Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Introduction

  • Chapter
  • First Online:
The Next Generation Vehicular Networks, Modeling, Algorithm and Applications

Part of the book series: Wireless Networks ((WN))

  • 529 Accesses

Abstract

As one of the most important enabling technologies to realize the next generation intelligent transportation system (ITS), vehicular networks are considered as a set of vehicles embedded with on-board units (OBUs) and road infrastructures (i.e., roadside units (RSUs) and base stations (BSs)). With a radio interface, each OBU can make the connection with other OBUs, RSUs, BSs and other smart devices, by which they can communicate with each other to share useful information with the goal of facilitating the driving and transportation system. Generally, as the typical scenario which is shown in Fig. 1.1, vehicular networks mainly consist of two types: (1) vehicle to vehicle communications, i.e., V2V, and (2) vehicle to roadside infrastructure communications, i.e., V2I.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Z. Xiao, X. Shen, F. Zeng, V. Havyarimana, D. Wang, W. Chen, K. Li, Spectrum resource sharing in heterogeneous vehicular networks: a noncooperative game-theoretic approach with correlated equilibrium. IEEE Trans. Veh. Technol. 67(10), 9449–9458 (2018)

    Article  Google Scholar 

  2. T. Wang, X. Cao, S. Wang, Self-adaptive clustering and load-bandwidth management for uplink enhancement in heterogeneous vehicular networks. IEEE Internet Things J. 6(3), 5607–5617 (2019)

    Article  Google Scholar 

  3. Y. Hui, Z. Su, T.H. Luan, J. Cai, A game theoretic scheme for optimal access control in heterogeneous vehicular networks. IEEE Trans. Intell. Transp. Syst. 20(12), 4590–4603 (2019)

    Article  Google Scholar 

  4. P. Dai, K. Liu, X. Wu, Y. Liao, V.C.S. Lee, S.H. Son, Bandwidth efficiency and service adaptiveness oriented data dissemination in heterogeneous vehicular networks. IEEE Trans. Veh. Technol. 67(7), 6585–6598 (2018)

    Article  Google Scholar 

  5. X. Zhao, X. Li, Z. Xu, T. Chen, An optimal game approach for heterogeneous vehicular network selection with varying network performance. IEEE Intell. Transp. Syst. Mag. 11(3), 80–92 (2019)

    Article  Google Scholar 

  6. W. Xu, W. Shi, F. Lyu, H. Zhou, N. Cheng, X. Shen, Throughput analysis of vehicular internet access via roadside wifi hotspot. IEEE Trans. Veh. Technol. 68(4), 3980–3991 (2019)

    Article  Google Scholar 

  7. L. Liang, H. Ye, G.Y. Li, Toward intelligent vehicular networks: a machine learning framework. IEEE Internet Things J. 6(1), 124–135 (2019)

    Article  Google Scholar 

  8. Y. Hui, Z. Su, S. Guo, Utility based data computing scheme to provide sensing service in internet of things. IEEE Trans. Emerg. Top. Comput. 7(2), 337–348 (2019)

    Article  Google Scholar 

  9. Z. Zhou, J. Feng, Z. Chang, X. Shen, Energy-efficient edge computing service provisioning for vehicular networks: a consensus admm approach. IEEE Trans. Veh. Technol. 68(5), 5087–5099 (2019)

    Article  Google Scholar 

  10. H. Peng, L. Liang, X. Shen, G.Y. Li, Vehicular communications: a network layer perspective. IEEE Trans. Veh. Technol. 68(2), 1064–1078 (2019)

    Article  Google Scholar 

  11. M.A. Togou, L. Khoukhi, A. Hafid, Performance analysis and enhancement of wave for v2v non-safety applications. IEEE Trans. Intell. Transp. Syst. 19(8), 2603–2614 (2018)

    Article  Google Scholar 

  12. S. Darbha, S. Konduri, P.R. Pagilla, Benefits of v2v communication for autonomous and connected vehicles. IEEE Trans. Intell. Transp. Syst. 20(5), 1954–1963 (2019)

    Article  Google Scholar 

  13. J. Mei, K. Zheng, L. Zhao, Y. Teng, X. Wang, A latency and reliability guaranteed resource allocation scheme for lte v2v communication systems. IEEE Trans. Wireless Commun. 17(6), 3850–3860 (2018)

    Article  Google Scholar 

  14. F. Abbas, P. Fan, Z. Khan, A novel low-latency v2v resource allocation scheme based on cellular v2x communications. IEEE Trans. Intell. Transp. Syst. 20(6), 2185–2197 (2019)

    Article  Google Scholar 

  15. P.S. Bithas, A.G. Kanatas, D.B. da Costa, P.K. Upadhyay, U.S. Dias, On the double-generalized gamma statistics and their application to the performance analysis of v2v communications. IEEE Trans. Commun. 66(1), 448–460 (2018)

