Skip to main content
SpringerLink
Log in

Effective crowdsensing and routing algorithms for next generation vehicular networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The vehicular ad hoc network (VANET) has recently emerged as a promising networking technique attracting both the vehicular manufacturing industry and the academic community. Therefore, the design of next generation VANET management schemes becomes an important issue to satisfy the new demands. However, it is difficult to adapt traditional control approaches, which have already proven reliable in ad-hoc wireless networks, directly. In this study, we focus on the development of vehicular crowdsensing and routing algorithms in VANETs. The proposed scheme, which is based on reinforcement learning and game theory, is designed as novel vertical and horizontal game models, and provides an effective dual-plane control mechanism. In a vertical game, network agent and vehicles work together toward an appropriate crowdsensing process. In a horizontal game, vehicles select their best routing route for the VANET routing. Based on the decentralized, distributed manner, our dual-plane game paradigm captures the dynamics of the VANET system. Simulations and performance analysis verify the efficiency of the proposed scheme, showing that our approach can outperform existing schemes in terms of RSU’s task success ratio, normalized routing throughput, and end-to-end packet delay.

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

Similar content being viewed by others

References

  1. Wang, Y., Liu, Y., Zhang, J., Ye, H., & Tan, Z. (2017). Cooperative store–carry–forward scheme for intermittently connected vehicular networks. IEEE Transactions on Vehicular Technology, 66(1), 777–784.

    Google Scholar 

  2. Morales, M. M. C., Haw, R., Cho, E. J., Hong, C. S., & Lee, S. (2012). An adaptable destination-based dissemination algorithm using a publish/subscribe model in vehicular networks. JCSE, 6(3), 227–242.

    Article  Google Scholar 

  3. Chen, J., Mao, G., Li, C., Zafar, A., & Zomaya, A. Y. (2017). Throughput of infrastructure-based cooperative vehicular networks. IEEE Transactions on Intelligent Transportation Systems, 18(11), 2964–2979.

    Article  Google Scholar 

  4. Su, Z., Hui, Y., & Yang, Q. (2017). The next generation vehicular networks: A content-centric framework. IEEE Wireless Communications, 24(1), 60–66.

    Article  Google Scholar 

  5. Wang, C., Zhang, Z., Shao, L., & Zhou, M. (2016). Estimating travel speed via sparse vehicular crowdsensing data. IEEE World Forum on Internet of Things (WF-IoT), 1, 643–648.

  6. Xiao, L., Chen, T., Xie, C., Dai, H., & Poor, V. (2017). Mobile crowdsensing games in vehicular networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2016.2647624.

    Google Scholar 

  7. Kim, S. (2016). Timed bargaining-based opportunistic routing model for dynamic vehicular ad hoc network. EURASIP Journal on Wireless Communications and Networking, 2016(14), 1–10.

    Google Scholar 

  8. Kim, J.-H., Lee, K.-J., Kim, T.-H., & Yang, S.-B. (2011). Effective routing schemes for double-layered peer-to-peer systems in MANET. JCSE, 5(1), 19–31.

    Article  Google Scholar 

  9. Jang, I., Pyeon, D., Kim, S., & Yoon, H. (2013). A survey on communication protocols for wireless sensor networks. JCSE, 7(4), 231–241.

    Article  Google Scholar 

  10. Kim, S. (2014). Game theory applications in network design. Hershey, PA: IGI Global.

    Book  Google Scholar 

  11. Chalkiadakis, G. (2007). A Bayesian approach to multiagent reinforcement learning and coalition formation under uncertainty. Doctoral dissertation, University of Toronto.

  12. Hu, T., & Fei, Y. (2010). QELAR: A machine-learning-based adaptive routing protocol for energy-efficient and lifetime-extended underwater sensor networks. IEEE Transactions on Mobile Computing, 9(6), 796–809.

    Article  Google Scholar 

  13. Galindo-Serrano, A., & Giupponi, L. (2010). Distributed Q-learning for aggregated interference control in cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(4), 1823–1834.

    Article  Google Scholar 

  14. Bai, F., & Krishnamachari, B. (2010). Exploiting the wisdom of the crowd: Localized, distributed information-centric VANETs. IEEE Communications Magazine, 48(5), 138–146.

