Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Review Article

A Survey on Architecture, Applications, and Challenges in Vehicular Fog Computing

Author(s): Deep Chandra Binwal* and Monit Kapoor

Volume 12, Issue 3, 2022

Published on: 14 March, 2022

Page: [194 - 211] Pages: 18

DOI: 10.2174/2210327912666220127130014

Price: $65

Abstract

Connected vehicles are making intelligent transportation system (ITS) a reality, and because of the increase in the onboard computing capability of vehicles, demand for new applications is growing. Vehicular fog computing (VFC) represents a collaborative multitude of onboard vehicular resources to augment the capacity of fog computing. It is an important concept to address numerous issues related to fog computing like, high installation cost, sub-optimal utilization of resources, and developing many novel applications, etc. Vehicular fog computing research has witnessed different architectural, applications, and implementation challenges, studied by various authors. In this paper, we study the architecture of vehicular fog computing and present various unique characteristics of vehicular fog computing along with providing a detailed comparison with conventional fog computing. We analyze VFC implications in ITS with respect to architecture, applications, and challenges. We discuss and analyze the existing applications and research challenges in them. We also explain a novel application use-case of vehicular fog computing, bridging cellular network coverage holes using vehicular fog computing. Finally, we present new and promising applications and open research challenges therein.

Keywords: Fog computing, vehicular fog, vehicular ad-hoc network (VANET), intelligent transportation system (ITS), on-board unit (OBU), latency.

