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Vehicular Communications: A Network Layer Perspective
Open accessAbstract
Vehicular communications, referring to information exchange among vehicles, infrastructures, etc., have attracted a lot of attention recently due to great potential to support intelligent transportation, various safety applications, and on-road infotainment. In this paper, we provide a comprehensive overview of a recent research on enabling efficient and reliable vehicular communications from the network layer perspective. First, we introduce general applications and unique characteristics of vehicular communication networks and the corresponding classifications. Based on different driving patterns, we categorize vehicular networks into manual driving vehicular networks and automated driving vehicular networks, and then discuss the available communication techniques, network structures, routing protocols, and handoff strategies applied in these vehicular networks. Finally, we identify the challenges confronted by the current vehicular networks and present the corresponding research opportunities.
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Rajamani, R. (2012) · Mechanical Engineering Series · doi:10.1007/978-1-4614-1433-9 · Cited by 4,401
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000
Dedicated Short-Range Communications (DSRC) Standards in the United States
Kenney, J. B. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2132790 · Cited by 2,366
Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges
Pathak, P. H., Feng, X., Hu, P., & Mohapatra, P. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2476474 · Cited by 1,550
Vehicular ad hoc networks (VANETS): status, results, and challenges
Zeadally, S., Hunt, R., Chen, Y.-S., Irwin, A., & Hassan, A. (2012) · Telecommunication Systems · doi:10.1007/s11235-010-9400-5 · Cited by 1,208
Routing in vehicular ad hoc networks: A survey
Li, F., & Wang, Y. (2007) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2007.912927 · Cited by 1,124
Machine Learning Paradigms for Next-Generation Wireless Networks
Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., & Hanzo, L. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1500356wc · Cited by 1,095
Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety
Biswas, S., Tatchikou, R., & Dion, F. (2006) · IEEE Communications Magazine · doi:10.1109/mcom.2006.1580935 · Cited by 879
Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing
Choi, J., Va, V., Gonzalez-Prelcic, N., Daniels, R., Bhat, C. R., & Heath, R. W. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.1600071cm · Cited by 794
VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks
Zhao, J., & Cao, G. (2008) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.901869 · Cited by 788
Research works printed but not linked: 30
- 10.1109/TVT.2012.2215969 · doi:10.1109/tvt.2012.2215969
- 10.1109/LAWP.2014.2340994 · doi:10.1109/lawp.2014.2340994
- 10.1109/TVT.2015.2499205 · doi:10.1109/tvt.2015.2499205
- 10.1109/MNET.2016.7513860 · doi:10.1109/mnet.2016.7513860
- lee, 2014, The K-hop cooperative video streaming protocol using H.264/SVC over the hybrid vehicular networks, IEEE Trans Comput, 13, 1338, 10.1109/TMC.2013.153 · doi:10.1109/tmc.2013.153
- 10.1109/TC.2014.2349509 · doi:10.1109/tc.2014.2349509
- 10.1109/TITS.2015.2446771 · doi:10.1109/tits.2015.2446771
- 10.1109/MWC.2010.5450660 · doi:10.1109/mwc.2010.5450660
- 10.1109/MWC.2007.4407231 · doi:10.1109/mwc.2007.4407231
- 10.1109/TPDS.2014.2307852 · doi:10.1109/tpds.2014.2307852
- 10.1109/TVT.2015.2476504 · doi:10.1109/tvt.2015.2476504
- 10.1109/TVT.2009.2024341 · doi:10.1109/tvt.2009.2024341
- 2016, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems-J3016
- 10.1109/JSAC.2014.1411RP02 · doi:10.1109/jsac.2014.1411rp02
- 10.1109/TMC.2014.2341652 · doi:10.1109/tmc.2014.2341652
- 10.1109/MVT.2017.2669349 · doi:10.1109/mvt.2017.2669349
- 10.1109/TVT.2013.2252931 · doi:10.1109/tvt.2013.2252931
- lee, 2009, Vertical handoff decision algorithms for providing optimized performance in heterogeneous wireless networks, IEEE Trans Veh Technol, 58, 865, 10.1109/TVT.2008.925301 · doi:10.1109/tvt.2008.925301
- 10.1109/TAC.2015.2400664 · doi:10.1109/tac.2015.2400664
- 10.1109/5254.683216 · doi:10.1109/5254.683216
- 10.1109/TITS.2006.883939 · doi:10.1109/tits.2006.883939
- 10.1109/TSMCC.2007.913917 · doi:10.1109/tsmcc.2007.913917
- 10.1109/JSAC.2011.110305 · doi:10.1109/jsac.2011.110305
- 10.1109/TITS.2016.2584079 · doi:10.1109/tits.2016.2584079
- 10.1109/MCOM.2017.1601161 · doi:10.1109/mcom.2017.1601161
- 10.1109/JPHOT.2016.2644960 · doi:10.1109/jphot.2016.2644960
- 10.1109/TVT.2015.2443808 · doi:10.1109/tvt.2015.2443808
- 10.1109/VETEC.1997.600460 · doi:10.1109/vetec.1997.600460
- 10.1109/JSAC.2011.110105 · doi:10.1109/jsac.2011.110105
- segata, 0, On platooning control using IEEE 802.11 p in conjunction with visible light communications, Proc Annu Conf Wireless On-Demand Netw Syst Services, 1
Cited by
Deep Reinforcement Learning Based Resource Allocation for V2V Communications
Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870
Self-citationSpectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Liang, L., Ye, H., & Li, G. Y. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933962 · Cited by 559
Self-citationIntegrated Sensing and Communications (ISAC) for Vehicular Communication Networks (VCN)
Cheng, X., Duan, D., Gao, S., & Yang, L. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3191386 · Cited by 304
Machine learning for vehicular networks: Recent advances and application examples
Ye, H., Liang, L., Ye Li, G., Kim, J., Lu, L., & Wu, M. (2018) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2018.2811185 · Cited by 303
Self-citationRaza, S., Wang, S., Ahmed, M., & Anwar, M. R. (2019) · Wireless Communications and Mobile Computing · doi:10.1155/2019/3159762 · Cited by 265
Metadata
- Authors
- Peng, H., Liang, L., Shen, X., & Li, G. Y.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2019-02
- Volume
- 68
- Issue
- 2
- Pages
- 1064-1078
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2964048168
- Cited by
- 304
- References
- 80
- FWCI
- 15.15
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- Funders
- National Science Foundation (1443894, 1731017)
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