Social Networking and Big Data Analytics Assisted Reliable Recommendation System Model for Internet of Vehicles

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10036)

Abstract

The devices are becoming ubiquitous and interconnected due to rapid advancements in computing and communication technology. The Internet of Vehicles (IoV) is one such example which consists of vehicles that converse with each other as well as with the public networks through V2V (vehicle-to-vehicle), V2P (vehicle-to-pedestrian) and V2I (vehicle-to-infrastructure) communications. The social relationships amongst vehicles create a social network where the participants are intelligent objects rather than the human beings and this leads to emergence of Social Internet of Vehicles (SIoV). The big data generated from these networks of devices are needed to be processed intelligently for making these systems smart. The security and privacy issues such as authentication and recognition attacks, accessibility attacks, privacy attacks, routing attacks, data genuineness attacks etc. are to be addressed to make these cyber physical network systems very reliable. This paper presents a comprehensive survey on SIoV and proposes a novel social recommendation model that could establish links between social networking and SIoV for reliable exchange of information and intelligently analyze the information to draw authentic conclusions for making right assessment. The future Intelligent IoV system which should be capable to learn and explore the cyber physical system could be designed.

Keywords

Big data IoV SIoV Social recommendation system Cyber physical systems 

References

  1. 1.
    Fan, W., Bifet, A.: Mining big data: current status, and forecast to the future. SIGKDD Explor. 14(2), 1–5 (2013)CrossRefGoogle Scholar
  2. 2.
    Bifet, A.: Mining big data in real time. Informatica 37, 15–20 (2013)Google Scholar
  3. 3.
    Wu, B.: Internet-of-vehicles based on technologies of internet-of-things. In: ICLEM, pp. 348–356 (2012)Google Scholar
  4. 4.
    Vermesan, O., Friess, P.: Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers, Aalborg (2013). ISBN 978-87-92982-96-4Google Scholar
  5. 5.
    Bin, S., Yuan, L., Xiaoyi, W.: Research on data mining models for the internet of things. In: Proceedings International Conference on Image Analysis and Signal Processing, pp. 127–132 (2010)Google Scholar
  6. 6.
    Leng, Y., Zhao, L.: Novel design of intelligent internet-of-vehicles management system based on cloud-computing and internet-of-things. In: Proceedings International Conference on Electronic & Mechanical Engineering and Information Technology, Harbin, Heilongjiang, China, vol. 6, pp. 3190–3193 (2011)Google Scholar
  7. 7.
    Goggin, G.: Driving the internet: mobile internets, cars, and the social. Future Internet 4, 306–321 (2012). doi: 10.3390/fi4010306 CrossRefGoogle Scholar
  8. 8.
    Guo, D., Mennis, J.: Spatial data mining and geographic knowledge discovery: an introduction. Comput. Environ. Urban Syst. 33, 403–408 (2009). ElsevierCrossRefGoogle Scholar
  9. 9.
    Crawford, K., Schultz, J.: Big data and due process: toward a framework to redress predictive privacy harms. B. C.L. Rev. 55, 93 (2014). http://lawdigitalcommons.bc.edu/bclr Google Scholar
  10. 10.
    Dlodlo, N., et al.: The state of affairs in internet of things research. Electron. J. Inf. Syst. Eval. 15(3), 244–258 (2012)Google Scholar
  11. 11.
    El-Hoiydi, A., Decotignie, J.D.: WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks. In: Proceedings of the Ninth International Symposium on Computers and Communications (ISCC 2004), Alexandria, Egypt, vol. 1, pp. 244–251, 28 June–1 July 2004Google Scholar
  12. 12.
    Perkins, C.E.: Ad Hoc Networking. Addison-Wesley Professional, Boston (2008)Google Scholar
  13. 13.
    Caballero-Gil, P., Caballero-Gil, C., Molina-Gil, J.: How to build vehicular ad-hoc networks on smartphones. J. Syst. Architect. 59, 996–1004 (2013)CrossRefGoogle Scholar
  14. 14.
    Liu, B., Liu, Z., Towsley, D.: On the capacity of hybrid wireless networks. In: Proceedings of the Twenty-Second Annual Joint Conference of the IEEE Computer and Communications on IEEE Societies (INFOCOM 2003), San Francisco, CA, USA, vol. 2, pp. 1543–1552, 30 March–3 April 2003Google Scholar
  15. 15.
    Wang, M., Shan, H., Lu, R., Zhang, R., Shen, X., Bai, F.: Real-time path planning based on hybrid-VANET-enhanced transportation system. IEEE Trans. Veh. Technol. 64, 1664–1678 (2014)CrossRefGoogle Scholar
  16. 16.
    Tornell, S.M., Patra, S., Calafate, C.T., Cano, J.C., Manzoni, P.: GRCBox: extending smartphone connectivity in vehicular networks. Int. J. Distrib. Sens. Netw. 2015, 478064 (2015)CrossRefGoogle Scholar
  17. 17.
    Marquez-Barja, J.M., Ahmadi, H., Tornell, S.M., Calafate, C., Cano, J., Manzoni, P., Da Silva, L.: Breaking the vehicular wireless communications barriers: vertical handover techniques for heterogeneous networks. IEEE Trans. Veh. Technol. 64, 5878–5890 (2014)CrossRefGoogle Scholar
  18. 18.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29, 1645–1660 (2013)CrossRefGoogle Scholar
  19. 19.
    Joerer, S., Bloessl, B., Huber, M., Jamalipour, A., Dressler, F.: Demo: simulating the impact of communication performance on road traffic safety at intersections. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, HI, USA, pp. 287–290, 7–11 September 2014Google Scholar
  20. 20.
    Maglaras, L.A., Basaras, P., Katsaros, D.: Exploiting vehicular communications for reducing CO2 emissions in urban environments. In: Proceedings of the 2013 International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, NV, USA, pp. 32–37, 2–6 December 2013Google Scholar
  21. 21.
    Cheng, H.T., Shan, H., Zhuang, W.: Infotainment and road safety service support in vehicular networking: from a communication perspective. Mech. Syst. Signal Process. 25, 2020–2038 (2011)CrossRefGoogle Scholar
  22. 22.
    Atzori, L., Iera, A., Morabito, G., Nitti, M.: The Social Internet of Things (SIoT)—when social networks meet the internet of things: concept, architecture and network characterization. Comput. Netw. 56, 3594–3608 (2012)CrossRefGoogle Scholar
  23. 23.
    Alam, K., Saini, M., El Saddik, A.: Toward social internet of vehicles: concept, architecture, and applications. IEEE Access 3, 343–357 (2015)CrossRefGoogle Scholar
  24. 24.
    Alam, K.M., Saini, M., Saddik, A.E.: Workload model based dynamic adaptation of social internet of vehicles. Sensors 15, 23262–23285 (2015)CrossRefGoogle Scholar
  25. 25.
    Nitti, M., Girau, R., Floris, A., Atzori, L.: On adding the social dimension to the internet of vehicles: friendship and middleware. In: Proceedings of the 2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Odessa, Ukraine, pp. 134–138, 27–30 May 2014Google Scholar
  26. 26.
    Luan, T., Lu, R., Shen, X., Bai, F.: Social on the road: enabling secure and efficient social networking on highways. IEEE Wirel. Commun. 22, 44–51 (2015)CrossRefGoogle Scholar
  27. 27.
    Schwarz, C., Thomas, G., Nelson, K., McCrary, M., Sclarmann, N., Powell, M.: Towards autonomous vehicles. Technical report 25-1121-0003-117, Mid-America Transportation Center, Lincoln, NE, USA (2013)Google Scholar
  28. 28.
    Squatriglia, C.: Ford’s Tweeting Car Embarks on American Journey 2.0. Wired (2010). http://www.wired.com/2010/05/ford-american-journey/. Accessed 15 Jan 2016
  29. 29.
    Sha, W., Kwak, D., Nath, B., Iftode, L.: Social vehicle navigation: integrating shared driving experience into vehicle navigation. In: Proceedings of the 14th Workshop on Mobile Computing Systems and Applications, Jekyll Island, GA, USA, 26–27 February 2013Google Scholar
  30. 30.
    Wan, J., Zhang, D., Zhao, S., Yang, L., Lloret, J.: Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Commun. Mag. 52, 106–113 (2014)CrossRefGoogle Scholar
  31. 31.
    Vegni, A., Loscri, V.: A Survey on Vehicular Social Networks. IEEE Commun. Surv. Tutor. 17, 2397–2419 (2015)CrossRefGoogle Scholar
  32. 32.
    Al-Sultan, S., Al-Doori, M.M., Al-Bayatti, A.H., Zedan, H.: A comprehensive survey on vehicular ad hoc network. J. Netw. Comput. Appl. 37, 380–392 (2014)CrossRefGoogle Scholar
  33. 33.
    Scott, J.: Social Network Analysis. Sage Publications Ltd., Thousand Oaks (2012)Google Scholar
  34. 34.
    Cunha, F., Carneiro Vianna, A., Mini, R., Loureiro, A.: How effective is to look at a vehicular network under a social perception? In: Proceedings of the 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Lyon, France, pp. 154–159, 7–9 October 2013Google Scholar
  35. 35.
    Basaras, P., Katsaros, D., Tassiulas, L.: Detecting influential spreaders in complex, dynamic networks. Computer 46, 24–29 (2013)CrossRefGoogle Scholar
  36. 36.
    Borge-Holthoefer, J., Rivero, A., Moreno, Y.: Locating privileged spreaders on an online social network. Phys. Rev. E 85 (2011). doi: 10.1103/PhysRevE.85.066123
  37. 37.
    Canright, G.S., Engø-Monsen, K.: Spreading on networks: a topographic view. Complexus 3, 131–146 (2006)CrossRefMATHGoogle Scholar
  38. 38.
    Noel, S., Jajodia, S.: Optimal IDS sensor placement and alert prioritization using attack graphs. J. Netw. Syst. Manag. 16, 259–275 (2008)CrossRefGoogle Scholar
  39. 39.
    Souza, E., Nikolaidis, I., Gburzynski, P.: A new aggregate local mobility (ALM) clustering algorithm for VANETs. In: Proceedings of the 2010 IEEE International Conference on Communications (ICC), Cape Town, South Africa, pp. 1–5, 23–27 May 2010Google Scholar
  40. 40.
    Lazarevic, A., Srivastava, J., Kumar, V.: Cyber threat analysis–a key enabling technology for the objective force (a case study in network intrusion detection). In: Proceedings of the IT/C4ISR, 23rd Army Science Conference (2002)Google Scholar
  41. 41.
    Yu, L., Deng, J., Brooks, R.R., Yun, S.B: Automobile ECU design to avoid data tampering. In: Proceedings of the 10th Annual Cyber and Information Security Research Conference, p. 10. ACM (2015)Google Scholar
  42. 42.
    Sicari, S., Rizzardi, A., Grieco, L., Coen-Porisini, A.: Security, privacy and trust in internet of things: the road ahead. Comput. Netw. 76, 146–164 (2015)CrossRefGoogle Scholar
  43. 43.
    Singh, R., Singh, P., Duhan, M.: An effective implementation of security based algorithmic approach in mobile adhoc networks. Hum. Centric Comput. Inf. Sci. 4(1), 1–14 (2014)CrossRefGoogle Scholar
  44. 44.
    Othmane, L.B., Weffers, H., Mohamad, M.M., Wolf, M.: A survey of security and privacy in connected vehicles. In: Benhaddou, D., Al-Fuqaha, A. (eds.) Wireless Sensor and Mobile Ad-Hoc Networks, pp. 217–247. Springer, New York (2015)Google Scholar
  45. 45.
    Yan, G., Wen, D., Olariu, S., Weigle, M.C.: Security challenges in vehicular cloud computing. IEEE Trans. Intell. Transp. Syst. 14(1), 284–294 (2013)CrossRefGoogle Scholar
  46. 46.
    Kannhavong, B., Nakayama, H., Nemoto, Y., Kato, N., Jamalipour, A.: A survey of routing attacks in mobile ad hoc networks. IEEE Wirel. Commun. 14(5), 85–91 (2007)CrossRefGoogle Scholar
  47. 47.
    Cheng, J., Cheng, J., Zhou, M., Liu, F., Gao, S., Liu, C.: Routing in internet of vehicles: a review. IEEE Trans. Intell. Transp. Syst. 16(5), 2339–2352 (2015)CrossRefGoogle Scholar
  48. 48.
    Shah, N., Valiveti, S.: Intrusion detection systems for the availability attacks in ad-hoc networks. Int. J. Electron. Comput. Sci. Eng. (IJECSE) 1(3), 1850–1857 (2012). ISSN 2277-1956Google Scholar
  49. 49.
    Ji, S., Chen, T., Zhong, S.: Wormhole attack detection algorithms in wireless network coding systems. IEEE Trans. Mob. Comput. 14(3), 660–674 (2015)CrossRefGoogle Scholar
  50. 50.
    Wallgren, L., Raza, S., Voigt, T.: Routing attacks and countermeasures in the RPL-based internet of things. Int. J. Distrib. Sens. Netw. 13(794326) (2013)Google Scholar
  51. 51.
    Xia, H., Jia, Z., Li, X., Ju, L., Sha, E.H.-M.: Trust prediction and trust-based source routing in mobile ad hoc networks. Ad Hoc Netw. 11(7), 2096–2114 (2013)CrossRefGoogle Scholar
  52. 52.
    Mejri, M.N., Ben-Othman, J., Hamdi, M.: Survey on vanet security challenges and possible cryptographic solutions. Veh. Commun. 1(2), 53–66 (2014)CrossRefGoogle Scholar
  53. 53.
    Rawat, D.B., Yan, G., Bista, B., Weigle, M.C.: Trust on the security of wireless vehicular ad-hoc networking. Ad Hoc Sens. Wirel. Netw. (AHSWN) J. 24, 283–305 (2014)Google Scholar
  54. 54.
    Raya, M., Hubaux, J.P.: The security of vehicular ad hoc networks. In: Proceedings of the 3rd ACM Workshop on Security of Ad Hoc and Sensor Networks, Alexandria, VA, USA, pp. 11–21, 7 November 2005Google Scholar
  55. 55.
    Zeadally, S., Hunt, R., Chen, Y.S., Irwin, A., Hassan, A.: Vehicular ad hoc networks (VANETS): Status, results, and challenges. Telecommun. Syst. 50, 217–241 (2012)CrossRefGoogle Scholar
  56. 56.
    Golbeck, J.: Computing with trust: definition, properties, and algorithms. In: Proceedings of the 2006 Securecomm and Workshops, Baltimore, MD, USA, pp. 1–7, 28 August–1 September 2006Google Scholar
  57. 57.
    Golbeck, J.: Computing with Social Trust. HCI. Springer, London (2008)MATHGoogle Scholar
  58. 58.
    Wang, S., Huang, L., Hsu, C.-H., Yang, F.: Collaboration reputation for trustworthy Web service selection in social networks. J. Comput. Syst. Sci. 82(1), 130–143 (2016)MathSciNetCrossRefGoogle Scholar
  59. 59.
    Zhang, D., Hsu, C.H., Chen, M., Chen, Q., Xiong, N., Lloret, J.: Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Trans. Emerg. Top. Comput. 2(2), 239–250 (2014)CrossRefGoogle Scholar
  60. 60.
    Huang, L., Wang, S., Hsu, C.H., et al.: J. Supercomput. 71, 2190 (2015). doi: 10.1007/s11227-015-1432-x CrossRefGoogle Scholar
  61. 61.
    Gupta, S.: A general context-dependent trust model for controlling access to resources. Ph.D. thesis, Jadavpur University, Kolkata, India (2012)Google Scholar
  62. 62.
    Djamaludin, C., Foo, E., Corke, P.: Establishing initial trust in autonomous delay tolerant networks without centralised PKI. Comput. Secur. 39(Part B), 299–314 (2013). ElsevierCrossRefGoogle Scholar
  63. 63.
    Crawford, K., Schultz, J.: Big data and due process: toward a framework to redress predictive privacy harms. BCL Rev. 55, 93 (2014). http://lawdigitalcommons.bc.edu/bclr Google Scholar
  64. 64.
    Tene, O., Polonetsky, J.: Big data for all: privacy and user control in the age of analytics. Nw. J. Tech. Intell. Prop. 11, 239 (2013). http://scholarlycommons.law.northwestern.edu Google Scholar
  65. 65.
    Diebold, F.X.: Big Data Dynamic Factor Models for Macroeconomic Measurement and Forecasting, pp. 115–122. Cambridge University Press, Cambridge (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science, Institute of ScienceBanaras Hindu UniversityVaranasiIndia

Personalised recommendations