automotive network engineers

Abstract: Vehicle-to-Everything (V2X) communications rely on elliptic-curve signatures that a quantum adversary breaks and on pseudonym certificates whose anonymity rests on rotation rate, not cryptography. This work presents qDGSP, a post-quantum V2X infrastructure: a hash-based dynamic group signature scheme (DGSP, over SPHINCS+ and WOTS+) distributed between a central controller and roadside verifiers. Group keys derive deterministically from quantum key distribution (QKD) seeds served over the ETSI GS QKD 014 interface, so no group-signature secret crosses the classical channel. qDGSP-EA (Ephemeral Association) binds an ephemeral ML-DSA-65 key to one group signature per 5G cell: messages remain unlinkable across cells, linkable within one. On a testbed with emulated 5G links, median end-to-end latency stayed within the 100ms ETSI budget for every variant. The qDGSP-EA intra-cell DENM matched the NIST ML-DSA-65 baseline on the wire, and under load EA mode raised delivery from roughly 78% to 93%.

Abstract: In vehicular networks under edge computing environments, vehicle-to-vehicle delay-tolerant networking (V-DTN) can disseminate congestion warnings to other vehicles via a store-carry-forward mechanism, helping them proactively choose suitable routes. However, most existing in-vehicle information dissemination methods rely on flooding or limited flooding strategies, broadcasting alerts across the entire network whenever congestion is detected. This leads to excessive redundant copies and consumes node cache space. To address this issue, this paper proposes a congestion-event- aware on-demand message dissemination mechanism. By considering the recurrence and duration of congestion, the mechanism suppresses broadcasts of short-lived, self-dissipating congestion events. Based on real-world PeMS data from California's I-210 corridor, we construct an I-210 Freeway Traffic Congestion Use Case. Experiments show that predicting congestion recurrence heavily relies on historical free-flow speed data of lanes. Without incorporating additional feature dimensions, the accuracy of modeling and comparing lane free-flow speeds across different day types outweighs the choice of machine learning models, and such patterns are difficult to reproduce in simulators. Meanwhile, congestion duration prediction serves effectively as the basis for dynamic-threshold on-demand dissemination in V-DTN, which demonstrate that machine learning-based congestion awareness combined with dynamic-threshold on-demand dissemination significantly reduces network resource consumption and network-layer congestion, enhances delivery reliability, and lowers delivery latency.

Abstract: Zonal in-vehicle networks ship Ethernet, MACsec, and TSN, but treat the network itself as trusted: once configured at the factory, there is no standardized runtime way to easily revoke access, rotate keys, or contain a compromised ECU. Zero Trust Architecture targets exactly that gap, yet existing automotive ZTA proposals bolt on dedicated infrastructure that duplicates the SDN management plane already required to enable SDVs. Thus, ZTA is not yet adopted in the automotive domain, and the question remains: can we do better? We answer this in two steps. Step 1 analyses what Open Alliance TC17~v1.0 MACsec/MKA with pre-shared CAKs already provides in terms of NIST SP~800-207 ZTA tenets. Step 2 adds CORECONF/YANG management as proposed in Open Alliance TC19, maps the SDN Controller and Agents one-to-one onto NIST's PE, PA, and PEP. We then instantiate this with two YANG-based mechanisms: a network-access-control flow and a key-management scheme. The result fully covers five and two partially of the seven tenets with no ZTA-specific infrastructure added.