Graphical Abstract

[1]
Zhang J, Wang FY, Wang K, et al. Data-driven intelligent transportation systems: A survey. IEEE Trans Intell Transp Syst 2011; 12: 1624-39.
[http://dx.doi.org/10.1109/TITS.2011.2158001]
[2]
Hussain MSA, Beg MS, Nadeem MMA. Towards minimizing delay and energy consumption in vehicular fog computing (VFC). J Intell Fuzzy Syst 2020; 38: 6549-60.
[http://dx.doi.org/10.3233/JIFS-179735]
[3]
Gross M. A planet with two billion cars. Curr Biol 2016; 26: R307-10.
[http://dx.doi.org/10.1016/j.cub.2016.04.019]
[4]
Alam M, Ferreira J, Fonseca J. Introduction to intelligent transportation systems. Stud Syst Decis Control 2016; 52: 1-17.
[http://dx.doi.org/10.1007/978-3-319-28183-4_1]
[5]
Khattak HA, Farman H, Jan B. Din Ikram Ud. Toward inte-grating vehicular clouds with IoT for smart city services. IEEE Netw 2019; 33: 65-71.
[http://dx.doi.org/10.1109/MNET.2019.1800236]
[6]
Foster I, Kesselman C, Tuecke S. The anatomy of the grid: Enabling scalable virtual organizations. Int J High Perform Comput Appl 2001; 15: 200-22.
[http://dx.doi.org/10.1177/109434200101500302]
[7]
Gu Y, Grossman R. SABUL: A transport protocol for grid computing. J Grid Comput 2003; 1: 377-86.
[http://dx.doi.org/10.1023/B:GRID.0000037553.18581.3b]
[8]
Hussain MM, Beg MMS, Gu Y, Grossman Y, Robert L. UDT: An application level transport protocol for grid computing. J Grid Comput 2003; 1: 86-102.
[9]
Allen G, Davis K, Dolkas KN, et al. Enabling applications on the grid: A GridLab overview. Int J High Perform Comput Appl 2003; 17: 449-66.
[http://dx.doi.org/10.1177/10943420030174008]
[10]
Millán Tejedor R, Esfandiari S. Qué es NFV (Network Func-tions Virtualization). BIT 2014; 196: 18.
[11]
Darwish TSJ, Abu Bakar K. Fog based intelligent transporta-tion big data analytics in the internet of vehicles environment: Motivations, architecture, challenges, and critical issues. IEEE Access 2018; 6: 15679-701.
[http://dx.doi.org/10.1109/ACCESS.2018.2815989]
[12]
Kaiwartya O, Abdullah AH, Cao Y, et al. Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects. IEEE Access 2014; 4: 5356-73.
[http://dx.doi.org/10.1109/ACCESS.2016.2603219]
[13]
Satyanarayanan M. The Emergence of Edge Computing. Comput Published by IEEE Computer Society 2017; 18: 17-20.
[14]
Eltoweissy M, Olariu S, Younis M. Towards autonomous vehicular clouds: A position paper (Invited paper). Lect Notes Inst Comput Sci Soc Telecommun Eng LNICST 2010; 49: 1-16.
[15]
Hussain R, Son J, Eun H, Kim S, Oh H. Rethinking Vehicular Communications: Merging VANET with cloud computing. Cloud- Com 2012 Proc 2012 4th IEEE Int Conf Cloud Comput Technol Sci. 2012; 606-9.
[16]
Sadiku MNO, Tembely M, Musa SM. Internet of vehicles: An introduction. Int J Adv Res Comput Sci Softw Eng 2018; 8: 11.
[http://dx.doi.org/10.23956/ijarcsse.v8i1.512]
[17]
Gasmi R, Aliouat M. Vehicular Ad Hoc NETworks versus internet of vehicles-a comparative view. 4th Int Conf Netw Adv Syst.
[http://dx.doi.org/10.1109/ICNAS.2019.8807870]
[18]
Cheng J, Cheng J, Zhou M, et al. Routing in internet of vehi-cles: A review. IEEE Trans Intell Transp Syst 2015; 16(5): 2339-52. Epub ahead of print
[http://dx.doi.org/10.1109/TITS.2015.2423667]
[19]
Bonomi F. Milito Rodolfo, Natarajan Preethi, Zhu Jiang. Fog Computing: A Platform for Internet of Things and Analytics. Stud Comput Intell 2014; 546: 1-19.
[20]
Bonomi F, Milito R, Zhu J, Addepalli S. Fog computing and its role in the internet of things. MCC’12 - Proc 1st ACM Mob Cloud Comput Work. 2012; 13-5.
[http://dx.doi.org/10.1145/2342509.2342513]
[21]
Jyoti G, Jain Ashish BS. Real-Time VANET applications us-ing fog computing. Proceedings of First International Confer-ence on Smart System. Innovations and Computing 2012; 978-81.
