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Learning for adaptive anycast in vehicular delay tolerant networks

  • Celimuge WuEmail author
  • Tsutomu Yoshinaga
  • Dabhur Bayar
  • Yusheng Ji
Original Research
  • 61 Downloads

Abstract

We propose a protocol for vehicular delay tolerant networks (DTN) with special focus on anycast communications from a vehicle to the cloud where multiple gateways (such as road side units) exist. The protocol employs a Q-learning algorithm to estimate the multi-hop destination encounter probability by discounting the reward with the number of forwards (number of hops). Anycast encounter probability is maintained in a specific entry which considers multiple gateways as the same virtual destination. The proposed protocol also uses an adaptive data replication scheme to take into account the destination encounter probability and the relative velocity between vehicles jointly, which can achieve a high data delivery ratio and low overhead. We use computer simulations to evaluate the proposed protocol.

Notes

Acknowledgements

This research was supported in part by the open collaborative research program at National Institute of Informatics (NII) Japan (FY2018), Inner Mongolia Autonomous Region Research project No. MW-2018-MGYWXXH-211, JSPS KAKENHI Grant Number 16H02817, and 16K00121.

References

  1. Agarwal A, Starobinski D, Little TDC (2012) Phase transition of message propagation speed in delay-tolerant vehicular networks. IEEE Trans Intell Transp Syst 13(1):249–263CrossRefGoogle Scholar
  2. Bista BB, Rawat DB (2016) Enhancement of PRoPHET routing in delay tolerant networks from an energy prospective. In: Proc. IEEE TENCON, pp 1579–1582Google Scholar
  3. Cai Y, Fan Y, Wen D (2016) An incentive-compatible routing protocol for two-hop delay-tolerant networks. IEEE Trans Veh Technol 65(1):266–277CrossRefGoogle Scholar
  4. Cao Y, Wang N, Sun Z, Cruickshank H (2015) A reliable and efficient encounter-based routing framework for delay/disruption tolerant networks. IEEE Sens J 15(7):4004–4018CrossRefGoogle Scholar
  5. Cao Y, Sun Z, Wang N, Riaz M, Cruickshank H, Liu X (2015) Geographic-based spray-and-relay (GSaR): an efficient routing scheme for DTNS. IEEE Trans Veh Technol 64(4):1548–1564CrossRefGoogle Scholar
  6. Freitas EP, Heimfarth T, Costa LAG, Ferreira AM, Pereira CE, Wagner FR, Larsson T (2011) Analyzing different levels of geographic context awareness in agent ferrying over VANETs. In: Proc. the 2011 ACM Symposium on Applied Computing, pp 413–418Google Scholar
  7. Freitas EP, Heimfarth T, Wagner FR, Pereira CE, Larsson T (2013) Exploring geographic context awareness for data dissemination on mobile ad hoc networks. Ad Hoc Netw 11(6):1746–1764CrossRefGoogle Scholar
  8. Galluccio L, Lorenzo B, Glisic S (2016) Sociality-aided new adaptive infection recovery schemes for multicast DTNs. IEEE Trans Veh Technol 65(5):3360–3376CrossRefGoogle Scholar
  9. Grondman I, Busoniu L, Lopes GAD, Babuska R (2012) A survey of actor-critic reinforcement learning: standard and natural policy gradients. IEEE Trans Syst Man Cybern. Part C 42(6):1291–1307Google Scholar
  10. Keranen A, Ott J, Karkkainen T (2009) The ONE simulator for DTN protocol evaluation, In: Proc. SIMUTools, pp 1–10Google Scholar
  11. Khan A, Sadhu S, Yeleswarapu M (2009) A comparative analysis of DSRC and 802.11 over Vehicular Ad hoc Networks, Project Report, University of California, Santa Barbara, pp 1–8Google Scholar
  12. Li F, Jiang H, Li H, Cheng Y, Wang Y (2017) SEBAR: social energy based routing for mobile social delay tolerant networks. IEEE Trans Veh Technol 66(8):7195–7206CrossRefGoogle Scholar
  13. Liang M, Zhang Z, Liu C, Chen L (2015) Multihop-delivery-quality-based routing in DTNs. IEEE Trans Veh Technol 64(3):1095–1104CrossRefGoogle Scholar
  14. Lindgren A, Doria A, Schelen O (2003) Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Comput Commun Rev 7(3):19–20CrossRefGoogle Scholar
  15. Prophet v2 (2012) Prophet v2 router [online]. https://www.netlab.tkk.fi/tutkimus/dtn/theone/contrib/ProphetV2Router.java [Accessed 19 Feb. 2018]Google Scholar
  16. Qi W, Song Q, Wang X, Guo L (2017) Trajectory data mining-based routing in DTN-enabled vehicular Ad Hoc networks. IEEE Access 5:24128–24138CrossRefGoogle Scholar
  17. Sharma DK, Dhurandher SK, Woungang I, Srivastava RK, Mohananey A, Rodrigues JJPC (2018) A machine learning-based protocol for efficient routing in opportunistic networks. IEEE Syst J. DOI: https://doi.org/10.1109/JSYST.2016.2630923
  18. Spyropoulos T, Psounis K, Raghavendra CS (2005) Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In: Proc. the 2005 ACM SIGCOMM workshop on Delay-tolerant networking, pp 252–259Google Scholar
  19. The ONE (2009) The ONE simulator [online]. https://www.netlab.tkk.fi/tutkimus/dtn/theone/ [Accessed 19 Feb. 2018]Google Scholar
  20. Tornell SM, Calafate CT, Cano J-C, Manzoni P (2015) DTN protocols for vehicular networks: an application oriented overview. IEEE Commun Surveys Tuts 17(2):868–887CrossRefGoogle Scholar
  21. Wang E, Yang Y, Wu J, Liu W (2015) A comprehensive forwarding strategy in DTNs: theory and practice. IEEE Trans. Veh. Technol. 66(12):11220–11232CrossRefGoogle Scholar
  22. Wu C, Kumekawa K, Kato T (2011) A dynamic route change mechanism for mobile ad hoc networks. Int J Commun Netw Distrib Syst 7(1/2):4–17CrossRefGoogle Scholar
  23. Wu C, Ji Y, Liu F, Ohzahata S, Kato T (2015) Towards practical and intelligent routing in vehicular Ad Hoc networks. IEEE Trans Veh Technol 64(12):1–17CrossRefGoogle Scholar
  24. Wu C, Kumekawa K, Kato T (2010) Distributed reinforcement learning approach for vehicular ad hoc networks. IEICE Trans. Commun. E93-B(6):1431–1442Google Scholar
  25. Yang Y, Zhao C, Yao S, Zhang W, Ge X, Mao G (2015) Delay performance of network-coding-based epidemic routing. IEEE Trans Veh Technol 65(5):3676–3684CrossRefGoogle Scholar
  26. Zhang L, Song J, Pan J (2016) A privacy-preserving and secure framework for opportunistic routing in DTNs. IEEE Trans Veh Technol 65(9):7684–7697CrossRefGoogle Scholar
  27. Zhu K, Li W, Fu X (2014) SMART: a social- and mobile-aware routing strategy for disruption-tolerant networks. IEEE Veh Technol Mag 63(7):3423–3434CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The University of Electro-CommunicationsTokyoJapan
  2. 2.Inner Mongolia UniversityHohhotPeople’s Republic of China
  3. 3.National Institute of InformaticsTokyoJapan

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