    Article  Google Scholar 

  16. R. Atallah, M. Khabbaz, C. Assi, Multihop v2i communications: a feasibility study, modeling, and performance analysis. IEEE Trans. Veh. Technol. 66(3), 2801–2810 (2017)

    Article  Google Scholar 

  17. O. Popescu, S. Sha-Mohammad, H. Abdel-Wahab, D.C. Popescu, S. El-Tawab, Automatic incident detection in intelligent transportation systems using aggregation of traffic parameters collected through v2i communications. IEEE Intell. Transp. Syst. Mag. 9(2), 64–75 (2017)

    Article  Google Scholar 

  18. Z. Su, Y. Hui, T.H. Luan, S. Guo, Engineering a game theoretic access for urban vehicular networks. IEEE Trans. Veh. Technol. 66(6), 4602–4615 (2017)

    Article  Google Scholar 

  19. J. Shi, Z. Yang, H. Xu, M. Chen, B. Champagne, Dynamic resource allocation for lte-based vehicle-to-infrastructure networks. IEEE Trans. Veh. Technol. 68(5), 5017–5030 (2019)

    Article  Google Scholar 

  20. F. Jiang, C. Li, Z. Gong, Low complexity and fast processing algorithms for v2i massive mimo uplink detection. IEEE Trans. Veh. Technol. 67(6), 5054–5068 (2018)

    Article  Google Scholar 

  21. A. Boualouache, S. Senouci, S. Moussaoui, A survey on pseudonym changing strategies for vehicular ad-hoc networks. IEEE Commun. Surv. Tutorials 20(1), 770–790 (2018)

    Article  Google Scholar 

  22. P.S. Bithas, G.P. Efthymoglou, A.G. Kanatas, V2V cooperative relaying communications under interference and outdated CSI. IEEE Trans. Veh. Technol. 67(4), 3466–3480 (2018)

    Article  Google Scholar 

  23. Z. Su, Y. Hui, S. Guo, D2d-based content delivery with parked vehicles in vehicular social networks. IEEE Wirel. Commun. 23(4), 90–95 (2016)

    Article  Google Scholar 

  24. D.M. Mughal, J.S. Kim, H. Lee, M.Y. Chung, Performance analysis of v2v communications: a novel scheduling assignment and data transmission scheme. IEEE Trans. Veh. Technol. 68(7), 7045–7056 (2019)

    Article  Google Scholar 

  25. J. Gao, M. Li, L. Zhao, X. Shen, Contention intensity based distributed coordination for v2v safety message broadcast. IEEE Trans. Veh. Technol. 67(12), 12288–12301 (2018)

    Article  Google Scholar 

  26. H. Yao, D. Zeng, H. Huang, S. Guo, A. Barnawi, I. Stojmenovic, Opportunistic offloading of deadline-constrained bulk cellular traffic in vehicular DTNs. IEEE Trans. Comput. 64(12), 3515–3527 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  27. P. Kolios, V. Friderikos, K. Papadaki, Energy-efficient relaying via store-carry and forward within the cell. IEEE Trans. Mobile Comput. 13(1), 202–215 (2014)

    Article  Google Scholar 

  28. J. He, L. Cai, J. Pan, P. Cheng, Delay analysis and routing for two-dimensional vanets using carry-and-forward mechanism. IEEE Trans. Mobile Comput. 16(7), 1830–1841 (2017)

    Article  Google Scholar 

  29. Q. Xu, Z. Su, K. Zhang, P. Ren, X. Shen, Epidemic information dissemination in mobile social networks with opportunistic links. IEEE Trans. Emerg. Top. Comput. 3(3), 399–409 (2015)

    Article  Google Scholar 

  30. K. Zheng, L. Hou, H. Meng, Q. Zheng, N. Lu, L. Lei, Soft-defined heterogeneous vehicular network: architecture and challenges. IEEE Netw. 30(4), 72–80 (2016)

    Article  Google Scholar 

  31. Z. He, J. Cao, X. Liu, SDVN: enabling rapid network innovation for heterogeneous vehicular communication. IEEE Netw. 30(4), 10–15 (2016)

    Article  Google Scholar 

  32. Y. Hui, Z. Su, T.H. Luan, Collaborative content delivery in software-defined heterogeneous vehicular networks. IEEE/ACM Trans. Netw. 28(2), 575–587 (2020)

    Article  Google Scholar 

  33. K. Zheng, Q. Zheng, P. Chatzimisios, W. Xiang, Y. Zhou, Heterogeneous vehicular networking: a survey on architecture, challenges, and solutions. IEEE Commun. Surv. Tutorials 17(4), 2377–2396 (2015)

