Skip to main content
Log in

Spatiotemporal opportunistic transmission for mobile crowd sensing networks

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to characterize the mobility patterns and social attributes of nodes more precisely and combine their advantages, this method defines spatiotemporal encountering and visiting parameters related to specific space-time units for nodes in a MCS network. Further, to realize reliable opportunistic transmission across regions and time intervals, this method searches publishers or participants of sensing tasks in a space-time unit according to the spatiotemporal encountering parameters of nodes in the unit and tracks the publishers or participants across the space-time units according to the spatiotemporal visiting parameters of nodes. The simulation results verify that the proposed method can achieve higher success rate with less transmission delay than existing typical methods.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ganti RK, Ye F, Lei H (2011) Mobile crowd sensing: current state and future challenges. IEEE Commun 49(11):32–39

    Article  Google Scholar 

  2. Zheng Y, Liu F, Hsieh H (2013) U-air: when urban air quality inference meets big data. ACM Proceedings of the 19th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1436–1444

  3. Maisonneuve N, Stevens M, Niessen M et al (2009) Noisetube: measuring and mapping noise pollution with mobile phones. Inform Technol Environ Eng 2(6):215–228

    Google Scholar 

  4. Koukoumidis E, Peh L, Martonosi MR (2011) Signalguru: leveraging mobile phones for collaborative traffic signal schedule advisory. ACM Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp 127–140

  5. Miluzzo E, Lane ND, Fodor K, et al (2008) Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. ACM Proceedings of the 6th Conference on Embedded Network Sensor Systems, pp 337–350

  6. Yu Z, Guo W, Zhang D, Wang L, Guo B (2020) Cyber-physical-social-mediated communication. IT Professional 22(2):60–66

    Article  Google Scholar 

  7. Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P (2019) A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun Surv Tutorials 21(3):2419–2465

    Article  Google Scholar 

  8. Ra M, Liu B, Porta T, et al (2012) Medusa: a programming framework for crowd-sensing applications. ACM Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp 337–350

  9. Das T, Mohan P, Padmanabhan VN, et al (2010) PRISM: platform for remote sensing using smartphones. ACM Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp 63–76

  10. Hicks J, Ramanathan N, Kim D, et al (2010) Andwellness: an open mobile system for activity and experience sampling. ACM Wireless Health, pp 34–43

  11. Xiong H, Zhang D, Wang L, Chaouchi H (2015) EMC3: energy-efficient data transfer in mobile crowdsensing under full coverage constraint. IEEE Trans Mob Comput 14(7):1355–1368

    Article  Google Scholar 

  12. 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 Trans Syst Man Cybern 45(12):1549–1563

    Article  Google Scholar 

  13. Fall K, Farrell S (2008) DTN: an architectural retrospective. IEEE J Sel Areas Commun 26(5):828–836

    Article  Google Scholar 

  14. Lindgren A, Doria A, Schelén O (2003) Probabilistic routing in intermittently connected networks. ACM Sigmobile Mobile Comput Commun Rev 7(3):19–20

    Article  Google Scholar 

  15. Daly EM, Haahr M (2009) Social network analysis for information flow in disconnected delay-tolerant MANETs. IEEE Trans Mob Comput 8(5):606–621

    Article  Google Scholar 

  16. Yu Z, Zhang D, Yu Z, Yang D (2015) Participant selection for offline event marketing leveraging location based social networks. IEEE Trans Syst Man Cybern Syst 45(6):853–864

    Article  Google Scholar 

  17. Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

    Article  Google Scholar 

  18. Khan WZ, Xiang Y, Aalsalem MY, Arshad Q (2013) Mobile phone sensing systems: a survey. IEEE Commun Surv Tutorials 15(1):402–427

    Article  Google Scholar 

  19. Fiandrino C, Kantarci B, Anjomshoa F, et al (2016) Sociability-driven user recruitment in mobile crowd sensing internet of things platforms. IEEE Global Communication Conference, pp 1–6

  20. Han K, Zhang C, Luo J (2014) BLISS: budget limited robust crowd sensing through online learning. IEEE 11th International Conference on Sensing, Communication, and Networking, pp 555–563

  21. Balasubramanian N, Balasubramanian A, Venkataramani A (2009) Energy consumption in mobile phones: a measurement study and implications for network applications. ACM Proceedings of the 9th SIGCOMM Conference on Internet Measurement, pp 280–293

  22. Wang L, Zhang D, Xiong H, Gibson JP, Chen C, Xie B (2017) Ecosense: minimize participants’ total 3G data cost in mobile crowd sensing using opportunistic relays. IEEE Trans Syst Man Cybern 47(6):965–978

    Article  Google Scholar 

  23. Han Y, Zhu Y, Yu J (2015) Utility-maximizing data collection in crowd sensing: an optimal scheduling approach. IEEE 12th International Conference on Sensing, Communication, and Networking, pp 345–353

  24. Capponi A, Fiandrino C, Kliazovich D, Bouvry P, Giordano S (2017) A cost-effective distributed framework for data collection in cloud-based mobile crowd sensing architectures. IEEE Trans Sustain Comput 2(1):3–16

    Article  Google Scholar 

  25. Jain S, Fall K, Patra R (2004) Routing in a delay tolerant network. ACM SIGCOMM Comput Commun Rev 34(4):145–158

    Article  Google Scholar 

  26. Vahdat A, Becker D (2000) Epidemic routing for partially-connected ad hoc networks. Duke Tech Report CS-2000-06

  27. Spyropoulos T, Psounis K, Raghavendra CS (2008) Efficient routing in intermittently connected mobile networks: the multiple-copy case. IEEE/ACM Trans Networking 16(1):77–90

    Article  Google Scholar 

  28. Balasubramanian A, Levine B, Venkataramani A (2007) DTN routing as a resource allocation problem. ACM SIGCOMM Comput Commun Rev 37(4):373–384

    Article  Google Scholar 

  29. Lee K, Yi Y, Jeong J, et al (2010) Max-contribution: on optimal resource allocation in delay tolerant networks. IEEE Proceedings of the 29th Conference on Information Communications, pp 1136–1144

  30. Hui P, Crowcroft J, Yoneki E (2011) BUBBLE rap: social-based forwarding in delay-tolerant networks. IEEE Trans Mob Comput 10(11):1576–1589

    Article  Google Scholar 

  31. Yuan P, Liu P, Tang S (2014) Exploiting partial centrality of nodes for data forwarding in mobile opportunistic networks. IEEE 17th International Conference on Computational Science and Engineering, pp 1435–1442

  32. Link JAB, Schmitz D, Wehrle K (2011) GeoDTN: geographic routing in disruption tolerant networks. IEEE Global Telecommunications Conference, pp 1–5

  33. Chen K, Shen H, Yan L (2016) DSearching: using floating mobility information for distributed node searching in DTNs. IEEE Trans Mob Comput 15(1):121–136

    Article  Google Scholar 

  34. Henderson T, Kotz D, Abyzov I (2004) The changing usage of a mature campus-wide wireless network. Comput Netw 52(14):2690–2712

    Article  MATH  Google Scholar 

  35. Zhang X, Kurose J, Levine BN, et al (2010) Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing. Proceedings of International Conference on Mobile Computing and Networking, pp 195–206

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61802257 and 61602305 and by the Natural Science Foundation of Shanghai under Grants 18ZR1426000 and 19ZR1477600.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guisong Yang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, X., Liu, M. & Yang, G. Spatiotemporal opportunistic transmission for mobile crowd sensing networks. Pers Ubiquit Comput 27, 551–561 (2023). https://doi.org/10.1007/s00779-020-01439-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-020-01439-7

Keywords

Navigation