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A Survey on Software-Defined VANETs: Benefits, Challenges, and Future Directions

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arxiv 1904.04577 v2 pith:LZGDAUUT submitted 2019-04-09 cs.NI

classification cs.NI
keywords architecturesbenefitsfuturesdvnsecuritysurveythreatsvanets
verification ladder T0 review T1 audit T2 compute T3 formal
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The evolving of Fifth Generation (5G) networks isbecoming more readily available as a major driver of the growthof new applications and business models. Vehicular Ad hocNetworks (VANETs) and Software Defined Networking (SDN)represent the key enablers of 5G technology with the developmentof next generation intelligent vehicular networks and applica-tions. In recent years, researchers have focused on the integrationof SDN and VANET, and look at different topics related to thearchitecture, the benefits of software-defined VANET servicesand the new functionalities to adapt them. However, securityand robustness of the complete architecture is still questionableand have been largely negleted. Moreover, the deployment andintegration of novel entities and several architectural componentsdrive new security threats and vulnerabilities.In this paper, first we survey the state-of-the-art SDN basedVehicular ad-hoc Network (SDVN) architectures for their net-working infrastructure design, functionalities, benefits, and chal-lenges. Then we discuss these SDVN architectures against majorsecurity threats that violate the key security services such asavailability, confidentiality, authentication, and data integrity.We also propose different countermeasures to these threats.Finally, we discuss the lessons learned with the directions offuture research work towards provisioning stringent security andprivacy solutions in future SDVN architectures. To the best of ourknowledge, this is the first comprehensive work that presents sucha survey and analysis on SDVNs in the era of future generationnetworks (e.g., 5G, and Information centric networking) andapplications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).

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Cited by 1 Pith paper

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  1. The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models

    cs.CL 2024-12 reject novelty 2.0 of 10

    Specialized language models can lose accuracy on general tasks, a trade-off the paper calls the Rosetta Paradox and tries to quantify with DSI and PIM.

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