    Article  Google Scholar 

  34. M. Xing, J. He, L. Cai, Utility maximization for multimedia data dissemination in large-scale vanets. IEEE Trans. Mobile Comput. 16(4), 1188–1198 (2017)

    Article  Google Scholar 

  35. J. Qiao, Y. He, X.S. Shen, Improving video streaming quality in 5g enabled vehicular networks. IEEE Wirel. Commun. 25(2), 133–139 (2018)

    Article  Google Scholar 

  36. J. Guo, B. Song, Y. He, F.R. Yu, M. Sookhak, A survey on compressed sensing in vehicular infotainment systems. IEEE Commun. Surv. Tutorials 19(4), 2662–2680 (2017)

    Article  Google Scholar 

  37. L. Sarakis, T. Orphanoudakis, H.C. Leligou, S. Voliotis, A. Voulkidis, Providing entertainment applications in vanet environments. IEEE Wirel. Commun. 23(1), 30–37 (2016)

    Article  Google Scholar 

  38. E. Costa-Montenegro, F. Quinoy-Garcia, F.J. Gonzalez-castano, F. Gil-Castineira, Vehicular entertainment systems: mobile application enhancement in networked infrastructures. IEEE Veh. Technol. Mag. 7(3), 73–79 (2012)

    Article  Google Scholar 

  39. C. Wang, Y. Li, D. Jin, S. Chen, On the serviceability of mobile vehicular cloudlets in a large-scale urban environment. IEEE Trans. Intell. Transp. Syst. 17(10), 2960–2970 (2016)

    Article  Google Scholar 

  40. T. ETSI, Intelligent transport systems (its); vehicular communications; basic set of applications; definitions, Tech. Rep. ETSI TR 102 638, Tech. Rep., 2009

    Google Scholar 

  41. E. Smith, Statistics on intersection accidents, https://www.autoaccident.com/statistics-on-intersection-accidents.html

  42. F.J. Martinez, C.K. Toh, J.C. Cano, C.T. Calafate, P. Manzoni, Emergency services in future intelligent transportation systems based on vehicular communication networks. IEEE Intell. Transp. Syst. Mag. 2(2), 6–20 (2010)

    Article  Google Scholar 

  43. L. Wang, T. Han, Q. Li, J. Yan, X. Liu, D. Deng, Cell-less communications in 5g vehicular networks based on vehicle-installed access points. IEEE Wirel. Commun. 24(6), 64–71 (2017)

    Article  Google Scholar 

  44. J. Nightingale, P. Salva-Garcia, J.M.A. Calero, Q. Wang, 5g-QoE: QoE modelling for ultra-hd video streaming in 5g networks. IEEE Trans. Broadcast. 64(2), 621–634 (2018)

    Article  Google Scholar 

  45. C. Mao, M. Khalily, P. Xiao, T.W.C. Brown, S. Gao, Planar sub-millimeter-wave array antenna with enhanced gain and reduced sidelobes for 5g broadcast applications. IEEE Trans. Antennas Propag. 67(1), 160–168 (2019)

    Article  Google Scholar 

  46. V. Petrov, M.A. Lema, M. Gapeyenko, K. Antonakoglou, D. Moltchanov, F. Sardis, A. Samuylov, S. Andreev, Y. Koucheryavy, M. Dohler, Achieving end-to-end reliability of mission-critical traffic in softwarized 5g networks. IEEE J. Sel. Areas Commun. 36(3), 485–501 (2018)

    Article  Google Scholar 

  47. T.K. Vu, M. Bennis, M. Debbah, M. Latva-Aho, Joint path selection and rate allocation framework for 5g self-backhauled mm-wave networks. IEEE Trans. Wireless Commun. 18(4), 2431–2445 (2019)

    Article  Google Scholar 

  48. W. Lu, X. Meng, G. Guo, Fast service migration method based on virtual machine technology for MEC. IEEE Internet Things J. 6(3), 4344–4354 (2019)

    Article  Google Scholar 

  49. X. He, R. Jin, H. Dai, Deep PDS-learning for privacy-aware offloading in MEC-enabled IoT. IEEE Internet Things J. 6(3), 4547–4555 (2019)

    Article  Google Scholar 

  50. Z. Ding, P. Fan, H.V. Poor, Impact of non-orthogonal multiple access on the offloading of mobile edge computing. IEEE Trans. Commun. 67(1), 375–390 (2019)

    Article  Google Scholar 

  51. Z. Ning, P. Dong, X. Kong, F. Xia, A cooperative partial computation offloading scheme for mobile edge computing enabled internet of things. IEEE Internet Things J. 6(3), 4804–4814 (2019)

