Skip to main content
SpringerLink
Log in

A unified delay analysis framework for opportunistic data collection

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The opportunistic data collection paradigm leverages human mobility to improve sensing coverage and data transmission for collecting data from a number of Points of Interest scattered across a large sensing field, enabling many large-scale mobile crowd sensing applications at lower cost. Sensing delay and transmission delay are two critical Quality of Service (QoS) metrics for such applications. However, the existing works only study them separately, and do not fully consider the effects of various sink deployment schemes (i.e., single sink or multiple sinks, and sinks are static or mobile) and transmission schemes (i.e., direct transmission, epidemic transmission or other schemes). In contrast, we provide a unified delay analysis framework for opportunistic data collection, by integrating both the sensing and transmission delays, to describe the QoS more comprehensively. We analyze how the three delays (sensing delay, transmission delay, and a novel metric called data collection delay) vary with several parameters considering the effects of various sink deployment schemes and transmission schemes. Validity of the theoretic analysis is verified by simulations.

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

Similar content being viewed by others

References

  1. Ahmed, A., Yasumoto, K., Yamauchi, Y., & Ito, M. (2011). Distance and time based node selection for probabilistic coverage in people-centric sensing. In Proceedings of IEEE SECON (pp. 134–142).

  2. Chin, T., Ramanathan, P., & Saluja, K. (2006). Analytic modeling of detection latency in mobile sensor networks. In Proceedings of ACM/IEEE IPSN (pp. 194–201).

  3. Conti, M., Boldrini, C., Kanhere, S. S., Mingozzi, E., Pagani, E., Ruiz, P. M., et al. (2015). From manet to people-centric networking: Milestones and open research challenges. Computer Communications, 71, 1–21.

    Article  Google Scholar 

  4. Dutta, P., Aoki, P., Kumar, N., Mainwaring, A., Myers, C., Willett, W., & Woodruff, A. (2009). Common sense: Participatory urban sensing using a network of handheld air quality monitors. In Proceedings of ACM SenSys (pp. 349–350).

  5. Eisenman, S. B., Lane, N. D., & Campbell, A. T. (2008). Techniques for improving opportunistic sensor networking performance. In Proceedings of IEEE DCOSS (pp. 157–175).

  6. Ganti, R. K., Ye, F., & Lei, H. (2011). Mobile crowdsensing: Current state and future challenges. IEEE Communications Magazine, 49(11), 32–39.

    Article  Google Scholar 

  7. Groenevelt, R., Nain, P., & Koole, G. (2005). The message delay in mobile ad hoc networks. Performance Evaluation, 62(1–4), 210–228.

    Article  Google Scholar 

  8. Guo, B., Zhang, D., Wang, Z., Yu, Z., & Zhou, X. (2013). Opportunistic iot: Exploring the harmonious interaction between human and the internet of things. Journal of Network and Computer Applications, 36(6), 1531–1539.

    Article  Google Scholar 

  9. Ip, Y. K., Lau, W. C., & Yue, O. C. (2008). Performance modeling of epidemic routing with heterogeneous node types. In Proceedings of IEEE ICC (pp. 219–224).

  10. Lee, K., Hong, S., Kim, S., Rhee, I., & Chong, S. (2009). Slaw: A mobility model for human walks. In Proceedings of IEEE INFOCOM (pp. 855–863).

  11. Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), 19–20.

    Article  Google Scholar 

  12. Liu, B., Brass, P., Dousse, O., Nain, P., & Towsley, D. (2005). Mobility improves coverage of sensor networks. InProceedings of ACM MobiHoc (pp. 300–308).

  13. Liu, B., & Towsley, D. (2004). A study of the coverage of large-scale sensor networks. In Proceedings of IEEE MASS (pp. 475–483).

  14. Luo, J., & Hubaux, J. (2005). Joint mobility and routing for lifetime elongation in wireless sensor networks. In Proceedings of IEEE INFOCOM (vol. 3, pp. 1735–1746).

  15. Ma, H. D., Zhao, D., & Yuan, P. (2014). Opportunities in mobile crowd sensing. IEEE Communications Magazine, 52(8), 29–35.

    Article  Google Scholar 

  16. Picu, A., Spyropoulos, T., & Hossmann, T. (2012). An analysis of the information spreading delay in heterogeneous mobility dtns. In Proceedings of IEEE WoWMoM (pp. 1–10).

  17. Rana, R., Chou, C., Kanhere, S., Bulusu, N., & Hu, W. (2010). Ear-phone: An end-to-end participatory urban noise mapping system. In Proceedings of ACM/IEEE IPSN (pp. 105–116).

  18. Shah, R., Roy, S., Jain, S., & Brunette, W. (2003). Data mules: Modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Networks, 1(2–3), 215–233.

    Article  Google Scholar 

  19. Small, T., & Haas, Z. (2003). The shared wireless infostation model: A new ad hoc networking paradigm (or where there is a whale, there is a way). In Proceedings of ACM MobiHoc (pp. 233–244).

  20. Tseng, Y. C., Wu, F. J., & Lai, W. T. (2013). Opportunistic data collection for disconnected wireless sensor networks by mobile mules. Ad Hoc Networks, 11(3), 1150–1164.

    Article  Google Scholar 

  21. Wang, L., Zhang, D., Yan, Z., Xiong, H., & Xie, B. (2015). Effsense: A novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploading. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(12), 1549–1563.

    Article  Google Scholar 

  22. Wang, Q., Wang, X., & Lin, X. (2009). Mobility increases the connectivity of k-hop clustered wireless networks. In Proceedings of ACM MobiCom (pp. 121–132).

  23. Wang, X., Wang, X., & Zhao, J. (2011). Impact of mobility and heterogeneity on coverage and energy consumption in wireless sensor networks. In Proceedings of IEEE ICDCS.

  24. Wang, Y., & Wu, H. (2007). Delay/fault-tolerant mobile sensor network (dft-msn): A new paradigm for pervasive information gathering. IEEE Transactions on Mobile Computing, 6(9), 1021–1034.

    Article  MathSciNet  Google Scholar 

  25. Wang, Z., Basagni, S., Melachrinoudis, E., & Petrioli, C. (2005). Exploiting sink mobility for maximizing sensor networks lifetime. In Proceedings of HICSS.

  26. Wimalajeewa, T., & Jayaweera, S. (2010). Impact of mobile node density on detection performance measures in a hybrid sensor network. IEEE Transactions on Wireless Communications, 9(5), 1760–1769.

    Article  Google Scholar 

  27. Wimalajeewa, T., & Jayaweera, S. (2010). Mobility assisted distributed tracking in hybrid sensor networks. In Proceedings of IEEE ICC (pp. 1–5).

  28. Xiong, H., Zhang, D., Wang, L., & Chaouchi, H. (2015). Emc 3: Energy-efficient data transfer in mobile crowdsensing under full coverage constraint. IEEE Transactions on Mobile Computing, 14(7), 1355–1368.

    Article  Google Scholar 

  29. Yuan, P., & Ma, H. D. (2012). Impact of infection rate on scaling law of epidemic routing. In Proceedings of IEEE WCNC (pp. 2934–2939).

  30. Yun, Y., & Xia, Y. (2010). Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Transactions on Mobile Computing (pp. 1308–1318).

  31. Zhang, X., Neglia, G., Kurose, J., & Towsley, D. (2007). Performance modeling of epidemic routing. Computer Networks, 51(10), 2867–2891.

    Article  MATH  Google Scholar 

  32. Zhao, D., Ma, H. D., & Liu, L. (2014). Energy-efficient opportunistic coverage for people-centric urban sensing. Wireless networks, 20(6), 1461–1476.

    Article  Google Scholar 

  33. Zhao, D., Ma, H. D., Liu, L., & Li, X. Y. (2015). Opportunistic coverage for urban vehicular sensing. Computer Communications, 60, 71–85.

    Article  Google Scholar 

  34. Zhao, D., Ma, H. D., Tang, S., & Li, X. Y. (2015). Coupon: A cooperative framework for building sensing maps in mobile opportunistic networks. IEEE Transactions on Parallel and Distributed Systems, 26(2), 392–402.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61332005, 61502051, and 61302087, the Funds for Creative Research Groups of China under Grant No. 61421061, the Cosponsored Project of Beijing Committee of Education, and the Beijing Training Project for the Leading Talents in S&T (ljrc201502).

Author information

Authors and Affiliations

  1. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Dong Zhao & Huadong Ma

  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qi Li

  3. Naveen Jindal School of Management, University of Texas at Dallas, Richardson, TX, USA

    Shaojie Tang

Authors
  1. Dong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huadong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaojie Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, D., Ma, H., Li, Q. et al. A unified delay analysis framework for opportunistic data collection. Wireless Netw 24, 1313–1325 (2018). https://doi.org/10.1007/s11276-016-1403-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-016-1403-z

Keywords

Search

Navigation