Vehicles on the road, where an Intelligent Transportation System (ITS) is built, can share a lot of traffic information and drive more safely and efficiently through data sharing. Since incorrect information misleads vehicles and causes confusion in traffic, a vehicular trust model is needed to check the messages trustworthiness while considering the vehicles properties and protecting its privacy. In this paper, we proposed a two-layer blockchain-based vehicle reputation system, which consists of a local one-day message blockchain and a global vehicle reputation blockchain. It can administrate the reputation score securely and preserve the vehicles partial privacy. The proposed model efficiently manages local traffic information through the local one-day blockchain, reducing the memory overhead of vehicles. As the vehicles actual identity and activities in other areas are hidden by using one-time public keys, partial privacy of the vehicle is preserved. According to the activity of the vehicle, the vehicles reputation score is updated and stored permanently in the global reputation blockchain. We also suggested the location-based practical byzantine fault tolerance (LPBFT), a new consensus algorithm for fast block generating. The LPBFT lowers message propagation time through location-based primary node selection and is about 1.4 times faster than existing PBFT. The simulation results show the efficiency and the feasibility of LPBFT and our proposed protocol.

This systematic review examines the integration of directed acyclic graph (DAG)-based blockchain technology in smart mobility ecosystems, focusing on electric vehicles (EVs), robotic systems, and drone swarms. Adhering to PRISMA guidelines, we conducted a comprehensive literature search across Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, screening 1248 records to identify 47 eligible studies. Our analysis demonstrates that DAG-based blockchain addresses critical limitations of traditional blockchains by enabling parallel transaction processing, achieving high throughput (>1000 TPS), and reducing latency (<1 s), which are essential for real-time applications like autonomous vehicle coordination and microtransactions in EV charging. Key technical challenges include consensus mechanism complexity, probabilistic finality, and vulnerabilities to attacks such as double-spending and Sybil attacks. This study identifies five research priorities: (1) standardized performance benchmarks, (2) formal security proofs for DAG protocols, (3) hybrid consensus models combining DAG with Byzantine fault tolerance, (4) privacy-preserving cryptographic techniques, and (5) optimization of feeless microtransactions. These advancements are critical for deploying robust, scalable DAG-based solutions in smart mobility, and fostering secure and efficient urban transportation networks.

Due to the rising number of Electric Vehicles (EV), the subsequent increase in demand for charging, as well as the long charging time, will make it difficult for drivers to charge their vehicles. Current charging service platforms inform their customers of the charging station information for the network to which they belong. However, they do not ensure the reliability and credibility of information and do not share the charging information of other charging stations. To address this issue, a model in which CSs collaborate for information sharing and charging must be developed. In this paper, we propose a blockchain-based integrated charging platform for charging cooperation. Verifier groups for each CS verify the reliability of shared information. We applied the concept of Mobile Charging Stations (MCSs) to help busy CSs, reducing EV charging latency. We also designed a contribution-based incentive distribution process to elicit active CSs' cooperation. We performed a charging scenario simulation and blockchain implementation to demonstrate the proposed model's efficacy. The simulation results showed that the proposed charging scenario with MCS application showed a 46.9 % reduction in waiting time. Also, when the number of verifier members equals the global average of EVs per charging spot, the verification time is approximately 547ms, demonstrating that the proposed model is effective.