AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Computer Science and Technology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Survey

Edge Computing Based Applications in Vehicular Environments: Comparative Study and Main Issues

LaBRI Laboratory, University of Bordeaux, Talence 33400, France
IRISA Laboratory, University of Rennes 1, Lannion 22300, France
LaBRI Laboratory, Bordeaux INP, Talence 33400, France
Show Author Information

Abstract

Despite the expanded efforts, the vehicular ad-hoc networks (VANETs) are still facing many challenges such as network performances, network scalability and context-awareness. Many solutions have been proposed to overcome these obstacles, and the edge computing, an extension of the cloud computing, is one of them. With edge computing, communication, storage and computational capabilities are brought closer to end users. This could offer many benefits to the global vehicular network including, for example, lower latency, network off-loading and context-awareness (location, environment factors, etc.). Different approaches of edge computing have been developed: mobile edge computing (MEC), fog computing (FC) and cloudlet are the main ones. After introducing the vehicular environment background, this paper aims to study and compare these different technologies. For that purpose their main features are compared and the state-of-the-art applications in VANETs are analyzed. In addition, MEC, FC, and cloudlet are classified and their suitability level is debated for different types of vehicular applications. Finally, some challenges and future research directions in the fields of edge computing and VANETs are discussed.

Keywords

vehicular networkedge computingcloud computingfog computingcloudlet

Electronic Supplementary Material

Download File(s)
jcst-34-4-869-Highlights.pdf (181.4 KB)
jcst-34-4-869_ESM.pdf (37.4 KB)

References

[1]

Vijayakumar P, Azees M, Kannan A, Deborah L J. Dual authentication and key management techniques for secure data transmission in vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4): 1015-1028.
Crossref Google Scholar
[2]

Zheng K, Zheng Q, Chatzimisios P, Xiang W, Zhou Y. Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2377-2396.
Crossref Google Scholar
[3]
Bouali T, Senouci S M. A fuzzy logic-based communication medium selection for QoS preservation in vehicular networks. In Proc. the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, November 2015, pp.101-108.
Crossref
[4]

He D, Zeadally S, Xu B, Huang X. An efficient identity-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks. IEEE Transactions on Information Forensics and Security, 2015, 10(12): 2681-2691.
Crossref Google Scholar
[5]

La Vinh H, Cavalli A. Security attacks and solutions in vehicular ad hoc networks: A survey. International Journal on Ad Hoc Networking Systems, 2014, 4(2): 1-20.
Crossref Google Scholar
[6]

Raw R S, Kumar M, Singh N. Security challenges, issues and their solutions for VANET. International Journal of Network Security & Its Applications, 2013, 5(5): 95-105.
Crossref Google Scholar
[7]

Liang W, Li Z, Zhang H, Wang S, Bie R. Vehicular ad hoc networks: Architectures, research issues, methodologies, challenges, and trends. International Journal of Distributed Sensor Networks, 2015, 11: Article No. 745303.
Crossref Google Scholar
[8]
Rasheed A, Gillani S, Ajmal S, Qayyum A. Vehicular ad hoc network (VANET): A survey, challenges, and applications. In Proc. the 2nd Int. Workshop on Vehicular Ad-Hoc Networks for Smart Cities, April 2016, pp.39-51.
Crossref
[9]
Hussain R, Son J, Eun H, Kim S, Oh H. Rethinking vehicular communications: Merging VANET with cloud computing. In Proc. the 4th IEEE International Conference on Cloud Computing Technology and Science, December 2012, pp.606-609.
Crossref
[10]

Jabbarpour M R, Marefat A, Jalooli A, Zarrabi H. Could-based vehicular networks: A taxonomy, survey, and conceptual hybrid architecture. Wireless Networks, 2019, 25(1): 335-354.
Crossref Google Scholar
[11]

Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: Vision and challenges. IEEE Internet of Things Journal, 2016, 3(5): 637-646.
Crossref Google Scholar
[12]
Ha K, Pillai P, Lewis G, Simanta S, Clinch S, Davies N, Satyanarayanan M. The impact of mobile multimedia applications on data center consolidation. In Proc. the 2013 IEEE International Conference on Cloud Engineering, March 2013, pp.166-176.
Crossref
[13]
Dolui K, Datta S K. Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In Proc. the 2017 Global Internet of Things Summit, June 2017, Article No. 77.
Crossref
[14]

Baktir A C, Ozgovde A, Ersoy C. How can edge computing benefit from software-defined networking: A survey, use cases, and future directions. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2359-2391.
Crossref Google Scholar
[15]

Wang S, Zhang X, Zhang Y, Wang L, Yang J, Wang W. A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access, 2017, 5: 6757-6779.
Crossref Google Scholar
[16]
Borcoci E, Vochin M, Obreja S. Mobile edge computing versus fog computing in Internet of Vehicles. In Proc. the 10th International Conference on Advances in Future Internet, September 2018, pp.8-15.
[17]

Yaqoob I, Ahmad I, Ahmed E, Gani A, Imran M, Guizani N. Overcoming the key challenges to establishing vehicular communication: Is SDN the answer? IEEE Communications Magazine, 2017, 55(7): 128-134.
Crossref Google Scholar
[18]

Huang X, Yu R, Kang J, He Y, Zhang Y. Exploring mobile edge computing for 5G-enabled software defined vehicular networks. IEEE Wireless Communications, 2017, 24(6): 55-63.
Crossref Google Scholar
[19]
Anaya J J, Merdrignac P, Shagdar O, Nashashibi F, Naranjo J E. Vehicle to pedestrian communications for protection of vulnerable road users. In Proc. the 2014 IEEE Intelligent Vehicles Symposium, June 2014, pp.1037-1042.
Crossref
[20]

Ota Y, Taniguchi H, Nakajima T, Liyanage K M, Baba J, Yokoyama A. Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging. IEEE Transactions on Smart Grid, 2012, 3(1): 559-564.
Crossref Google Scholar
[21]

Tomar R, Prateek M, Sastry G H. Vehicular ad hoc network (vanet) — An introduction. International Journal of Control Theory and Applications, 2016, 9(18): 8883-8888.
Google Scholar
[22]
Bergenhem C, Shladover S, Coelingh E, Englund C, Tsug-Awa S. Overview of platooning systems. In Proc. the 19th ITS World Congress, October 2012, Article No. EU-00336.
[23]

Fagnant D J, Kockelman K M. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transportation Research Part C: Emerging Technologies, 2014, 40: 1-13.
Crossref Google Scholar
[24]

Umamaheswari S, Priya R M. An efficient healthcare monitoring system in vehicular ad hoc networks. International Journal of Computer Applications, 2013, 78(7): 45-49.
Crossref Google Scholar
[25]
Liu L, Zhang X, Qiao M, Shi W. SafeShareRide: Edge-based attack detection in ridesharing services. In Proc. the 2018 IEEE/ACM Symposium on Edge Computing, October 2018, pp.17-29.
Crossref
[26]

Satyanarayanan M, Bahl P, Cáceres R, Davies N. The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing, 2009, 8(4): 14-23.
Crossref Google Scholar
[27]

Satyanarayanan M. The emergence of edge computing. IEEE Computer, 2017, 50(1): 30-39.
Crossref Google Scholar
[28]

Bilal K, Khalid O, Erbad A, Khan S U. Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Computer Networks, 2018, 130: 94-120.
Crossref Google Scholar
[29]
Jelassi S, Bouzid A, Youssef H. QoE-driven video streaming system over cloud-based VANET. In Proc. the 8th International Workshop on Communication Technologies for Vehicles, May 2015, pp.84-93.
Crossref
[30]
Garai M, Rekhis S, Boudriga N. Communication as a service for cloud VANETs. In Proc. the 2015 IEEE Symposium on Computers and Communication, July 2015, pp.371-377.
Crossref
[31]

