Abstract
With the ever-increasing demand for ubiquitous communications from vehicles, there is an increasing request for Internet of Vehicles (IoV). IoV has been envisioned as an enabling technology for the next-generation mobile networks by using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-sensor (V2S) interactions and connections. It is anticipated that IoV will pave the way for supporting real-time applications for road safety, smart and green transportation, location-specific services, and in-vehicle Internet access. However, establishing and maintaining end-to-end connections in an IoV network is challenging due to the high vehicle mobility, dynamic inter-vehicle spacing, and variable vehicle density. This chapter focuses on routing algorithms review for the IoV. First, the background and knowledge of routing algorithms are introduced. Then, a centralized routing scheme with mobility prediction (CRS-MP) for IoV assisted by a software-defined network (SDN) controller powered with artificial intelligence is introduced. Specifically, through advanced artificial neural network (ANN) technique, the SDN controller is able to perform mobility prediction to deal with frequent network topology changes, so the probabilities of successful transmissions and average delay of each vehicle’s request can be estimated by the roadside units (RSUs) or the base station (BS). Mobility prediction is performed based on a stochastic urban traffic model in which the vehicle arrivals follow a non-homogeneous Poisson process (NHPP). The SDN controller collects network information from RSUs and BS, and they are considered as the switches. Since the SDN controller can obtain the global network information, it decides optimal routing paths for switches (i.e., BS and RSU). However, if the source vehicle and destination vehicle are located in the coverage area of the same switch, to minimize the overall service delay, the routing decision will be made by the RSUs or the BS independently, which schedules the requests of vehicles by either V2V or V2I communication, from the source vehicle to the destination vehicle.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
X. Shen, R. Fantacci, S. Chen, Internet of vehicles. Proc. IEEE. 108(2), 242–245 (2020)
W. Xu, H. Zhou, N. Cheng, F. Lyu, W. Shi, J. Chen, X. Shen, Internet of vehicles in big data era. IEEE/CAA J. Autom. Sin. 5(1), 18–35 (2018)
N. Cheng, L. Feng, J. Chen, W. Hu, H. Zhou, S. Zhang, X. Shen, Big data driven vehicular networks. IEEE Netw. 32(6), 160–167 (2018)
FCC, Amendment of the commission’s rules regarding dedicated short-range communication services in the 5.850–5.925 GHz band. FCC Report and order. 06–110 (2006)
Y.L. Morgan, Notes on DSRC & WAVE standards suite: its architecture, design, and characteristics. IEEE Commun. Surv. Tuts. 12(4), 504–518 (2010)
C. Perkins, E. Belding-Royer, S. Das, Ad hoc on-demand distance vector (AODV) routing. RFC 3561 (Experimental) (2003)
T. Clausen, P. Jacquet, Optimized link state routing protocol (OLSR). RFC 3626 (Experimental) (2003)
D. Johnson, Y. Hu, D. Maltz, The dynamic source routing protocol (DSR) for mobile ad hoc networks for IPv4. RFC 4728 (Experimental) (2007)
D. Tian, K. Zheng, J. Zhou, X. Duan, Y. Wang, Z. Sheng, Q. Ni, A microbial inspired routing protocol for VANETs. IEEE Internet Things J. 5(4), 2293–2303 (2018)
N. Alsharif, X. Shen, iCAR-II: infrastructure-based connectivity aware routing in vehicular networks. IEEE Trans. Veh. Technol. 66(5), 4231–4244 (2017)
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)
ONF, Software-defined networking: the new norm for networks. Open Networking Foundation White Paper (2012)
Y. Tang, N. Cheng, W. Wu, M. Wang, Y. Dai, X. Shen, Delay-minimization routing for heterogeneous VANETs with machine learning based mobility prediction. IEEE Trans. Veh. Technol. 68(4), 3967–3979 (2019)
G. Korkmaz, E. Ekici, F. Özgüner, Ü Özgüner, Urban multi-hop broadcast protocol for inter-vehicle communication systems, in Proceedings of 1st ACM International Workshop Vehicular Ad Hoc Network (2004), pp. 76–85
M. Durresi, A. Durresi, L. Barolli, Emergency broadcast protocol for inter-vehicle communications, in Proceddings of 11th International Conference Parallel Distributed Systems (2005), pp. 402–406
S. Fang, T. Luo, A novel two-timer-based broadcast routing algorithm for vehicular ad-hoc networks, in Proceedings of IEEE International Conference on Green Computing and Communications (2013), pp. 1518–1522
C. Celes, R.B. Braga, C.T. De Oliveira, R.M.C. Andrade, A.A.F. Loureiro, GeoSPIN: an approach for geocast routing based on SPatial INformation in VANETs, in Proceedings of IEEE 78th VTC Fall (2013), pp. 1–6