[22]
Hu P, Dhelim S, Ning H, Qui T. Survey on fog computing: Architecture, key technologies, applications and open issues. J Netw Comput Appl 2017; 98: 27-42.
[http://dx.doi.org/10.1016/j.jnca.2017.09.002]
[23]
Mahmud R, Ramamohanarao K, Buyya R. Latency-aware application module management for fog computing environ-ments. ACM Trans Internet Technol 2019; 19: 1-19.
[http://dx.doi.org/10.1145/3186592]
[24]
Khattak HA, Islam SU, Din IU, Guizani M. Integrating fog computing with VANETs: A consumer perspective. IEEE Commun Stand Mag 2019; 3: 19-25.
[http://dx.doi.org/10.1109/MCOMSTD.2019.1800050]
[25]
Kai K, Cong W, Tao L. Fog computing for vehicular Ad-hoc networks: Paradigms, scenarios, and issues. J China Univ Posts Telecommun 2016; 23: 56-65.
[26]
Chen C, Qiu T, Hu J, Ren Z, Zhou Y, Sangaiah A. A conges-tion avoidance game for information exchange on intersec-tions in heterogeneous vehicular networks. J Netw Comput Appl 2017; 85: 116-26.
[http://dx.doi.org/10.1016/j.jnca.2016.12.014]
[27]
Contreras-Castillo J, Zeadally S, Guerrero-Ibanez JA. Internet of vehicles: Architecture, protocols, and security. IEEE Internet Things J 2018; 5: 3701-9.
[http://dx.doi.org/10.1109/JIOT.2017.2690902]
[28]
Hou X, Li Y, Chen M, Wu D, Jin D, Chen S. Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Trans Vehicular Technol 2016; 65: 3860-73.
[http://dx.doi.org/10.1109/TVT.2016.2532863]
[29]
Xiao X, Hou X, Chen X, Liu X, Li C. Quantitative analysis for capabilities of vehicular fog computing. Inf Sci (Ny) 2019; 501: 742-60.
[http://dx.doi.org/10.1016/j.ins.2019.03.065]
[30]
Rahman FH, Yura Muhammad Iqbal A, Newaz SHS, Thien WA, Ahsan M. Street parked vehicles based vehicular fog computing: TCP throughput evaluation and future research di-rection. Int Conf Adv Commun Technol ICACT 2019; 26-31.
[31]
Bennis M, Debbah M, Poor HV. Ultrareliable and low-latency wireless communication: Tail, risk, and scale. Proc IEEE 2018; 106: 1834-53.
[http://dx.doi.org/10.1109/JPROC.2018.2867029]
[32]
Varghese A, Tandur D. Wireless requirements and challenges in Industry 4.0. Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I. 634-8.
[33]
Zhang J, Letaief KB. Mobile edge intelligence and computing for the internet of vehicles. Proc IEEE 2020; 108: 246-61.
[http://dx.doi.org/10.1109/JPROC.2019.2947490]
[34]
Meneguette RI, Boukerche A, Pimenta AHM. AVARAC: An availability-based resource allocation scheme for vehicular cloud. IEEE Trans Intell Transp Syst 2019; 20: 3688-99.
[http://dx.doi.org/10.1109/TITS.2018.2880298]
[35]
Mohanty P, Kumar L, Malakar M, Vishwakarma S, Reza M. Dynamic resource allocation in vehicular cloud computing systems using game theoretic based algorithm. PDGC 2018 - 2018 5th Int Conf Parallel, Distrib Grid Comput. 2018 476-81.
[http://dx.doi.org/10.1109/PDGC.2018.8745913]
[36]
Sathish K, Kumar TA. Resource management techniques in Vehicular Fog Computing  A brief survey. Irish Interdiscipli-nary J Sci Res 2020; 4: 26-34.
[37]
Hoque MA, Hasan R. Towards an analysis of the architecture, security, and privacy issues in Vehicular Fog Computing. Conf Proc - IEEE SOUTHEASTCON. 2019-April.
[38]
Huang C, Lu R, Choo KKR. Vehicular Fog Computing: Archi-tecture, use case, and security and forensic challenges. IEEE Commun Mag 2017; 55: 105-11.
[http://dx.doi.org/10.1109/MCOM.2017.1700322]
[39]
Hussain MM, Beg MMS. CODE-V: Multi-hop computation offloading in Vehicular Fog Computing. Future Gener Comput Syst 2021; 116: 86-102.
[http://dx.doi.org/10.1016/j.future.2020.09.039]
[40]
Baccarelli E, Naranjo PGV, Scarpiniti M, Shojafar M, Abawa-jy J. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study. IEEE Access 2017; 5: 9882-910.
[41]
