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1. When Cryptography Needs a Hand: Practical Post-Quantum Authentication for V2V Communications

   https://doi.org/10.14722/ndss.2024.24267  

   Twardokus, Geoff; Bindel, Nina; Rahbari, Hanif; McCarthy, Sarah (February 2024, Network and Distributed System Security Symposium (NDSS 2024))

   We tackle the atypical challenge of supporting postquantum cryptography (PQC) and its significant overhead in safety-critical vehicle-to-vehicle (V2V) communications, dealing with strict overhead and latency restrictions within the limited radio spectrum for V2V. For example, we show that the current use of spectrum to support signature verification in V2V makes it nearly impossible to adopt PQC. Accordingly, we propose a scheduling technique for message signing certificate transmissions (which we find are currently up to 93% redundant) that learns to adaptively reduce the use of radio spectrum. In combination, we design the first integration of PQC and V2V, which satisfies the above stringent constraints given the available spectrum. Specifically, we analyze the three PQ signature algorithms selected for standardization by NIST, as well as XMSS (RFC 8391), and propose a Partially Hybrid authentication protocol—a tailored fusion of classical cryptography and PQC—for use in the V2V ecosystem during the nascent transition period we outline towards fully PQ V2V. Our provably secure protocol efficiently balances security and performance, as demonstrated experimentally with software-defined radios (USRPs), commercial V2V devices, and road traffic and V2V simulators. We show our joint transmission scheduling optimization and Partially Hybrid design are scalable and reliable under realistic conditions, adding a negligible average delay (0.39 ms per message) against the current state-of-the-art. 

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2. REPLACE: Real-time Security Assurance in Vehicular Platoons Against V2V Attacks

   Boddupalli, S; Hegde, A; Ray, S. (January 2021, 24th IEEE International Conference on Intelligent Transportation (ITSC 2021))

   null (Ed.)

   Connected Autonomous Vehicular (CAV) platoon refers to a group of vehicles that coordinate their movements and operate as a single unit. The vehicle at the head acts as the leader of the platoon and determines the course of the vehicles following it. The follower vehicles utilize Vehicle-to-Vehicle (V2V) communication and automated driving support systems to automatically maintain a small fixed distance between each other. Reliance on V2V communication exposes platoons to several possible malicious attacks which can compromise the safety, stability, and efficiency of the vehicles. We present a novel distributed resiliency architecture, RePLACe for CAV platoon vehicles to defend against adversaries corrupting V2V communication reporting preceding vehicle position. RePLACe is unique in that it can provide real-time defense against a spectrum of communication attacks. RePLACe provides systematic augmentation of a platoon controller architecture with real-time detection and mitigation functionality using machine learning. Unlike computationally intensive cryptographic solutions RePLACe accounts for the limited computation capabilities provided by automotive platforms as well as the real-time requirements of the application. Furthermore, unlike control-theoretic approaches, the same framework works against the broad spectrum of attacks. We also develop a systematic approach for evaluation of resiliency of CAV applications against V2V attacks. We perform extensive experimental evaluation to demonstrate the efficacy of RePLACe. 

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3. Graph-Based Radio Resource Management for Vehicular Networks

   https://doi.org/10.1109/ICC.2018.8422585  

   Liang, L.; Xie, S.; Li, Y. G.; Ding, Z.; Yu, X (May 2018, IEEE International Conference on Communications)

   This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm. 

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4. Vehicle-to-Vehicle Charging Coordination over Information Centric Networking

   https://doi.org/10.1109/LCN53696.2022.9843207  

   Thompson, Robert; Ismail, Muhammad; Shannigrahi, Susmit (September 2022, IEEE LCN)

   Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination. 

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5. Beamforming and Scalable Image Processing in Vehicle-to-Vehicle Networks

   https://doi.org/10.1007/s11265-021-01696-6  

   Ngo, Hieu; Fang, Hua; Wang, Honggang (January 2022, Journal of Signal Processing Systems)

   Vehicle to Vehicle (V2V) communication allows vehicles to wirelessly exchange information on the surrounding environment and enables cooperative perception. It helps prevent accidents, increase the safety of the passengers, and improve the traffic flow efficiency. However, these benefits can only come when the vehicles can communicate with each other in a fast and reliable manner. Therefore, we investigated two areas to improve the communication quality of V2V: First, using beamforming to increase the bandwidth of V2V communication by establishing accurate and stable collaborative beam connection between vehicles on the road; second, ensuring scalable transmission to decrease the amount of data to be transmitted, thus reduce the bandwidth requirements needed for collaborative perception of autonomous driving vehicles. Beamforming in V2V communication can be achieved by utilizing image-based and LIDAR’s 3D data-based vehicle detection and tracking. For vehicle detection and tracking simulation, we tested the Single Shot Multibox Detector deep learning-based object detection method that can achieve a mean Average Precision of 0.837 and the Kalman filter for tracking. For scalable transmission, we simulate the effect of varying pixel resolutions as well as different image compression techniques on the file size of data. Results show that without compression, the file size for only transmitting the bounding boxes containing detected object is up to 10 times less than the original file size. Similar results are also observed when the file is compressed by lossless and lossy compression to varying degrees. Based on these findings using existing databases, the impact of these compression methods and methods of effectively combining feature maps on the performance of object detection and tracking models will be further tested in the real-world autonomous driving system. 

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