    Article  Google Scholar 

  52. J. Zhang, X. Hu, Z. Ning, E.C. Ngai, L. Zhou, J. Wei, J. Cheng, B. Hu, V.C.M. Leung, Joint resource allocation for latency-sensitive services over mobile edge computing networks with caching. IEEE Internet Things J. 6(3), 4283–4294 (2019)

    Article  Google Scholar 

  53. T.Q. Dinh, Q.D. La, T.Q.S. Quek, H. Shin, Learning for computation offloading in mobile edge computing. IEEE Trans. Commun. 66(12), 6353–6367 (2018)

    Article  Google Scholar 

  54. X. Lyu, W. Ni, H. Tian, R.P. Liu, X. Wang, G.B. Giannakis, A. Paulraj, Optimal schedule of mobile edge computing for internet of things using partial information. IEEE J. Sel. Areas Commun. 35(11), pp. 2606–2615 (2017)

    Article  Google Scholar 

  55. S. Sardellitti, G. Scutari, S. Barbarossa, Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Trans. Signal Inf. Process. Netw. 1(2), 89–103 (2015)

    MathSciNet  Google Scholar 

  56. X. Chen, L. Jiao, W. Li, X. Fu, Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2016)

    Article  Google Scholar 

  57. A. Fischer, J.F. Botero, M.T. Beck, H. de Meer, X. Hesselbach, Virtual network embedding: a survey. IEEE Commun. Surv. Tutorials 15(4), 1888–1906 (2013)

    Article  Google Scholar 

  58. V.G. Nguyen, A. Brunstrom, K.J. Grinnemo, J. Taheri, SDN/NFV-based mobile packet core network architectures: a survey. IEEE Commun. Surv. Tutorials 19(3), 1567–1602 (2017)

    Article  Google Scholar 

  59. X. Cheng, Y. Wu, G. Min, A.Y. Zomaya, Network function virtualization in dynamic networks: a stochastic perspective. IEEE J. Sel. Areas Commun. 36(10), 2218–2232 (2018)

    Article  Google Scholar 

  60. R. Mijumbi, J. Serrat, J. Gorricho, N. Bouten, F. De Turck, R. Boutaba, Network function virtualization: state-of-the-art and research challenges. IEEE Commun. Surv. Tutorials 18(1), 236–262 (2016)

    Article  Google Scholar 

  61. D. Cotroneo, R. Natella, S. Rosiello, NFV-throttle: an overload control framework for network function virtualization. IEEE Trans. Netw. Serv. Manag. 14(4), 949–963 (2017).

    Article  Google Scholar 

  62. R. Mijumbi, J. Serrat, J.L. Gorricho, N. Bouten, F.D. Turck, R. Boutaba, Network function virtualization: state-of-the-art and research challenges. IEEE Commun. Surv. Tutorials 18(1), 236–262 (2015)

    Article  Google Scholar 

  63. B. Han, V. Gopalakrishnan, L. Ji, S. Lee, Network function virtualization: Challenges and opportunities for innovations. IEEE Commun. Mag. 53(2), 90–97 (2015)

    Article  Google Scholar 

  64. T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, D. Sabella, On multi-access edge computing: a survey of the emerging 5g network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutorials 19(3), 1657–1681 (2017)

    Article  Google Scholar 

  65. R. Riggio, A. Bradai, D. Harutyunyan, T. Rasheed, T. Ahmed, Scheduling wireless virtual networks functions. IEEE Trans. Netw. Serv. Manage. 13(2), 240–252 (2016)

    Article  Google Scholar 

  66. M. Zhu, J. Cao, Z. Cai, Z. He, M. Xu, Providing flexible services for heterogeneous vehicles: an NFV-based approach. IEEE Netw. 30(3), 64–71 (2016)

    Article  Google Scholar 

  67. S. Khan, A. Gani, A.W.A. Wahab, M. Guizani, M.K. Khan, Topology discovery in software defined networks: threats, taxonomy, and state-of-the-art. IEEE Commun. Surv. Tutorials 19(1), 303–324 (2016)

    Article  Google Scholar 

  68. S. Khan, A. Gani, A.W.A. Wahab, A. Abdelaziz, K. Ko, M.K. Khan, M. Guizani, Software-defined network forensics: motivation, potential locations, requirements, and challenges. IEEE Netw. 30(6), 6–13 (2016)

    Article  Google Scholar 

  69. M.A. Salahuddin, A. Al-Fuqaha, M. Guizani, Software-defined networking for rsu clouds in support of the internet of vehicles. IEEE Internet Things J. 2(2), 133–144 (2015)

    Article  Google Scholar 

  70. R. Jain, S. Paul, Network virtualization and software defined networking for cloud computing: a survey. IEEE Commun. Mag. 51(11), 24–31 (2013)