Vaquero LM, Rodero-Merino L. Finding your way in the fog: Towards a comprehensive definition of fog computing. Comput Commun Rev 2014; 44: 27-32.
[http://dx.doi.org/10.1145/2677046.2677052]
[42]
Barton MILFR, Goren MJMN, Mahmoudi CT. Fog Computing Conceptual Model: Recommendations of the National Institute of Standards and Technology. NIST Spec Publ 2018; 1: 500-325.
[43]
Hong HJ, Tsai PH, Hsu CH. Dynamic module deployment in a fog computing platform. 18th Asia-Pacific Netw Oper Manag Symp APNOMS 2016 Manag Softwarized Infrastruct - Proc. 1-6.
[44]
Mayer R, Graser L, Gupta H, Saurez E, Ramachandran U, et al. EmuFog: Extensible and scalable emulation of large-scale fog computing infrastructures 2017 IEEE Fog World Congr FWC 2017 1-6.
[45]
Yan J, Hasan F. Prospect of Vehicular Fog Computing. Cited 2020 Dec 21. Available from: https://www.researchgate.net
[46]
Huang J, Qian Y, Hu RQ. Security Provision for Vehicular Fog Computing. IEEE Veh Technol Conf 2020; 16-20.
[http://dx.doi.org/10.1109/VTC2020-Spring48590.2020.9129424]
[47]
Xiao Y, Zhu C. Vehicular fog computing: Vision and challenges. 2017 IEEE Int Conf Pervasive Comput Commun Work PerCom Work 2017 6-9.
[48]
Tang C, Wei X, Zhu C, Wang Y, Jia W. Mobile vehicles as fog nodes for latency optimization in smart cities. IEEE Trans Vehicular Technol 2020; 69(9): 9364-75.
[http://dx.doi.org/10.1109/TVT.2020.2970763]
[49]
Xu X, Liu K, Xiao K, Feng L, Wu Z, Guo S. Vehicular Fog Computing enabled real-time collision warning via trajectory calibration. Mob Networks Appl Springer 2020; 25: 2482-94.
[http://dx.doi.org/10.1007/s11036-020-01591-7]
[50]
Du H, Leng S, Wu F, Chen X, Mao S. A new vehicular fog computing architecture for cooperative sensing of autono-mous driving. IEEE Access 8: 10997-1006.
[http://dx.doi.org/10.1109/ACCESS.2020.2964029]
[51]
Sami H, Mourad A. Dynamic on-demand fog formation offering on-the-fly IoT service deployment IEEE eTrans Netw Serv Manag 2020; 17: 1026-39.
[http://dx.doi.org/10.1109/TNSM.2019.2963643]
[52]
Shrestha R, Bajracharya R, Nam SY. Challenges of future VANET and cloud-based approaches wireless communica-tions and mobile computing. Hindawi 2018.
[53]
Sookhak M, Yu FR, He Y, et al. Fog Vehicular Computing. IEEE Veh Technol Mag 2017; 2-11.
[54]
Ning Z, Huang J, Wang X. Vehicular fog computing: Enabling real-time traffic management for smart cities. IEEE Wirel Commun 2019; 26: 87-93.
[http://dx.doi.org/10.1109/MWC.2019.1700441]
[55]
Hussain MM, Alam MS, Beg MMS. Vehicular fog computing-planning and design. Procedia Comput Sci 2020; 167: 2570-80.
[http://dx.doi.org/10.1016/j.procs.2020.03.313]
[56]
Mekki T, Jmal R, Chaari L, Jabri I, Rachedi A. Vehicular fog resource allocation scheme: A multi-objective optimization based approach. 2020 IEEE 17th Annual Consumer Commu-nications & Networking Conference (CCNC). In: IEEE 2020; pp. 1-6.
[http://dx.doi.org/10.1109/CCNC46108.2020.9045361]
[57]
Birhanie HM, Senouc SM, Messous MA, Arfaoui A, Kies A. A stochastic theoretical game approach for resource allocation in vehicular fog computing. 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC). In: IEEE 2020; pp. 1-2.
[http://dx.doi.org/10.1109/CCNC46108.2020.9045224]
[58]
Wu Y, Wu J, Zhou G, Chen L. A direction-based vehicular network model in vehicular fog computing. In: 2018 IEEE Smart- World, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/ IOP/SCI). IEEE 2018; pp. 585-9.
[59]
Nadu T, Nadu T. The fog placed at the perfect position char-acteristics of fog computing. Int J Adv Res Basic Eng Sci Technol Vol 2016; 2: 56-62.
[60]
Xu C, Wang Y, Zhou Z, Gu B, Frascolla V, Mumtaz S. A low-latency and massive-connectivity vehicular fog computing framework for 5G 2018 IEEE Globecom Workshops (GC Wkshps). In: IEEE 2018; pp. 1-6.
