outdoor LBSs in interworking wireless networks exploiting GlobalPreLoc features.

References

  1. Appear Network Inc (2015) Appear context engine. http://www.appearnetworks.com. Accessed Aug 2014
  2. Bellavista P, Corradi A, Giannelli C (2008) The PoSIM middleware for translucent and context-aware integrated management of heterogeneous positioning systems. Comput Commun 31:1078–1090CrossRefGoogle Scholar
  3. Chen Y, Chen XY, Rao FY, Yu XL, Li Y, Liu D (2004) LORE: an infrastructure to support location-aware services. IBM J Res Dev 48(5):601–615CrossRefGoogle Scholar
  4. Chicano F, Luna F, Nebro AJ, Alba E (2011) Using multi-objective metaheuristics to solve the software project scheduling problem. In: Krasnogor N (ed) Proceedings of the 13th annual conference on genetic and evolutionary computation (GECCO ’11). ACM, pp 1915–1922Google Scholar
  5. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Earlbaum Associates, Hillsdale, NJGoogle Scholar
  6. Coulouris G, Naguib H, Samugalingam K (2002) FLAME: an open framework for location-aware systems. Ubiquitous ComputGoogle Scholar
  7. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197CrossRefGoogle Scholar
  8. Deb Kalyanmoy, Gupta Shivam (2011) Understanding knee points in bicriteria problems and their implications as preferred solution principles. Eng Optim 43(11):1175–1204MathSciNetCrossRefGoogle Scholar
  9. Di Flora C, Ficco M, Russo S, Vecchio V (2005) Indoor and outdoor location based services for portable wireless devices. In: Proceedings of the IEEE international workshop on services and infrastructure for the ubiquitous and mobile internet (SIUMI’05), June 2005. IEEE CS Press, pp 244–250Google Scholar
  10. Dietterich TG (2002) Machine learning for sequential data: a review. In: Proceedings of the joint IAPR international workshop on structural, syntactic, and statistical pattern recognition. Springer, pp 15–30Google Scholar
  11. Durillo JJ, Nebro AJ (2011) jMetal: a Java framework for multi-objective optimization. Adv Eng Softw 42:760–771CrossRefGoogle Scholar
  12. Ekahau Inc (2015) Ekahau Positioning Engine 2.0. http://www.ekahau.com. Accessed Sep 2014
  13. Faggion N, Leroy S (2005) Alcatel location-based services solution. Alcatel Telecommunication, Technology White Paper, Sept 2005. http://www3.alcatellucent.com/wps/DocumentStreamerServlet?LMSG_CABINET=Docs_and_Resource_Ctr&LMSG_CONTENT_FILE=White_Papers/End_to_End_Location-Based_Services.pdf&lu_lang_code=en_WW
  14. Ferrucci F, Harman M, Ren J, Sarro F (2013) Not going to take this anymore: multi-objective overtime planning for software engineering projects. In: Proceedings of the 2013 international conference on software engineering (ICSE ’13), pp 462–471Google Scholar
  15. Ficco M, Russo S (2009) A hybrid positioning system for technology-independent location-aware computing. Softw: Pract Exp 39:1095–1125Google Scholar
  16. Ficco M, Pietrantuono R, Russo S (2010) Supporting ubiquitous location information in interworking 3G and wireless networks. Commun ACM 53(11):116–123CrossRefGoogle Scholar
  17. Ficco M, Esposito C, Napolitano A (2014) Calibrating indoor positioning systems with low efforts. IEEE Trans Mobile Comput 13(4):737–751CrossRefGoogle Scholar
  18. Geomena (2011) An open geo database of Wi-Fi access points. http://geomena.org/. Accessed Sept 2011
  19. Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: KDD 2007, pp 330–339Google Scholar
  20. Google Gears (2011) The Google Gears Geolocation API. http://code.google.com/intl/it-IT/apis/gears/api_geolocation.html. Accessed Feb 2011
  21. Google Latitude (2010) Google Latitude enables users to update and read their current location, and their location history. www.google.it/mobile/latitude/. Accessed Oct 2010
  22. Hansen S, Richter K, Klippel A (2006) Landmarks in OpenLS: a data structure for cognitive ergonomic route directions. In: LNCS, vol 4197. Springer, pp 383–393Google Scholar
  23. Hightower J, Brumitt B, Borriello G (2002) The location stack: layered model for location in ubiquitous computing. In: Proceedings of the 4th IEEE international workshop on mobile computing system and applications, IEEE CS PressGoogle Scholar
  24. Hohl F, Kubach U, Leonhardi A, Rothermel K, Schwehm M (1999) Next century challenges: nexus—an open global infrastructure for spatial-aware applications. In: Proceedings of the ACM international mobicom conference. ACM Press, pp 249–255Google Scholar
  25. Hosokawa Y, Takahashi N, Taga H (2004) A system architecture for seamless navigation. In: Proceedings of the international conference on distributed computing systems, March 2004Google Scholar
  26. Ilarri S, Illarramendi A, Mena E, Sheth AP (2011) Semantics in location-based services. IEEE Internet Comput 15(6):10–14CrossRefGoogle Scholar
