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Spatial Intelligence and Vehicle-to-Vehicle Communication: Topologies and Architectures

  • Kelvin Joseph BwalyaEmail author
Chapter

Abstract

As the developing world positions itself towards implementing Smart Cities, concepts such as intelligent transport systems and spatial intelligence come to the fore. Smart Cities require contemporary pervasive and dynamic topologies and architectures to achieve spatial intelligence which is supported by intelligent transport systems. In such systems, vehicles can communicate with one another using Vehicle-to-Vehicle (V2V) communication models. V2V requires the availability of information on demand and anytime; also, that this information must be accessible in real time by the vehicles as they traverse through the city. Advanced information provision in Smart City environments enable vehicles to exchange information and make intelligent decisions on the roads. A whole array of both functional and non-functional requirements such as usability, aesthetics, security (access, availability and reliability), topology and information architecture, etc. need to be considered to achieve the desired level of spatial intelligence. Putting in place a network to handle the different network dimensions to achieve ubiquity can be significantly costly and beyond the reach of many of the developing world countries. Although, there have been some pockets of research on different aspects of vehicular networks, there is no significant research that brings a great deal of spatial intelligence together. This chapter aims to comprehensively explore the concept of spatial intelligence in the realm of V2V communication. Without carefully thought topologies and architecture, given the context, spatial intelligence in V2V communication cannot be realised. This chapter contributes to knowledge by exploring the different topologies and architectures in mobile agents (vehicles) where cost is one of the key inhibiting factors influencing the actual design.

Keywords

V2V V2V communication Issues Spatial intelligence IoV MANET VANET FANET Topologies Architecture Big data Network Markov decision process Q-learning Fuzzy logic 

References

  1. 1.
    Aliyu A, Abdullah AH, Kaiwartya O, Cao Y, Lloret J, Aslam N, Joda U (2018) Towards video streaming in IoT environments: vehicular communication perspective. Comput Commun 118:93–119CrossRefGoogle Scholar
  2. 2.
    Alouache L, Nguyeny N, Aliouatz M, Chelouah R (2018) Toward a hybrid SDN architecture for V2V communication in IoV environment. In: 2018 fifth international conference on software defined systems (SDS), Barcelona, Spain.  https://doi.org/10.1109/sds.2018.8370428, 23–26 Apr 2018
  3. 3.
    Hajj HM, El-Hajj W, Kabalan KY, El Dana MM, Dakroub M, Fawaz F (2009) An efficient vehicle communication network topology with an extensible framework. https://pdfs.semanticscholar.org/8985/a4ee452f25bcd2d22451fbff3417f0870c53.pdf?_ga=2.92291477.942827893.1558614756-2097276758.1558614756. Accessed 3 July 2019
  4. 4.
    Jawhar I, Mohamed N, Usmani H (2013) An overview of inter-vehicular communication systems, protocols and middleware. J Netw 8(12):2749–2761Google Scholar
  5. 5.
    Lathrop S (2008) Extending cognitive architectures with spatial and visual imagery mechanisms. Unpublished PhD dissertation, University of Michigan, USAGoogle Scholar
  6. 6.
    Raza S, Wang S, Ahmed M, Anwar MR (2019) A survey on vehicular edge computing: architecture, applications, technical issues, and future directions. Wirel Commun Mob Comput 1–19.  https://doi.org/10.1155/2019/3159762. https://www.hindawi.com/journals/wcmc/2019/3159762/. Accessed 3 July 2019Google Scholar
  7. 7.
    Sherazi HHR, Khan ZA, Iqbal R, Rizwan S, Imran MA, Awan K (2019) A heterogeneous IoV architecture for data forwarding in vehicle to infrastructure communication.  https://doi.org/10.1155/2019/3101276. Accessed 3 July 2019CrossRefGoogle Scholar
  8. 8.
    Shi W, Zhou H, Li J, Xu W, Zhang N, Shen XSN (2018) Drone assisted vehicular networks: architecture, challenges and opportunities. IEEE Netw 99:1–8.  https://doi.org/10.1109/mnet.2017.1700206. Accessed 3 July 2019CrossRefGoogle Scholar
  9. 9.
    Storck CR, Duarte-Figueiredo F (2019) 5G V2X ecosystem providing internet of vehicles. Special issue “Recent advances in software-defined internet of vehicles (SDIoV)”. Sensors 19(3):1–20.  https://doi.org/10.3390/s19030550. Accessed 3 July 2019CrossRefGoogle Scholar
  10. 10.
    Wu C, Liu Z, Zhang D, Yoshinaga T, Ji Y (2018) Spatial intelligence towards trustworthy vehicular IoT. IEEE Commun Mag 56(10):22–27.  https://doi.org/10.1109/mcom.2018.1800089. Accessed 2 July 2019CrossRefGoogle Scholar
  11. 11.
    Xu WC, Zhou HB, Cheng N, Lyu F, Shi WS, Chen JY, Shen XM (2018) Internet of vehicles in big data era. IEEE/CAA J Autom Sin 5(1):19–35CrossRefGoogle Scholar
  12. 12.
    Yang FH, Li JL, Lei T, Wang S (2017) Architecture and key technologies for internet of vehicles: a survey. J Commun Inf Netw 2(2):1–17CrossRefGoogle Scholar
  13. 13.
    Ye H, Liang L, Li GY, Kim JB, Lu L, Wu M (2018) Machine learning for vehicular networks. IEEE Veh Technol Mag. https://arxiv.org/pdf/1712.07143.pdf. Accessed 3 July 2019
  14. 14.
    Yi F, Zhang N (2017) A survey on software-defined vehicular networks. J Comput 28(4):236–244.  https://doi.org/10.3966/199115592017062803025CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Consumer Intelligence and Information SystemsUniversity of JohannesburgJohannesburgSouth Africa

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