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It is a critical matter for a blockchain system whether a Byzantine fault tolerance (BFT) can be guaranteed during a consensus process. Can connected vehicles (CVs) achieve the BFT consensus when the vehicles keep mobile? This paper seeks an answer to this fundamental question. It focuses on characterizing the impact of mobility on the performance of a BFT consensus among CVs.

The huge research interest in cellular vehicle-to-everything (C-V2X) communications in recent days is attributed to their ability to schedule multiple access more efficiently as compared to its predecessor technology, i.e., dedicated short-range communications (DSRC). However, one of the foremost issues still remaining is the need for the V2X to operate stably in a highly dynamic environment. This paper proposes a way to exploit the dynamicity. That is, we propose a resource allocation mechanism adaptive to the environment, which can be an efficient solution for air interface congestion that a V2X network often suffers from. Specifically, the proposed mechanism aims at granting a higher chance of transmission to a vehicle with a higher crash risk. As such, the channel access is prioritized to those with urgent needs. The proposed framework is established based on reinforcement learning (RL), which is modeled as a contextual multi-armed bandit (MAB). Importantly, the framework is designed to operate at a vehicle autonomously without any assistance from a central entity, which, henceforth, is expected to make a particular fit to distributed V2X network such as C-V2X mode 4., Comment: This manuscript will be submitted to IEEE Vehicular Technology Conference 2021 Spring

Due to the recent re-allocation of the 5.9 GHz band by the US Federal Communications Commission (FCC), it will be inevitable that dedicated short-range communications (DSRC) and cellular vehicle-to-everything (C-V2X), two representative technologies for V2X, have to address coexistence. Especially, the distributed nature of DSRC makes it not trivial to control congestions due to an asynchronous structure, which makes it inefficient to disseminate a controlling signal. To this end, we propose a method to "lighten" a distributed V2X networking. Technically, it is to allocate the backoff counter according to the level of an accident risk to which a vehicle is temporarily exposed. We provide an analysis framework based on a spatiotemporal analysis. The numerical results show the improvement in the successful delivery of basic safety messages (BSMs).

The significance of vehicle-to-everything (V2X) communications has been ever increased as connected and autonomous vehicles get more emergent in practice. The key challenge is the dynamicity: each vehicle needs to recognize the frequent changes of the surroundings and apply them to its networking behavior. This is the point where the need for machine learning is highlighted. However, the learning itself is extremely complicated due to the dynamicity as well, which necessitates that the learning framework itself must be resilient and flexible according to the environment. As such, this paper proposes a V2X networking framework integrating reinforcement learning (RL) into scheduling of multiple access. Specifically, the learning mechanism is formulated as a multi-armed bandit (MAB) problem, which enables a vehicle, without any assistance from external infrastructure, to (i) learn the environment, (ii) quantify the accident risk, and (iii) adapt its backoff counter according to the risk. The results of this paper show that the proposed learning protocol is able to (i) evaluate an accident risk close to optimal and (ii) as a result, yields a higher chance of transmission for a dangerous vehicle., Comment: The manuscript will be submitted to IEEE VTC 2020 Fall

Many parties claim the technical significance of Dedicated Short-Range Communications (DSRC) in intelligent transportation system (ITS) for promotion of transportation safety. The main challenge in this key vehicle-to-everything (V2X) standard is high odds of network congestion. Furthermore, in accordance with a V2X network being inherently dynamic in key parameters such as vehicle density and velocity, the networking behavior of a DSRC system is usually highly complicated to analyze. In addition, the United States Federal Communications Commission (US FCC) recently announced the "5.9 GHz band reform," which reduced the dedicated bandwidth for DSRC to 10 MHz from 75 MHz. Motivated from these, the necessity of "lightening" the networking load of a DSRC network has become essential to keep safety-related operations without performance deterioration. To this end, this paper proposes a protocol that prioritizes transmission of a basic safety message (BSM) at a vehicle according to the level of danger that the vehicle experiences. The proposed protocol uses the distance between a danger source and a vehicle as the metric to determine the priority for transmission. Our results show that this protocol prioritizes the transmission opportunity to dangerous vehicles, and hence results in higher performance in terms of key metrics--i.e., average delay, throughput, and inter-reception time ($\mathsf{IRT}$)., Comment: This manuscript will be submitted to IEEE Access