    Article  Google Scholar 

  15. Wu, D., Arkhipov, D. I., Zhang, Y., Liu, C. H., & Regan, A. C. (2015). Online war-driving by compressive sensing. IEEE Transactions on Mobile Computing, 14(11), 2349–2362.

    Article  Google Scholar 

  16. Wu, D., Liu, Q., Li, Y., McCann, J. A., Regan, A. C., & Venkatasubramanian, N. (2016). Adaptive lookup of open WiFi using crowdsensing. IEEE/ACM Transactions on Networking, 24(6), 3634–3647.

    Article  Google Scholar 

  17. Talebifard, P., & Leung, V. C. M. (2013). Towards a content-centric approach to crowd-sensing in vehicular clouds. Journal of Systems Architecture, 59, 976–984.

    Article  Google Scholar 

  18. Bazzi, A., & Zanella, A. (2016). Position based routing in crowd sensing vehicular networks. Ad Hoc Networks, 36, 409–424.

    Article  Google Scholar 

  19. Wan, J., Liu, J., Shao, Z., Vasilakos, A. V., Imran, M., & Zhou, K. (2016). Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors, 16(1), 1–15.

    Article  Google Scholar 

  20. Wu, D., Zhang, Y., Luo, J., & Li, R. (2014). Efficient data dissemination by crowdsensing in vehicular networks. In IEEE IWQoS’2014 (pp. 314–319).

  21. Petrov, V., Samuylov, A., Begishev, V., Moltchanov, D., Andreev, S., Samouylov, K., & Koucheryavy, Y. (2017). Vehicle-based relay assistance for opportunistic crowdsensing over narrowband IoT (NB-IoT). IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2017.2670363.

    Google Scholar 

  22. Han, K., Chen, C., Zhao, Q., & Guan, X. (2015). Trajectory-based node selection scheme in vehicular crowdsensing. In IEEE/CIC international conference on communications in China (pp. 1–6).

  23. Sun, J., Hou, F., Ma, S., & Shan, H. (2016). Social-aware incentive mechanism for participatory sensing. In IEEE GLOBECOM’2016 (pp. 1–6).

  24. Nzouonta, J., Rajgure, N., Wang, G., & Borcea, C. (2009). VANET routing on city roads using real-time vehicular traffic information. IEEE Transactions on Vehicular Technology, 58(7), 3609–3626.

    Article  Google Scholar 

  25. Hieu, C. T., & Hong, C. S. (2010). A connection entropy-based multi-rate routing protocol for mobile ad hoc networks. JCSE, 4(3), 225–239.

    Article  Google Scholar 

  26. Jeon, M., Kim, S.-K., Yoon, J.-H., Lee, J., & Yang, S.-B. (2014). A direction entropy-based forwarding scheme in an opportunistic network. JCSE, 8(3), 173–179.

    Article  Google Scholar 

  27. Kim, S. (2012). Adaptive ad hoc network routing scheme by using incentive-based model. Ad Hoc & Sensor Wireless Networks, 15, 1–19.

    Google Scholar 

  28. Khaliq-ur-Rahman Raazi, S. M., & Lee, S. (2010). A survey on key management strategies for different applications of wireless sensor networks. JCSE, 4(1), 23–51.

    Article  Google Scholar 

  29. Kim, K., Uno, S., & Kim, M. (2010). Adaptive QoS mechanism for wireless mobile network. JCSE, 4(2), 153–172.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2014-0-00636) supervised by the IITP (Institute for Information and Communications Technology Promotion) and was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01060835).

Author information

Authors and Affiliations

  1. Department of Computer Science, Sogang University, 35 Baekbeom-ro (Sinsu-dong), Mapo-gu, Seoul, 121-742, South Korea

    Sungwook Kim

Authors
  1. Sungwook Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sungwook Kim.

Ethics declarations

Conflict of interest

The author, Sungwook Kim, declares that there is no competing interests regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, S. Effective crowdsensing and routing algorithms for next generation vehicular networks. Wireless Netw 25, 1815–1827 (2019). https://doi.org/10.1007/s11276-017-1632-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1632-9

Keywords

Search

Navigation