  27. Jeung H, Liu Q, Shen HT, Zhou X (2008) A hybrid prediction model for moving objects. In: ICDE 2008, pp 70–79Google Scholar
  28. Karam R, Favetta F, Kilany R, Laurini R (2011) Location and Cartographic Integration for Multiproviders Location-Based Services. In: Advances in cartography and GIScience, LNCS, vol 1. Springer, pp 365–383Google Scholar
  29. La Marca A et al (2005) Place Lab: device positioning using radio beacons in the wild. In: Proceedings of the 3rd international conference on pervasive computing, LNCS, vol 3468. Springer, pp 116–133Google Scholar
  30. Lee S, Cheng S, Hsu JY, Huang P, You C (2006) Emergency care management with location-aware services. In: Proceedings of the pervasive health conference and workshops. IEEE CS Press, pp 1–6Google Scholar
  31. Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern 37(6):1067–1080CrossRefGoogle Scholar
  32. Mathew W, Raposo R, Martins B (2012) Predicting future locations with hidden Markov models. In: Proceedings of the 2012 ACM conference on ubiquitous computing (UbiComp ’12). ACM, New York, pp 911–918Google Scholar
  33. Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 637–646Google Scholar
  34. Morzy M (2006) Prediction of moving object location based on frequent trajectories. In: ISCIS, LNCS, vol 4263. Springer, pp 583–592Google Scholar
  35. Morzy M (2007) Mining frequent trajectories of moving objects for location prediction. In: Proceedings of the 5th international conference on machine learning and data mining in pattern recognition. Springer, pp 667–680Google Scholar
  36. Nguyen N, Guo Y (2007) Comparisons of sequence labeling algorithms and extensions. In: Proceedings of the 24th international conference on machine learning. ACM, pp 681–688Google Scholar
  37. Nord J, Synnes K, Parne P (2002) An architecture for location aware applications. In: Proceedings of the 35st international conference on system sciences, IEEE CS PressGoogle Scholar
  38. Ossama O, Mokhtar HMO (2009) Similarity search in moving object trajectories. In: Proceedings of the 15th international conference on management of data. Computer Society of India, pp 1–6Google Scholar
  39. Pfeifer T (2005) Redundant positioning architecture. Comput Commun 28(13):1575–1585 (Elsevier Press)Google Scholar
  40. Ranganathan A, Al-Muhtadi J, Chetan S, Campbell R, Mickunas D (2004) MiddleWhere: a middleware for location awareness in ubiquitous computing applications. In: Proceedings of the 5th international conference on middleware, LNCS, vol 3231. Springer, pp 397–416Google Scholar
  41. Skyhook Wireless (2011) Skyhook CEO undaunted by mobile giants. www.crunchbase.com/company/skyhook-wireless. Accessed June 2011
  42. Spanoudakis M, Batistakis A, Priggouris I, Ioannidis A, Hadjiefthymiades S, Merakos L (2003) Extensible platform for location based services provisioning. In: Proceedings of the international conference on web information systems engineering, Dec 2003Google Scholar
  43. The OMA Secure User Plane Location (SUPL) —v. 3 (2011) http://www.openmobilealliance.org/Technical/release_program/supl_v3_0.aspx. Last Release 2011
  44. TomTom International BV (2015) Tomtom’s Navigation Engine. www.tomtom.com/pro/page.php?ID=2. Accessed Sep 2014
  45. Vail DL, Veloso MM, Lafferty JD (2007) Conditional random fields for activity recognition. In: Proceedings of the 6th international joint conference on autonomous agents and multiagent systems. ACM, pp 1–8Google Scholar
  46. Van Veldhuizen DA, Lamont GB (1998) Multiobjective evolutionary algorithm research: a history and analysis. Technical report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson, AFB, OHGoogle Scholar
  47. Yavas G, Katsaros D, Ulusoy O, Manolopoulos Y (2005) A data mining approach for location prediction in mobile environments. DKE 54(2):121–146CrossRefGoogle Scholar
  48. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evolut Comput 3(4):257–271CrossRefGoogle Scholar
  49. Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength pareto evolutionary algorithms. EUROGEN 2001:95–100Google Scholar
  50. Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In: Yao X et al (eds) Parallel problem solving from nature (PPSN VIII). Springer, Berlin, pp 832–842Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Massimo Ficco
    • 1
  • Roberto Pietrantuono
    • 2
  • Stefano Russo
    • 2
  1. 1.Dipartimento di Ingegneria Industriale e dell’InformazioneSeconda Università degli Studi di NapoliAversaItaly
  2. 2.Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’InformazioneUniversità degli Studi di Napoli Federico IINaplesItaly

Personalised recommendations