Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments

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Abstract

Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios.

Keywords

Opportunistic networking Human mobility patterns Network measurements Mobile networks 
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References

  1. Aschenbruck N, Munjal A, Camp T (2011) Trace-based mobility modeling for multi-hop wireless networks. Elsevier Comput Commun 34(6):704–714CrossRefGoogle Scholar
  2. Benbadis F, Leguay J, Borrel V, Amorim M, Friedman T (2006) Experimental evaluation and characterization., Millipede: a rollerblade positioning system, ACM MobiCom 2006: workshop on wireless network testbedsWiNTECH, Los AngelesGoogle Scholar
  3. Bluetooth contact traces in shopping mall environments. CRAWDAD: community resource for archiving wireless data at Dartmouth, (online). http://crawdad.org/nottingham/mall
  4. Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wireless communication and mobile computing special issue on mobile ad hoc networking. Res Trends Appl 2(5):483–502Google Scholar
  5. Chaintreau A, Hui P, Crowcroft J, Diot C, Gass R, Scott J (2007) Impact of human mobility on the design of opportunistic forwarding algorithms. IEEE Trans Mobile Comput, pp 606–620Google Scholar
  6. Chaintreau A, Hui P, Crowcroft J, Diot C, Gass R, Scott J (2005) Pocket switched networks: real-world mobility and its consequences for opportunistic forwarding, Technical Report UCAM-CL-TR-617. Computer Laboratory, University of CambridgeGoogle Scholar
  7. Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquit Comput 10:255–268CrossRefGoogle Scholar
  8. Galati A, Greenhalgh C (2010) Human mobility in shopping mall environments, In: Proceedings of the second international workshop on mobile opportunistic networking, ACM MobiOpp ’10, Pisa, Italy, pp 1–7Google Scholar
  9. Galati A, Djemame K, Greenhalgh C (2013) Opportunistic forwarding throughout customers or sellers in shopping mall environments. In: Proceedings of the IFIP wireless days 2013 conference, Nov 21–23, Dublin, IrelandGoogle Scholar
  10. Ghosh J, Beal MJ, Ngo HQ, Qiao C (2005) On profiling mobility and predicting locations of campus-wide wireless users, in REALMAN ’06. In: Proceedings of the second international workshop on Multi-hop ad hoc networks: from theory to reality (MobiHoc), Florence, Italy, pp 187–201Google Scholar
  11. Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–82CrossRefGoogle Scholar
  12. Grossglauser M, Tse DNC (2002) Mobility increases the capacity of ad hoc wireless networks. IEEE ACM Trans Netw 10(2):477–486CrossRefGoogle Scholar
  13. Henderson T, Kotz D, Abyzov I (2004) The changing usage of a mature campus-wide wireless network. In: Proceedings of the 10th annual international conference on mobile computing and networking (MobiCom), Philadelphia, PA, USA, pp 187–201Google Scholar
  14. Hui P, Crowcroft J (2008) Human mobility models and opportunistic communication system design. Philosophical transactions of the royal society A, Math Phys Eng Sci 366:2005–2016Google Scholar
  15. Hui P, Chaintreau A, Scott J, Gass R, Crowcroft J, Diot C (2005) Pocket switched networks and human mobility in conference environments. In: Proceedings of the ACM SIGCOMM workshop on delay-tolerant networking, pp 244–251Google Scholar
  16. Karamshuk D, Boldrini C, Conti M, Passarella A (2011) Human mobility models for opportunistic networks. IEEE Commun Mag 49(12):157–65CrossRefGoogle Scholar
  17. Lenders V, Wagner J, May M (2006) Analyzing the impact of mobility in ad hoc networks. In: Proceedings of the ACM SIGMOBILE international workshop on multihop ad hoc networks: from theory to reality (REALMAN), Florence, ItalyGoogle Scholar
  18. McNett M, Voelker GM (2004) Access and mobility of sireless PDA users. Technical report, computer science and engineering, UC, San DiegoGoogle Scholar
  19. Minder D, Marrón PJ, Lachenmann A, Rothermel K (2005) Experimental construction of a meeting model for smart office environments. In: Proceedimgs of the first workshop on real-world wireless sensor networks (REALWSN 2005). SICS Technical Report T2005:09Google Scholar
  20. Kim M, Kotz D, Kim S (2006) Extracting a mobility model from real user traces. In: Proceedings of the 25th annual joint conference of the IEEE computer and communications societies (INFOCOM)Google Scholar
  21. Musolesi M, Mascolo C (2006) A community based mobility model for ad hoc network research. In: Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality, REALMAN ’06, 2006, Florence, Italy, pp 31–38, 8Google Scholar
  22. Musolesi M, Hailes S, Mascolo C (2004) An ad hoc mobility model founded on social network theory. In: Proceedings of 7th ACM international symposium on modeling, analysis and simulation of wireless and mobile systems, Venice, Italy, pp 20–24Google Scholar
  23. Okasha S (2005) Altruism, group selection and correlated interaction. Br J Philos Sci 56(4):703–725CrossRefGoogle Scholar
  24. Ou S, Pan H, Li F (2012) Heterogeneous wireless access technology and its impact on forming and maintaining friendship through mobile social networks. Int J Commun Syst 25:1300–1312. doi: 10.1002/dac.1314 CrossRefGoogle Scholar
  25. Palazzi CE, Bujari A (2012) Social-aware delay tolerant networking for mobile-to-mobile file sharing. Int J Commun Syst 25:1281–1299. doi: 10.1002/dac.1324 CrossRefGoogle Scholar
  26. Pirozmand P, Wu G, Jedari B, Xia F (2014) Human mobility in opportunistic networks: characteristics, models and prediction methods. J Netw Comput Appl (Accepted for publication)Google Scholar
  27. Venkateswaran P, Ghosh R, Das A, Sanyal SK, Nandi R (2006) An obstacle based realistic ad-hoc mobility model for social networks. J Netw Finl 1(2):37–44Google Scholar
  28. Vukadinovic V, Dreier F, Mangold S (2011) A simple framework to simulate the mobility and activity of theme park visitors. In: Proceedings of the winter simulation conference (WSC)Google Scholar
  29. Vukadinovic V, Mangold S (2011) Opportunistic wireless communication in theme parks: a study of visitors mobility. In: Proceedings of the 6th ACM workshop on challenged networks, CHANTS ’11, pp 3–8Google Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Adriano Galati
    • 1
    • 2
  • Karim Djemame
    • 1
  • Chris Greenhalgh
    • 3
  1. 1.Distributed Systems and Services Research Group, School of Computing, Faculty of EngineeringUniversity of LeedsLeedsUK
  2. 2.Wireless Communication and Mobile Computing research groupDisney Research ZurichZurichSwitzerland
  3. 3.Mixed Reality Lab, School of Computer ScienceUniversity of NottinghamNottinghamUK

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