L. Zhang, B. Yu, J. Pan, GeoMobCon: a mobility-contact-aware geocast scheme for urban VANETs. IEEE Trans. Veh. Technol. 65(8), 6715–6730 (2016)
A.M. Mezher, M.A. Igartua, Multimedia multimetric map-aware routing protocol to send video-reporting messages over VANETs in smart cities. IEEE Trans. Veh. Technol. 66(12), 10611–10625 (2017)
N. Li, J.-F. MartÃnez-Ortega, V.H. DÃaz, J.A.S. Fernandez, Probability prediction-based reliable and efficient opportunistic routing algorithm for VANETs. IEEE/ACM Trans. Netw. 26(4), 1933–1947 (2018)
M.H. Eiza, T. Owens, Q. Ni, Q. Shi, Situation-aware QoS routing algorithm for vehicular ad hoc networks. IEEE Trans. Veh. Technol. 64(12), 5520–5535 (2015)
M.A. Togou, A. Hafid, L. Khoukhi, SCRP: stable CDS-based routing protocol for urban vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 17(5), 1298–1307 (2016)
M.A. Salkuyeh, B. Abolhassani, An adaptive multipath geographic routing for video transmission in urban VANETs. IEEE Trans. Intell. Transp. Syst. 17(10), 2822–2831 (2016)
D. Lin, J. Kang, A. Squicciarini, Y. Wu, S. Gurung, O. Tonguz, MoZo: a moving zone based routing protocol using pure V2V communication in VANETs. IEEE Trans. Mobile Comput. 16(5), 1357–1370 (2017)
H. Zhu, X. Lin, R. Lu, Y. Fan, X. Shen, SMART: a secure multilayer credit-based incentive scheme for delay-tolerant networks. IEEE Trans. Veh. Technol. 58(8), 4628–4639 (2009)
H. Li, H. Zhu, S. Du, X. Liang, X. Shen, Privacy leakage of location sharing in mobile social networks: attacks and defense. IEEE Trans. Depend. Sec. Comput. 15(4), 646–660 (2018)
H. Zhu, C. Fang, Y. Liu, C. Chen, M. Li, X. Shen, You can jam but you cannot hide: defending against jamming attacks for geo-location database driven spectrum sharing. IEEE J. Sel. Areas Commun. 34(10), 2723–2737 (2016)
A. Destounis, S. Paris, L. Maggi, G.S. Paschos, J. Leguay, Minimum cost SDN routing with reconfiguration frequency constraints. IEEE/ACM Trans. Netw. 26(4), 1577–1590 (2018)
X. Duan, Y. Liu, X. Wang, SDN enabled 5G-VANET: adaptive vehicle clustering and beamformed transmission for aggregated traffic. IEEE Commun. Mag. 55(7), 120–127 (2017)
K. Liu, J.K.Y. Ng, V.C.S. Lee, S.H. Son, I. Stojmenovic, Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network. IEEE/ACM Trans. Netw. 24(3), 1759–1773 (2016)
Y. Liu, C. Chen, S. Chakraborty, A software defined network architecture for geoBroadcast in VANETs, in Proceedings of IEEE International Conference on Communications (2015), pp. 6559–6564
X. Shen, J.W. Mark, J. Ye, User mobility profile prediction: an adaptive fuzzy inference approach. Wireless Netw. 6(5), 363–374 (2000)
Y. Tang, Q. Zhang W. Lin, Artificial neural network based spectrum sensing method for cognitive radio, in Proceedings of International Conference on Wireless Communications Networking and Mobile Computing (2010), pp. 1–4
H.-F. Yang, T.S. Dillon, Y.-P. Chen, Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2371–2381 (2017)
Y. Lv, Y. Duan, W. Kang, Z. Li, F. Wang, Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Q. Ye, J. Li, K. Qu, W. Zhuang, X. Shen, X. Li, End-to-end quality of service in 5G networks-examining the effectiveness of a network slicing framework. IEEE Veh. Technol. Mag. 13(3), 65–74 (2018)
L. Zhu, C. Li, Y. Wang, Z. Luo, Z. Liu, B. Li, X. Wang, On stochastic analysis of greedy routing in vehicular networks. IEEE Trans. Intell. Transp. Syst. 16(6), 3353–3366 (2015)
S.-C. Kochar, Some results on interarrival times of nonhomogeneous Poisson processes. Probabil. Eng. Inf. Sci. 10(1), 75–85 (1996)
F. Pellerey, M. Shaked, J. Zinn, Nonhomogeneous Poisson processes and logconcavity. Probabil. Eng. Inf. Sci. 14(3), 353–373 (2000)
S. Ucar, S.C. Ergen, O. Ozkasap, Multihop-cluster-based IEEE 802.11p and LTE hybrid architecture for VANET safety message dissemination. IEEE Trans. Veh. Technol. 65(4), 2621–2636 (2016)
L. Baxter, Reliability applications of the relevation transform. Naval Res. Logist. Quart 29(2), 323–329 (1982)
P. Fazio, F.D. Rango, C. Sottile, A predictive cross-layered interference management in a multichannel MAC with reactive routing in VANET. IEEE Trans. Mobile Comput. 15(8), 1850–1862 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tang, Y., Wu, W. (2022). Routing Algorithms for Heterogeneous Vehicular Networks. In: Cai, L., Mark, B.L., Pan, J. (eds) Broadband Communications, Computing, and Control for Ubiquitous Intelligence. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-98064-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-98064-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98063-4
Online ISBN: 978-3-030-98064-1
eBook Packages: Computer ScienceComputer Science (R0)