Peer-to-Peer Networking and Applications

, Volume 9, Issue 4, pp 721–730 | Cite as

Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing

  • Thejaswini MEmail author
  • P. Rajalakshmi
  • U. B. Desai


Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability.


Duration of stay Mobile phone Opportunistic crowd sensing Scheduling Sensors 



  1. 1.
    Azevedo TS, Bezerra RL, Campos CAV, de Moraes LFM (2009) An analysis of human mobility using real traces. In: WCNC, pp 1–6Google Scholar
  2. 2.
    Bettstetter C, Resta G, Santi P (2003) The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Trans Mobile Comput:257–269Google Scholar
  3. 3.
    Birand B, Zafer M, Zussman G, Lee K W (2011) Dynamic graph properties of mobile networks under levy walk mobility. IEEE Int Conf Mobile Ad-Hoc Sensor Syst:292–301Google Scholar
  4. 4.
    Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. In: Wireless communication and mobile computing (WCMC): Special issue on mobile ad hoc networking: Research, trends and applicationsGoogle Scholar
  5. 5.
    Campbell A T, Eisenman S B, Lane N D, Miluzzo E, Peterson R A (2006) People-centric urban sensing. In: WICON’06, the 2nd annual international wireless internet conferenceGoogle Scholar
  6. 6.
    Chander D, Jagyasi B, Desai U B, Merchant S N (2008) Layered data aggregation in cell-phone based wireless sensor networks. In: International conference on telecommunicationGoogle Scholar
  7. 7.
    Chatterjee M, Das S, Turgut D (2002) Wca: A weighted clustering algorithm for mobile ad hoc networks. J Cluster Comput (Special Issue Mobile Ad hoc Netw) 5(2):193–204Google Scholar
  8. 8.
    Chen YC, Kurose J, Towsley D (2012) Mixed queueing network model of mobility in a campus wireless network. In: INFOCOM, 2012 proceedings IEEE, pp 2656–2660Google Scholar
  9. 9.
    Eisenman SB, Lu H, Campbell AT (2010) Halo: Managing node rendezvous in opportunistic sensor networks. IEEE Int Conf Distrib Comput Sensor Syst:273–287Google Scholar
  10. 10.
    Hachem S, Pathak A, Issarny V (2013) Probabilistic registration for large-scale mobile participatory sensing. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), pp 132–140Google Scholar
  11. 11.
    Kanhere SS (2011) Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces. In: IEEE international conference on mobile data management, pp 3–6Google Scholar
  12. 12.
    Karagiannis T, Boudec JYL, Vojnovic M (2007) Power law and exponential decay of inter contact times between mobile devices. In: MobiCom, pp 183–194Google Scholar
  13. 13.
    Karamshuk D, Boldrini C, Conti M, Passarella A (2011) Human mobility models for opportunistic networks. IEEE Commun Mag:157–165Google Scholar
  14. 14.
    Khan WZ, Xiang Y, Aalsalem MY, Arshad Q (2013) Mobile phone sensing systems: A survey. IEEE Commun Surv Tutor 15(1):402–427CrossRefGoogle Scholar
  15. 15.
    Networking Research Lab: Human mobility models downloads.
  16. 16.
    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A T (2010) A survey of mobile phone sensing. IEEE Commun Mag:140–150Google Scholar
  17. 17.
    Lee WCY (2006) Wireless and cellular telecommunication. McGrawHillGoogle Scholar
  18. 18.
    Li J, Zhang Y, Chen YF, Nagaraja K, Li S, Raychaudhuri D (2013) A mobile phone based wsn infrastructure for iot over future internet architecture. In: IEEE international conference on green computing and communications and IEEE internet of things and IEEE cyber, physical and social computing, pp 426– 433Google Scholar
  19. 19.
    Manweiler J, Santhapuri N, Choudhury RR, Nelakuditi S (2013) Predicting length of stay at wifi hotspots. In: INFOCOM, 2013 proceedings IEEE, pp 3102–3110Google Scholar
  20. 20.
    Rhee I, Shin M, Hong S, Lee K, Chong S (2008) On the levy-walk nature of human mobility. IEEE INFOCOM:1597– 1605Google Scholar
  21. 21.
    Rhee I, Shin M, Hong S, Lee K, Kim SJ, Chong S (2011) On the levy-walk nature of human mobility. IEEE/ACM Trans Network 19(3):630–643CrossRefGoogle Scholar
  22. 22.
    Shah MB, Verma PP, Merchant SN, Desai UB (2011) Human mobility based stable clustering for data aggregation in singlehop cell phone based wireless sensor network. In: International conference on advanced information networking and applications, pp 427–434Google Scholar
  23. 23.
    Sheng X, Tang J, Zhang W (2012) Energy-efficient collaborative sensing with mobile phones. IEEE Infocom:1916–1924Google Scholar
  24. 24.
    Sprake J, Rogers P (2013) Crowds, citizens and sensors: Process and practice for mobilising learning. Person Ubiquit ComputGoogle Scholar
  25. 25.
    Thajchayapong S, Peha JM (2003) Mobility patterns in microcellular wireless networksin: Proceedings of IEEE wireless communications and networking conference (WCNC)Google Scholar
  26. 26.
    Viswanathan GM, Buldyrev SV, Havlin S, da Luzk MGE, Raposok EP, Stanley HE (1999) Optimizing the success of random searches. Nature 401:911–914CrossRefGoogle Scholar
  27. 27.
    Wikipedia: Preferred walking speed.
  28. 28.
    Yang D, Zhang D, Frank K, Robertson P, Jennings E, Roddy M, Lichtenstern M (2014) Providing real-time assistance in disaster relief by leveraging crowdsourcing power. Person Ubiquit ComputGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology HyderabadTelanganaIndia
  2. 2.Department of Electrical EngineeringIndian Institute of Technology HyderabadTelanganaIndia

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