Yu R, Ding J, Huang X, Zhou M T, Gjessing S, Zhang Y. Optimal resource sharing in 5G-enabled vehicular networks: A matrix game approach. IEEE Transactions on Vehicular Technology, 2016, 65(10): 7844-7856.
Crossref Google Scholar
[32]
Otomo M, Sato G, Shibata Y. In-vehicle cloudlet computing based on delay tolerant network protocol for disaster information system. In Proc. the 11th International Conference on Broad-Band and Wireless Computing, Communication and Applications, November 2016, pp.255-266.
Crossref
[33]
Hagenauer F, Sommer C, Higuchi T, Altintas O, Dressler F. Vehicular micro clouds as virtual edge servers for efficient data collection. In Proc. the 2nd ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services, October 2017, pp.31-35.
Crossref
[34]

Wang C, Li Y, Jin D, Chen S. On the serviceability of mobile vehicular cloudlets in a large-scale urban environment. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2960-2970.
Crossref Google Scholar
[35]
Li L, Li Y, Hou R. A novel mobile edge computing-based architecture for future cellular vehicular networks. In Proc. the 2017 IEEE Wireless Communications and Networking Conference, March 2017, Article No. 352.
Crossref
[36]

Yuan Q, Zhou H, Li J, Liu Z, Yang F, Shen X S. Toward efficient content delivery for automated driving services: An edge computing solution. IEEE Network, 2018, 32(1): 80-86.
Crossref Google Scholar
[37]

Liu J, Wan J, Zeng B, Wang Q, Song H, Qiu M. A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Communications Magazine, 2017, 55(7): 94-100.
Crossref Google Scholar
[38]

Zhang K, Mao Y, Leng S, He Y, Zhang Y. Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading. IEEE Vehicular Technology Magazine, 2017, 12(2): 36-44.
Crossref Google Scholar
[39]

Huang C M, Chiang M S, Dao D T, Su W L, Xu S, Zhou H. V2V data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture. IEEE Access, 2018, 6: 17741-17755.
Crossref Google Scholar
[40]
Vigneri L, Spyropoulos T, Barakat C. Quality of experience-aware mobile edge caching through a vehicular cloud. In Proc. the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, November 2017, pp.91-98.
Crossref
[41]
Datta S K, Bonnet C, Haerri J. Fog computing architecture to enable consumer centric Internet of things services. In Proc. the 2015 International Symposium on Consumer Electronics, June 2015, Article No. 15.
Crossref
[42]

Wang X, Ning Z, Wang L. Offloading in Internet of Vehicles: A fog-enabled real-time traffic management system. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4568-4578.
Crossref Google Scholar
[43]

Sookhak M, Yu F R, He Y, Talebian H, Safa N S, Zhao N, Khan M K, Kumar N. Fog vehicular computing: Augmentation of fog computing using vehicular cloud computing. IEEE Vehicular Technology Magazine, 2017, 12(3): 55-64.
Crossref Google Scholar
[44]

Hou X, Li Y, Chen M, Wu D, Jin D, Chen S. Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Transactions on Vehicular Technology, 2016, 65(6): 3860-3873.
Crossref Google Scholar
[45]

Zhang W, Zhang Z, Chao H C. Cooperative fog computing for dealing with big data in the Internet of Vehicles: Architecture and hierarchical resource management. IEEE Communications Magazine, 2017, 55(12): 60-67.
Crossref Google Scholar
[46]

Darwish T S, Bakar K A. Fog based intelligent transportation big data analytics in the Internet of Vehicles environment: Motivations, architecture, challenges, and critical issues. IEEE Access, 2018, 6: 15679-15701.
Crossref Google Scholar
[47]

Yu R, Zhang Y, Gjessing S, Xia W, Yang K. Toward cloud-based vehicular networks with efficient resource management. IEEE Network, 2013, 27(5): 48-55.
Crossref Google Scholar
[48]
Grover J, Jain A, Singhal S, Yadav A. Real-time VANET applications using fog computing. In Proc. the 1st International Conference on Smart System, Innovations and Computing, April 2017, pp.683-691.
Crossref
[49]

Hasrouny H, Samhat A E, Bassil C, Laouiti A. VANet security challenges and solutions: A survey. Vehicular Communications, 2017, 7: 7-20.
Crossref Google Scholar
[50]

Wang L, Liu G, Sun L. A secure and privacy-preserving navigation scheme using spatial crowdsourcing in fog-based VANETs. Sensors, 2017, 17(4): Article No. 668.
Crossref Google Scholar
[51]

Mukherjee M, Matam R, Shu L, Maglaras L, Ferrag M A, Choudhury N, Kumar V. Security and privacy in fog computing: Challenges. IEEE Access, 2017, 5: 19293-19304.
Crossref Google Scholar
[52]

Huang C, Lu R, Choo K K R. Vehicular fog computing: Architecture, use case, and security and forensic challenges. IEEE Communications Magazine, 2017, 55(11): 105-111.
Crossref Google Scholar
[53]

Shaukat U, Ahmed E, Anwar Z, Xia F. Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges. Journal of Network and Computer Applications, 2016, 62: 18-40.
Crossref Google Scholar
[54]

Alouache L, Nguyen N, Aliouat M, Chelouah R. Nouveau protocole robuste pour les communications dans l’IoV. Internet des objets, 2017, 17-1(1): 3-19. (in French)
Crossref Google Scholar
[55]

Oliveira R, Montez C, Boukerche A, Wangham M S. Reliable data dissemination protocol for VANET traffic safety applications. Ad Hoc Networks, 2017, 63: 30-44.
Crossref Google Scholar
[56]

Rasool I U, Zikria Y B, Kim S W. A review of wireless access vehicular environment multichannel operational medium access control protocols: Quality-of-service analysis and other related issues. International Journal of Distributed Sensor Networks, 2017, 13(5): Article No. 23.
Crossref Google Scholar
[57]
Li H, Shou G, Hu Y, Guo Z. Mobile edge computing: Progress and challenges. In Proc. the 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, March 2016, pp.83-84.
Crossref
[58]

Mekki T, Jabri I, Rachedi A, ben Jemaa M. Vehicular cloud networks: Challenges, architectures, and future directions. Vehicular Communications, 2017, 9: 268-280.
Crossref Google Scholar
[59]
Skarlat O, Nardelli M, Schulte S, Dustdar S. Towards QoS-aware fog service placement. In Proc. the 1st IEEE International Conference on Fog and Edge Computing, May 2017, pp.89-96.
Crossref
[60]

Jiang Y, Huang Z, Tsang D H. Challenges and solutions in fog computing orchestration. IEEE Network, 2018, 32(3): 122-129.
Crossref Google Scholar
[61]

Ahmed E, Rehmani M H. Mobile edge computing: Opportunities, solutions, and challenges. Future Generation Computer Systems, 2017, 70: 59-63.
Crossref Google Scholar
[62]

Zhang H, Liu N, Chu X, Long K, Aghvami A H, Leung V C. Network slicing based 5G and future mobile networks: Mobility, resource management, and challenges. IEEE Communications Magazine, 2017, 55(8): 138-145.
Crossref Google Scholar
[63]
Chang C Y, Alexandris K, Nikaein N, Katsalis K, Spyropoulos T. MEC architectural implications for LTE/LTEA networks. In Proc. the 2016 Workshop on Mobility in the Evolving Internet Architecture, October 2016, pp.13-18.
Crossref
Journal of Computer Science and Technology
Volume 34 Issue 4,
July 2019
Pages 869-886
DOI: 10.1007/s11390-019-1947-3
Cite this article:
Mendiboure L, Chalouf M-A, Krief F. Edge Computing Based Applications in Vehicular Environments: Comparative Study and Main Issues. Journal of Computer Science and Technology, 2019, 34(4): 869-886. https://doi.org/10.1007/s11390-019-1947-3

355

Views

32

Crossref

N/A

Web of Science

32

Scopus

2

CSCD

Altmetrics
Received: 20 August 2018
Revised: 15 May 2019
Published: 19 July 2019
©2019 Springer Science + Business Media, LLC & Science Press, China
Return