    Article  Google Scholar 

  71. D. Kreutz, F.M.V. Ramos, P.E. Verłssimo, C.E. Rothenberg, S. Azodolmolky, S. Uhlig, Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2015)

    Article  Google Scholar 

  72. S. Garg, K. Kaur, S.H. Ahmed, A. Bradai, G. Kaddoum, M. Atiquzzaman, MobQoS: Mobility-aware and QoS-driven SDN framework for autonomous vehicles. IEEE Wirel. Commun. 26(4), 12–20 (2019)

    Article  Google Scholar 

  73. R. Amin, M. Reisslein, N. Shah, Hybrid SDN networks: a survey of existing approaches. IEEE Commun. Surv. Tutorials 20(4), 3259–3306 (2018)

    Article  Google Scholar 

  74. G. Yu, R. Liu, Q. Chen, Z. Tang, A hierarchical sdn architecture for ultra-dense millimeter-wave cellular networks. IEEE Commun. Mag. 56(6), 79–85 (2018)

    Article  Google Scholar 

  75. Z. Su, Q. Xu, H. Zhu, Y. Wang, A novel design for content delivery over software defined mobile social networks. IEEE Netw. 29(4), 62–67 (2015)

    Article  Google Scholar 

  76. K. Wang, Y. Wang, D. Zeng, S. Guo, An SDN-based architecture for next-generation wireless networks. IEEE Wirel. Commun. 24(1), 25–31 (2017)

    Article  Google Scholar 

  77. H. Li, M. Dong, K. Ota, Control plane optimization in software-defined vehicular ad hoc networks. IEEE Trans. Veh. Technol. 65(10), 7895–7904 (2016)

    Article  Google Scholar 

  78. J. Weng, J. Weng, Y. Zhang, W. Luo, W. Lan, BENBI: scalable and dynamic access control on the northbound interface of SDN-based vanet. IEEE Trans. Veh. Technol. 68(1), 822–831 (2019)

    Article  Google Scholar 

  79. K. Liu, L. Feng, P. Dai, V.C.S. Lee, S.H. Son, J. Cao, Coding-assisted broadcast scheduling via memetic computing in SDN-based vehicular networks. IEEE Trans. Intell. Transp. Syst. 19(8), 2420–2431 (2018)

    Article  Google Scholar 

  80. J. Liu, J. Wan, B. Zeng, Q. Wang, H. Song, M. Qiu, A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun. Mag. 55(7), 94–100 (2017)

    Article  Google Scholar 

  81. X. Huang, R. Yu, J. Kang, Z. Xia, Y. Zhang, Software defined networking for energy harvesting internet of things. IEEE Internet Things J. 5(3), 1389–1399 (2018)

    Google Scholar 

  82. A. Lara, A. Kolasani, B. Ramamurthy, Network innovation using openflow: a survey. IEEE Commun. Surv. Tutorials 16(1), 493–512 (2013)

    Article  Google Scholar 

  83. C.J. Bernardos, A. de la Oliva, P. Serrano, A. Banchs, L.M. Contreras, H. Jin, J.C. Zuniga, An architecture for software defined wireless networking. IEEE Wirel. Commun. 21(3), 52–61 (2014)

    Article  Google Scholar 

  84. F. Hu, Q. Hao, K. Bao, A survey on software-defined network and openflow: From concept to implementation. IEEE Commun. Surv. Tutorials 16(4), 2181–2206 (2014)

    Article  Google Scholar 

  85. J. Chen, H. Zhou, N. Zhang, W. Xu, Q. Yu, L. Gui, X. Shen, Service-oriented dynamic connection management for software-defined internet of vehicles. IEEE Trans. Intell. Transp. Syst. 18(10), 2826–2837 (2017)

    Article  Google Scholar 

  86. C. Wang, C. Liang, F.R. Yu, Q. Chen, L. Tang, Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Trans. Wireless Commun. 16(8), 4924–4938 (2017)

    Article  Google Scholar 

  87. J. Zhao, Q. Li, Y. Gong, K. Zhang, Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans. Veh. Technol. 68(8), 7944–7956 (2019)

    Article  Google Scholar 

  88. J. Du, F.R. Yu, X. Chu, J. Feng, G. Lu, Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization. IEEE Trans. Veh. Technol. 68(2), 1079–1092 (2019)

    Article  Google Scholar 

  89. Y. Wu, L.P. Qian, H. Mao, X. Yang, H. Zhou, X. Tan, D.H.K. Tsang, Secrecy-driven resource management for vehicular computation offloading networks. IEEE Netw. 32(3), 84–91 (2018)

    Article  Google Scholar 

  90. Z. Su, Y. Hui, T.H. Luan, Distributed task allocation to enable collaborative autonomous driving with network softwarization. IEEE J. Sel. Areas Commun. 36(10), 2175–2189 (2018)