[61]
Mekki T, Jabri I, Rachedi A, Jemaa MB. Towards multi-access edge based vehicular fog computing architecture. IEEE Global Communications Conference (GLOBECOM). 1-6.
[http://dx.doi.org/10.1109/GLOCOM.2018.8647850]
[62]
Khan AA, Abolhasan M, Ni W. 5G next generation VANETs using SDN and fog computing framework. CCNC 2018 - 2018 15th IEEE Annu Consum Commun Netw Conf 2018. 2018-Janua; 1-6.
[63]
Khaliq KA, Chughtai O, Shahwani A, Qayyum A, Pannek J. Road accidents detection, data collection and data analysis us-ing V2X communication and edge/cloud computing. Electronics (Basel) 2019; 8(8): 896.
[http://dx.doi.org/10.3390/electronics8080896]
[64]
OpenFog Consortium Architecture Working Group. OpenFog Reference Architecture for Fog Computing. OpenFog 2017; pp. 1-162.
[65]
Liu L, Chen C, Pei Q, Maharjan S, Zhang Y. Vehicular edge computing and networking: A survey. Mob Netw Appl 2021; 26(3): 1145-68.
[http://dx.doi.org/10.1007/s11036-020-01624-1]
[66]
Ran M, Bai X. Vehicle cooperative network model based on hypergraph in vehicular fog computing. Sensors (Basel) 2020; 20(8): 2269.
[http://dx.doi.org/10.3390/s20082269] [PMID: 32316327]
[67]
Hussain R, Zeadally S. Autonomous cars: Research results, issues, and future challenges. IEEE Comm Surv and Tutor 2019; 21: 1275-313.
[http://dx.doi.org/10.1109/COMST.2018.2869360]
[68]
Marín-Tordera E, Masip-Bruin X, García-Almiñana J, Jukan A, Ren GJ, Zhu J. Do we all really know what a fog node is? Current trends towards an open definition. Comput Commun 2017; 109: 117-30.
[http://dx.doi.org/10.1016/j.comcom.2017.05.013]
[69]
Khan AA, Abolhasan M, Ni W, Lipman J, Jamalipour A. A hybrid-fuzzy logic guided genetic algorithm (H-FLGA) ap-proach for resource optimization in 5G VANETs. IEEE Trans Vehicular Technol 2019; 68(7): 6964-74.
[http://dx.doi.org/10.1109/TVT.2019.2915194]
[70]
Fan W, Shi Y, Chen S, Zou L. A mobility metrics based dy-namic clustering algorithm for VANETs. IET Conf Publ. 752-6.
[71]
Kui X, Sun Y, Zhang S, Li Y. Characterizing the capability of vehicular fog computing in large-scale urban environment. Mob Netw Appl 2018; 23(4): 1050-67.
[http://dx.doi.org/10.1007/s11036-017-0969-8]
[72]
Iqbal S, Malik AW, Rahman AU, Noor RM. Blockchain-based reputation management for task offloading in micro-level vehicular fog network IEEE Access 2020; 8: 52968-80
[http://dx.doi.org/10.1109/ACCESS.2020.2979248]
[73]
Liao H, Mu Y, Zhou Z, Sun M, Wang Z, Pan C. Blockchain and learning-based secure and intelligent task offloading for vehicular fog computing. IEEE Trans Intell Transp Syst 2021; 22(7): 4051-63.
[74]
Wu Q, Liu H, Wang R, Fan P, Fan Q, Li Z. Delay-sensitive task offloading in the 802.11 p-based vehicular fog compu-ting systems. IEEE Internet Things J 2019; 7(1): 773-85.
[http://dx.doi.org/10.1109/JIOT.2019.2953047]
[75]
Zhu C, Pastor G, Xiao Y, Ylajaaski A. Vehicular fog compu-ting for video crowdsourcing. IEEE Commun Mag 2018; 56: 58-63.
[http://dx.doi.org/10.1109/MCOM.2018.1800116]
[76]
Zhang Y, Wang CY, Wei HY. Parking reservation auction for parked vehicle assistance in vehicular fog computing. IEEE Trans Vehicular Technol 2019; 68: 3126-39.
[http://dx.doi.org/10.1109/TVT.2019.2899887]
[77]
Zhou S, Sun Y, Jiang Z, Niu Z. Exploiting moving intelli-gence: Delay-optimized computation offloading in vehicular fog networks. IEEE Commun Mag 2019; 57(5): 49-55.
[http://dx.doi.org/10.1109/MCOM.2019.1800230]
[78]
Wang Z, Zhong Z, Ni M. Application-aware offloading policy using smdp in vehicular fog computing systems 2018 IEEE international conference on communications workshops (ICC Workshops). In: IEEE 2018; pp. 1-6.
[http://dx.doi.org/10.1109/ICCW.2018.8403696]
[79]
Vergis S, Komianos V, Tsoumanis G, Tsipis A, Oikonomou K. A low-cost vehicular traffic monitoring system using fog computing. Smart Cities 2020; 3(1): 138-56.
[http://dx.doi.org/10.3390/smartcities3010008]
[80]
Fu F, Kang Y, Zhang Z, Yu FR, Wu T. Soft actor–critic DRL for live transcoding and streaming in vehicular fog-computing-enabled IoV. IEEE Internet Things J 2020; 8(3): 1308-21.
[http://dx.doi.org/10.1109/JIOT.2020.3003398]
[81]
Li M, Zhu L, Zhang Z, Du X, Guizani M. PROS: A privacypreserving route-sharing service via vehicular fog computing IEEE Access 2018; 6: 66188-97
[82]
Baltrunas D, Elmokashfi A, Kvalbein A, Alay Ö. Investigating packet loss in mobile broadband networks under mobility. In 2016 IFIP Networking Conference (IFIP Networking) and Workshops. 2016 May 17; In: 325; 225-33
[http://dx.doi.org/10.1109/IFIPNetworking.2016.7497225]
[83]
Spiotta M, Dudek M, Gazda W, et al. Method for detection of wireless broadband coverage holes. US010827362B2, 2020.
[84]
Ge S, Cheng M, Zhou X. Interference aware service migration in vehicular fog computing IEEE Access 2020; 8: 84272-81
[http://dx.doi.org/10.1109/ACCESS.2020.2992275]
[85]
Liao H, Zhou Z, Zhao X, Ai B, Mumtaz S. Task offloading for vehicular fog computing under information uncertainty: A matching- learning approach. 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). 2019 Jun 24; In: IEEE ; 2001-6.
[http://dx.doi.org/10.1109/IWCMC.2019.8766579]
[86]
Zhou Z, Liao H, Zhao X, Ai B, Guizani M. Reliable task of-floading for vehicular fog computing under information asymmetry and information uncertainty. IEEE Trans Vehicular Technol 2019; 68(9): 8322-35.
[http://dx.doi.org/10.1109/TVT.2019.2926732]
[87]
Zhou Z, Liao H, Wang X, Mumtaz S, Rodriguez J. When ve-hicular fog computing meets autonomous driving: Computa-tional resource management and task offloading. IEEE Netw 2020; 34(6): 70-6.
[http://dx.doi.org/10.1109/MNET.001.1900527]
[88]
Zhu C, Pastor G, Xiao Y, Li Y, Ylae-Jaeaeski A. Fog following me: Latency and quality balanced task allocation in vehicular fog computing. In 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). 2018 Jun 11; In: IEEE; 1-9.
[89]
Wang X, Ning Z, Wang L. Offloading in Internet of vehicles: A fog-enabled real-time traffic management system. IEEE Trans Industr Inform 2018; 14(10): 4568-78.
[http://dx.doi.org/10.1109/TII.2018.2816590]
[90]
Wang Y, Xu C, Zhou Z, Pervaiz H, Mumtaz S. Contract-based resource allocation for low-latency vehicular fog computing. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). 2018 Sep 9; In: IEEE; 812-6
[http://dx.doi.org/10.1109/PIMRC.2018.8580843]
[91]
Peng X, Ota K, Dong M. Multiattribute-based double auction toward resource allocation in vehicular fog computing. IEEE Internet Things J 2020; 7(4): 3094-103.
[http://dx.doi.org/10.1109/JIOT.2020.2965009]
[92]
Kong Q, Su L, Ma M. Achieving privacy-preserving and veri-fiable data sharing in vehicular fog with blockchain. IEEE Trans Intell Transp Syst 2021; 22(8): 4889-98.
[93]
Alamer A, Basudan S. An efficient truthfulness privacy-preserving tendering framework for vehicular fog computing. Eng Appl Artif Intell 2020; 91: 103583.
[http://dx.doi.org/10.1016/j.engappai.2020.103583]
[94]
Sodhro AH, Sodhro GH, Guizani M, Pirbhulal S, Boukerche A. AI-enabled reliable channel modeling architecture for fog computing vehicular networks. IEEE Wirel Commun 2020; 27(2): 14-21.
[http://dx.doi.org/10.1109/MWC.001.1900311]
[95]
Sayed MM, Kashkoush MS, Azab M. Towards Resilient Adaptive Vehicular Fog Computing. 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 2020; 0681-5.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy