[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Characterizing the Capability of Vehicular Fog Computing in Large-scale Urban Environment

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The worldwide increase of vehicles is demanding the deployment of an intelligent transportation system for the urban environment. Recently, cloud computing technology is utilized to make the vehicles on the roads smarter and offer better driving experience. However, the intrinsic client-server communication model in the cloud-assisted service cannot meet the increasing demands for intensive computing in vehicles. To solve this challenging issue, we investigate another form of computing service, vehicular fog computing (VFC), which is a group of nearby smart vehicles connected via peer-to-peer communication model. Though VFC can provide computing service to any task initiator, its computational capability, i.e., the ability to provide computing service to the initiator, might be severely constrained by the realistic environments including limited communication ranges, high speeds and unpredictable mobility patterns of vehicles. In this paper, we characterize the computational capability (indicated by the product of processor speed and the time length from receiving the task) of VFC in a practical scenario through studying real-world vehicular mobility traces of Beijing. Specially, we propose a time-varying graph model to access the capability of VFC in such a large-scale urban environment with different scenarios. Based on this model, we reveal the temporal and spatial characteristics of the computational capability with different number of task initiators and portray its distribution of the number of connected vehicles and the computational capability. The distribution of the computational capability is also portrayed. Based on these observations, we define two modes to depict two different models of task distribution. Furthermore, we reveal the relationship between the computational capability and system parameters of computation delay, communication radius, and the number of initiators.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Transportation forecast: light duty vehicles (2014) http://www.navigantresearch.com/

  2. Texas Transport Institute (2012) Urban Mobility Report

  3. Wang J, Cho J, Lee S, Ma T (2011) Real time services for future cloud computing enabled vehicle networks. In: IEEE International Conference on Wireless Communications and Signal Processing (WCSP) (Nanjing, China), pp 1–5

  4. Whaiduzzaman M, Sookhak M, Gani A, Buyya R (2013) A survey on vehicular cloud computing. J Netw Comput Appl 40(7):325–344

    Google Scholar 

  5. Koukoumidis E, Lymberopoulos D, Strauss K, Liu J, Burger D (2012) Pocket cloudlets. ACM SIGPLAN Not 47(4):171–184

    Article  Google Scholar 

  6. Kenney J (2011) Dedicated short-range communications (DSRC) standards in the United States. Proc IEEE 99(7):1162–1182

    Article  Google Scholar 

  7. Li Y, Wang W (2014) Can mobile cloudlets support mobile applications?. In: Proceedings of IEEE INFOCOM (Toronto, Canada), pp 1060–1068

  8. Toor Y, Muhlethaler P, Laouiti A (2008) Vehicle ad hoc networks: Applications and related technical issues. IEEE Commun Surv Tutor 10(3):74–88

    Article  Google Scholar 

  9. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: State-of-the-art and research challenges. J Internet Serv Appl 1:7–18

    Article  Google Scholar 

  10. Gerla M (2012) Vehicular cloud computing. In: Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net) (Ayia Napa, Cyprus), pp 152–155

  11. Basagni S, Conti M, Giordano S, Stojmenovic I (2013) The next paradigm shift: From vehicular networks to vehicular clouds, Mobile Ad Hoc Networking: The Cutting Edge Directions. Wiley-IEEE Press, New Jersey, pp 645–700

    Google Scholar 

  12. Elgazzar K, Lutfiyya H, Mcheick H (2014) IEEE International Workshop on ubiquitous mobile cloud (UMC 2014). In: IEEE World Congress on Services (SERVICES), Anchorage, pp 410–411

  13. Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: Architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611

    Article  Google Scholar 

  14. Hui S, Liu Z, Wan J, Zhou K (2013) Security and privacy in mobile cloud computing. In: International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, pp 655–659

  15. Sharma R, Kumar S, Trivedi MC (2013) Mobile cloud computing: A needed shift from cloud to mobile cloud. In: International Conference on Computational Intelligence and Communication Networks (CICN), pp 536–539

  16. Han Q, Kuala L, Gani A (2012) Research on mobile cloud computing: Review, trend and perspectives. In: International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), Bangkok, pp 195–202

  17. Fernando N, Loke S, Rahayu W (2013) Mobile cloud computing: A survey. Fut Gener Comput Syst 29 (1):84–106

    Article  Google Scholar 

  18. Marinelli EE (2009) Hyrax: Cloud computing on mobile devices using MapReduce. Master Thesis, Carnegie Mellon University, Pittsburgh

    Google Scholar 

  19. Huerta-Canepa G, Lee D (2010) A virtual cloud computing provider for mobile devices. In: Proceedings 1st ACM Workshop Mobile Cloud Computing andamp; Services: Social Networks and Beyond, San Francisco, pp 1–5

  20. Fernando N, Loke S, Rahayu W (2011) Dynamic mobile cloud computing: Ad hoc and opportunistic job sharing. In: Proceedings of 4th IEEE UCC, Victoria, pp 281–286

  21. Shi C, Lakafosis V, Ammar MH, Zegura EW (2012) Serendipity: Enabling remote computing among intermittently connected mobile devices. In: Proceedings ACM MobiHoc“12 (Head), Island, pp 145–154

  22. Banerjee A, Mukherjee A, Paul HS, Dey S (2013) Offloading work to mobile devices: an availability-aware data partitioning approach. In: MCS ’13 Proceedings of the First International Workshop on Middleware for Cloud-enabled Sensing, p 4

  23. Naboulsi D, Stanica R, Fiore M (2014) Classifying call profiles in large-scale mobile traffic datasets. In: INFOCOM, 2014 Proceedings IEEE, Toronto, pp 1806–1814

  24. Han Q, Kuala L, Gani A (2013) Toward cloud-based vehicular networks with efficient resource management. IEEE Netw 27(5):48–55

    Article  Google Scholar 

  25. Yu R, Zhang Y, Wu H, Chatzimisios P, Xie S (2013) Virtual machine live migration for pervasive services in cloud-assisted vehicular networks. In: International ICST Conference on Communications and Networking in China (CHINACOM), Guilin, pp 540–545

  26. Xu K, Izard R, Yang F, Wang K, Martin J (2013). In: 2013 Second GENI Research and Educational Experiment Workshop (GREE), Salt Lake City, pp 45–49

  27. Sumra IA, Hasbullah H, Manan JA, Iftikhar M, Ahmad I, Aalsalem MY (2011) Trust levels in peer-to-peer (P2P) vehicular network. In: International Conference on ITS Telecommunications (ITST), St. Petersburg, pp 708–714

  28. Hung C-C, Chan H, Wu EH-K (2008) Mobility pattern aware routing for heterogeneous vehicular networks. In: IEEE Wireless Communications and Networking Conference, Las Vegas, pp 2200–2205

  29. Kumaran U, Shaji RS (2014) Vertical handover in Vehicular ad-hoc network using multiple parameters. In: International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, pp 1059–1064

  30. Han Q, Bai Y, Gong L, Wu W (2011) Link availability prediction-based reliable routing for mobile ad hoc networks. IET Commun 5(16):2291–2230

    Article  MathSciNet  Google Scholar 

  31. Olariu S, Weigle MC (2010) Vehicular Networks: From theory to practice. CRC Press, Boca Raton

    Google Scholar 

  32. Torkestani JA (2012) Mobility prediction in mobile wireless networks. J Netw Comput Appl 35(5):1633–1645

    Article  MathSciNet  Google Scholar 

  33. Qin Y, Huang D, Zhang X (2012) VehiCloud: Cloud computing facilitating routing in vehicular networks. In: Proceedings IEEE 11th International Con Trust, Security and Privacy in Computing and Communications, Liverpool, pp 1438–1445

  34. Artimy MM, Robertson W, Phillips WJ (2004) Connectivity in inter-vehicle ad hoc networks. In: IEEE Canadian Conference on Electrical and Computer Engineering, vol 1, pp 293–298

  35. Fler H, Torrent-Moreno M, Transier M, Krüger R, Hartenstein H, Effelsberg W (2006) Studying vehicle movements on highways and their impact on ad-hoc connectivity. ACM SIGMOBILE Mob Comput Commun Rev 10(4):26–27

    Article  Google Scholar 

  36. Yousefi S, Altman E, El-Azouzi R, Fathy M (2008) Analytical model for connectivity in vehicular ad hoc networks. IEEE Trans Veh Technol 57(6):33–41

    Article  Google Scholar 

  37. Khabazian M, Ali M (2008) A performance modeling of connectivity in vehicular adhoc networks. IEEE Trans Veh Technol 57(4):2440–2450

    Article  Google Scholar 

  38. Mohimani GH, Ashtiani F, Javanmard A, Hamdi M (2009) Mobility modeling, spatial traffic distribution, and probability of connectivity for sparse and dense vehicular ad hoc networks. IEEE Trans Veh Technol 58(4):1998–2007

    Article  Google Scholar 

  39. Gramaglia M, Trullols-Cruces O, Naboulsi D, Marco FM, Calderon M (2014) Vehicular networks on two Madrid highways. In: 2014 Eleventh Annual IEEE International Conference onSensing, Communication, and Networking (SECON), Singapore, pp 423–431

  40. Ho I. W. h., Leung KK (2007) Node connectivity in vehicular ad hoc networks with structured mobility. In: 32nd IEEE Conference on Local Computer Networks, Dublin, pp 635– 642

  41. Fiore M, Hrri J (2008) The networking shape of vehicular mobility. In: Proceedings of the 9th ACM International Symposium on Mobile ad hoc Networking and Computing. ACM, New York, pp 261–272

  42. Kafsi M, Papadimitrato P, Dousse O, Alpcan T, Hubaux J (2009) Vanet connectivity analysis, arXiv:0912.5527

  43. Viriyasitavat W, Tonguz OK, Bai F (2009) Network connectivity of VANETs in urban areas. In: IEEE 2009 SECON“09 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, Rome, pp 1–9

  44. Conceio H, Ferreira M, Barros J (2008) On the urban connectivity of vehicular sensor networks. In: Distributed Computing in Sensor Systems, Berlin Heidelberg, pp 112–125

  45. Pallis G, Katsaros D, Dikaiakos MD, Loulloudes N, Tassiulas R, Hellas V (2009) On the structure and evolution of vehicular networks. In: IEEE International Symposium on Modeling, Analysis andamp; Simulation of Computer and Telecommunication Systems, 2009. MASCOTS“09, London, pp 1–10

  46. Naboulsi D, Fiore M (2013) On the instantaneous topology of a large-scale urban vehicular network: the Cologne case. In: Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing. ACM, New York, pp 167–176

  47. Luo P, Huang H, Li M (2007) Characteristics of trace data for a large scale ad hoc network - Shanghai Urban Vehicular Network. In: IET Conference on Wireless, Mobile and Sensor Networks, 2007 (CCWMSN07), Shanghai, pp 1–5

  48. Whitbeck J, Amorim MD, Conan V, Guillaume J-L (2012) Temporal Reachability Graphs. In: Proceedings ACM Mobicom 2012, pp 377–388

Download references

Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (Grant No. 61502540, 61562005), the National Science Foundation (NSF) (Grant No. 1137732), the China Scholarship Council (Grant No. 2015 [3012]), the National Science Foundation of Hunan Province (Grant No. 2015JJ4077)

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Xiaoyan Kui & Shigeng Zhang

  2. Department of Electronic Engineering, Tsinghua National Laboratory for Information Science and Technology (TNLIST), Tsinghua University, Beijing, 100084, China

    Yue Sun & Yong Li

Authors
  1. Xiaoyan Kui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shigeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kui, X., Sun, Y., Zhang, S. et al. Characterizing the Capability of Vehicular Fog Computing in Large-scale Urban Environment. Mobile Netw Appl 23, 1050–1067 (2018). https://doi.org/10.1007/s11036-017-0969-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-017-0969-8

Keywords