    Article  Google Scholar 

  91. X. Hou, Y. Li, M. Chen, D. Wu, D. Jin, S. Chen, Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65(6), 3860–3873 (2016)

    Article  Google Scholar 

  92. B. Brik, N. Lagraa, N. Tamani, A. Lakas, Y. Ghamri-Doudane, Renting out cloud services in mobile vehicular cloud. IEEE Trans. Veh. Technol. 67(10), 9882–9895 (2018)

    Article  Google Scholar 

  93. E. Lee, E. Lee, M. Gerla, S.Y. Oh, Vehicular cloud networking: architecture and design principles. IEEE Commun. Mag. 52(2), 148–155 (2014)

    Article  Google Scholar 

  94. S. Wang, J. Wang, X. Wang, T. Qiu, Y. Yuan, L. Ouyang, Y. Guo, F. Wang, Blockchain-powered parallel healthcare systems based on the acp approach. IEEE Trans. Comput. Soc. Syst. 5(4), 942–950 (2018)

    Article  Google Scholar 

  95. D. Liu, A. Alahmadi, J. Ni, X. Lin, X. Shen, Anonymous reputation system for IIoT-enabled retail marketing atop PoS blockchain. IEEE Trans. Ind. Inf. 15(6), 3527–3537 (2019)

    Article  Google Scholar 

  96. P. Danzi, A.E. Kalør, Č. Stefanović, P. Popovski, Delay and communication tradeoffs for blockchain systems with lightweight IoT clients. IEEE Internet Things J. 6(2), 2354–2365 (2019)

    Article  Google Scholar 

  97. M. Liu, F.R. Yu, Y. Teng, V.C.M. Leung, M. Song, Performance optimization for blockchain-enabled industrial internet of things (IIoT) systems: a deep reinforcement learning approach. IEEE Trans. Ind. Inf. 15(6), 3559–3570 (2019)

    Article  Google Scholar 

  98. Y. Sun, L. Zhang, G. Feng, B. Yang, B. Cao, M.A. Imran, Blockchain-enabled wireless internet of things: performance analysis and optimal communication node deployment. IEEE Internet Things J. 6(3), 5791–5802 (2019)

    Article  Google Scholar 

  99. H. Yao, T. Mai, J. Wang, Z. Ji, C. Jiang, Y. Qian, Resource trading in blockchain-based industrial internet of things. IEEE Trans. Ind. Inf. 15(6), 3602–3609 (2019)

    Article  Google Scholar 

  100. J. Wan, J. Li, M. Imran, D. Li, A blockchain-based solution for enhancing security and privacy in smart factory. IEEE Trans. Ind. Inf. 15(6), 3652–3660 (2019)

    Article  Google Scholar 

  101. J. Huang, L. Kong, G. Chen, M. Wu, X. Liu, P. Zeng, Towards secure industrial IoT: Blockchain system with credit-based consensus mechanism. IEEE Trans. Ind. Inf. 15(6), 3680–3689 (2019)

    Article  Google Scholar 

  102. Y. Zhang, S. Kasahara, Y. Shen, X. Jiang, J. Wan, Smart contract-based access control for the internet of things. IEEE Internet Things J. 6(2), 1594–1605 (2019)

    Article  Google Scholar 

  103. Z. Su, Y. Wang, Q. Xu, M. Fei, Y. Tian, N. Zhang, A secure charging scheme for electric vehicles with smart communities in energy blockchain. IEEE Internet Things J. 6(3), 4601–4613 (2019)

    Article  Google Scholar 

  104. J. Pan, J. Wang, A. Hester, I. Alqerm, Y. Liu, Y. Zhao, Edgechain: an edge-IoT framework and prototype based on blockchain and smart contracts. IEEE Internet Things J. 6(3), 4719–4732 (2019)

    Article  Google Scholar 

  105. Z. Yang, K. Yang, L. Lei, K. Zheng, V.C.M. Leung, Blockchain-based decentralized trust management in vehicular networks. IEEE Internet Things J. 6(2), 1495–1505 (2019)

    Article  Google Scholar 

  106. M. Li, L. Zhu, X. Lin, Efficient and privacy-preserving carpooling using blockchain-assisted vehicular fog computing. IEEE Internet Things J. 6(3), 4573–4584 (2019)

    Article  Google Scholar 

  107. T. Jiang, H. Fang, H. Wang, Blockchain-based internet of vehicles: distributed network architecture and performance analysis. IEEE Internet Things J. 6(3), 4640–4649 (2019)

    Article  Google Scholar 

  108. Y. Wang, Z. Su, N. Zhang, BSIS: blockchain-based secure incentive scheme for energy delivery in vehicular energy network. IEEE Trans. Ind. Inf. 15(6), 3620–3631 (2019)

    Article  Google Scholar 

  109. J. Kang, R. Yu, X. Huang, M. Wu, S. Maharjan, S. Xie, Y. Zhang, Blockchain for secure and efficient data sharing in vehicular edge computing and networks. IEEE Internet Things J. 6(3), 4660–4670 (2019)

    Article  Google Scholar 

  110. V. Ortega, F. Bouchmal, J.F. Monserrat, Trusted 5g vehicular networks: blockchains and content-centric networking. IEEE Veh. Technol. Mag. 13(2), 121–127 (2018)

    Article  Google Scholar 

  111. C. Xu, M. Wang, X. Chen, L. Zhong, L.A. Grieco, Optimal information centric caching in 5g device-to-device communications. IEEE Trans. Mobile Comput. 17(9), 2114–2126 (2018)

    Article  Google Scholar 

  112. Y. Zhou, F.R. Yu, J. Chen, Y. Kuo, Resource allocation for information-centric virtualized heterogeneous networks with in-network caching and mobile edge computing. IEEE Trans. Veh. Technol. 66(12), 11339–11351 (2017)

    Article  Google Scholar 

  113. K. Xu, Y. Wan, G. Xue, Powering smart homes with information-centric networking. IEEE Commun. Mag. 57(6), 40–46 (2019)

    Article  Google Scholar 

  114. H. Yao, M. Li, J. Du, P. Zhang, C. Jiang, Z. Han, Artificial intelligence for information-centric networks. IEEE Commun. Mag. 57(6), 47–53 (2019)

    Article  Google Scholar 

  115. C. Liang, F.R. Yu, H. Yao, Z. Han, Virtual resource allocation in information-centric wireless networks with virtualization. IEEE Trans. Veh. Technol. 65(12), 9902–9914 (2016)

    Article  Google Scholar 

  116. G. Xylomenos, C.N. Ververidis, V.A. Siris, N. Fotiou, C. Tsilopoulos, X. Vasilakos, K.V. Katsaros, G.C. Polyzos, A survey of information-centric networking research. IEEE Commun. Surv. Tutorials 16(2), 1024–1049 (2014)

    Article  Google Scholar 

  117. R. Wang, X. Peng, J. Zhang, K.B. Letaief, Mobility-aware caching for content-centric wireless networks: modeling and methodology. IEEE Commun. Mag. 54(8), 77–83 (2016)

    Article  Google Scholar 

  118. H. Asaeda, K. Matsuzono, T. Turletti, Contrace: a tool for measuring and tracing content-centric networks. IEEE Commun. Mag. 53(3), 182–188 (2015)

    Article  Google Scholar 

  119. Z. Su, Q. Xu, Content distribution over content centric mobile social networks in 5g. IEEE Commun. Mag. 53(6), 66–72 (2015)

    Article  Google Scholar 

  120. Q. Wu, Z. Li, G. Tyson, S. Uhlig, M.A. Kaafar, G. Xie, Privacy-aware multipath video caching for content-centric networks. IEEE J. Sel. Areas Commun. 34(8), 2219–2230 (2016)

    Article  Google Scholar 

  121. T. Semertzidis, P. Daras, P. Moore, L. Makris, M.G. Strintzis, Automatic creation of 3d environments from a single sketch using content-centric networks. IEEE Commun. Mag. 49(3), 152–157 (2011)

    Article  Google Scholar 

  122. Z. Su, Y. Hui, Q. Yang, The next generation vehicular networks: a content-centric framework. IEEE Wirel. Commun. 24(1), 60–66 (2017)

    Article  Google Scholar 

  123. A. Mahmood, C.E. Casetti, C.F. Chiasserini, P. Giaccone, J. Harri, The rich prefetching in edge caches for in-order delivery to connected cars. IEEE Trans. Veh. Technol. 68(1), 4–18 (2019)

    Article  Google Scholar 

  124. Z. Su, Y. Hui, Q. Xu, T. Yang, J. Liu, Y. Jia, An edge caching scheme to distribute content in vehicular networks. IEEE Trans. Veh. Technol. 67(6), 5346–5356 (2018)

    Article  Google Scholar 

  125. L.T. Tan, R.Q. Hu, L. Hanzo, Twin-timescale artificial intelligence aided mobility-aware edge caching and computing in vehicular networks. IEEE Trans. Veh. Technol. 68(4), 3086–3099 (2019)

    Article  Google Scholar 

  126. Y. Hui, Z. Su, T.H. Luan, J. Cai, Content in motion: an edge computing based relay scheme for content dissemination in urban vehicular networks. IEEE Trans. Intell. Transp. Syst. 20(8), 3115–3128 (2019)

    Article  Google Scholar 

  127. K. Zhang, S. Leng, Y. He, S. Maharjan, Y. Zhang, Cooperative content caching in 5g networks with mobile edge computing. IEEE Wirel. Commun. 25(3), 80–87 (2018)

    Article  Google Scholar 

  128. Q. Xu, Z. Su, Q. Zheng, M. Luo, B. Dong, Secure content delivery with edge nodes to save caching resources for mobile users in green cities. IEEE Trans. Ind. Inf. 14(6), 2550–2559 (2018)

    Article  Google Scholar 

  129. E. Bastug, M. Bennis, M. Debbah, Living on the edge: the role of proactive caching in 5g wireless networks. IEEE Commun. Mag. 52(8), 82–89 (2014)

    Article  Google Scholar 

  130. N. Li, D.W. Oyler, M. Zhang, Y. Yildiz, I. Kolmanovsky, A.R. Girard, Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. IEEE Trans. Control Syst. Technol. 26(5), 1782–1797 (2018)

    Article  Google Scholar 

  131. J. Petit, S.E. Shladover, Potential cyberattacks on automated vehicles. IEEE Trans. Intell. Transp. Syst. 16(2), 546–556 (2015)

    Google Scholar 

  132. Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, J. Zhang, Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8),1738–1762 (2019)

    Article  Google Scholar 

  133. L. Li, N. Zheng, F. Wang, On the crossroad of artificial intelligence: a revisit to alan turing and norbert wiener. IEEE Trans. Cybern. 49(10), 3618–3626 (2019)

    Article  Google Scholar 

  134. G. Acampora, D.J. Cook, P. Rashidi, A.V. Vasilakos, A survey on ambient intelligence in healthcare. Proc. IEEE 101(12), 2470–2494 (2013)

    Article  Google Scholar 

  135. S. Hussein, P. Kandel, C.W. Bolan, M.B. Wallace, U. Bagci, Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans. Med. Imaging 38(8), 1777–1787 (2019)

    Article  Google Scholar 

  136. L. Shao, D. Wu, X. Li, Learning deep and wide: a spectral method for learning deep networks. IEEE Trans. Neural Netw. Learn. Syst. 25(12), 2303–2308 (2014)

    Article  Google Scholar 

  137. M. Mahmud, M.S. Kaiser, A. Hussain, S. Vassanelli, Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)

    Article  MathSciNet  Google Scholar 

  138. Z. Chen, L. Duan, S. Wang, Y. Lou, T. Huang, D.O. Wu, W. Gao, Toward knowledge as a service over networks: a deep learning model communication paradigm. IEEE J. Sel. Areas Commun. 37(6), 1349–1363 (2019)

    Article  Google Scholar 

  139. Z.M. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, K. Mizutani, State-of-the-art deep learning: evolving machine intelligence toward tomorrows intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19(4), 2432–2455 (2017)

    Article  Google Scholar 

  140. Q. Wang, J. Wan, X. Li, Robust hierarchical deep learning for vehicular management. IEEE Trans. Veh. Technol. 68(5), 4148–4156 (2019)

    Article  Google Scholar 

  141. Q. Qi, J. Wang, Z. Ma, H. Sun, Y. Cao, L. Zhang, J. Liao, Knowledge-driven service offloading decision for vehicular edge computing: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 68(5), 4192–4203 (2019)

    Article  Google Scholar 

  142. R.F. Atallah, C.M. Assi, M.J. Khabbaz, Scheduling the operation of a connected vehicular network using deep reinforcement learning. IEEE Trans. Intell. Transp. Syst. 20(5), 1669–1682 (2019)

    Article  Google Scholar 

  143. X. Liang, X. Du, G. Wang, Z. Han, A deep reinforcement learning network for traffic light cycle control. IEEE Trans. Veh. Technol. 68(2), 1243–1253 (2019)

    Article  Google Scholar 

  144. Y. He, N. Zhao, H. Yin, Integrated networking, caching, and computing for connected vehicles: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 67(1), 44–55 (2018)

    Article  Google Scholar 

  145. Y. Wang, M. Liu, J. Yang, G. Gui, Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shanghai University, Shanghai, China

    Zhou Su, Qiaorong Liu & Rui Xing

  2. Xidian University, Xi’an, Shaanxi, China

    Yilong Hui & Tom H. Luan

Authors
  1. Zhou Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yilong Hui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tom H. Luan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiaorong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Su, Z., Hui, Y., Luan, T.H., Liu, Q., Xing, R. (2021). Introduction. In: The Next Generation Vehicular Networks, Modeling, Algorithm and Applications. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-56827-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-56827-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-56826-9

  • Online ISBN: 978-3-030-56827-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation