Shuchi Priya, Sushil Kumar, Anjani Anjani, Ahmad M. Khasawneh · 5 authors
Smart vehicles integrated with the Internet of Things (IoT) provide rich data for traffic management, safety, and liability services; however, existing blockchain-enabled vehicular architectures still struggle with consensus scalability, heavy centralized validation, limited interaction-based corroboration, incomplete attack coverage, and rapid ledger growth. In particular, many schemes either optimize single-layer consensus or embed detailed reputation information into every transaction, while pushing most validation to central servers. This leads to bottlenecks under dense traffic and leaves replay, Sybil-assisted 51% attacks on roadside units (RSUs), and man-in-the-middle tampering only partially addressed. In this context, this paper proposes a novel hierarchical blockchain for vehicular IoT (HBV-IoT) model to address the above challenges. An independent transaction for periodic vehicle status reporting and an interaction-based transaction for corroborating data between vehicles in proximity are presented. Three smart contracts are designed to automate the validation and processing of transactions, and to identify compromised or malicious vehicles within the HBV-IoT network. Algorithms for distributed consensus to accept transactions into the blockchain and for vehicle reputation management to enforce edge-level filtering and down-weighting of malicious nodes are implemented. Simulation results demonstrate significant improvements compared to conventional vehicular blockchain approaches, with performance gains validated by 95% confidence intervals. The model supports practical applications, including real-time traffic monitoring, automated e-challan issuance, intelligent insurance claim processing, and blockchain-based vehicle registration.
This study proposes a mobility control architecture that replaces conventional physical distance-based autonomous driving with a network-based logical spatiotemporal occupancy reservation system. Instead of relying solely on onboard sensors, vehicles reserve future movement regions as packetized occupancy rights across a communication infrastructure. A multi-layer consensus mechanism—combining efficiency, safety, and legal validation algorithms—determines vehicle behavior, while smart contracts and distributed ledgers record risk evaluation and liability allocation in real time. This approach enables high-density traffic flow beyond traditional braking constraints, improves road utilization efficiency, and enhances legal accountability in autonomous mobility networks. The framework also integrates infrastructure routing and fail-safe fallback mechanisms and relies on the Quantum Thought Circuit OS ASI architecture for deterministic decision control.
The application of Distributed Ledger Technology in Intelligent Transportation Systems ensures a secure and decentralized mechanism for data sharing among vehicles and infrastructure. However, DAG based protocols such as the IOTA Tangle when applied to autonomous vehicular systems face significant challenges in ledger consistency, transaction confirmation, and convergence due to the highly dynamic and intermittently connected nature of vehicular networks. To address these limitations, this paper proposes a Connectivity Aware Intelligent Distributed Ledger Construction Model tailored for autonomous vehicular environments. The proposed framework integrates a connectivity aware tip selection mechanism with a reinforcement learning strategy based on sliding window bias thompson sampling to dynamically select optimal ledger construction actions under non-stationary network conditions. Vehicular connectivity is modeled using an alternating renewal process, and transaction arrivals by a nonstationary Poisson process. Extensive simulation results demonstrate that CA-IDLCM outperforms URTS, MCMC, and Biased-TS approaches by achieving confirmation rate of 95.8%, and maintaining the orphan fraction well below 2%, with tip counts stabilizing near 1.0. highlighting its robustness, adaptability, and suitability for next generation Intelligent Transportation Systems.
With the acceleration of urbanization and the promotion of the “dual carbon” goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.
Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints. These steps require expensive hardware (e.g., GPUs), and finality is reached only once all stages complete and results are posted on-chain. As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs. We base our study on Halo2-based proving systems and identify transactions per second (TPS), average gas usage, and finality time as key cost drivers. To address this, we propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load. We formulate this model as a constraint system and solve it using the Z3 SMT solver to find cost-optimal configurations. To validate our approach, we implement a simulator that detects lag and estimates operational costs. Our method shows a potential cost reduction of up to 70\%.
ABSTRACT The Internet of Vehicles (IoV) is a critical component of the smart city. Various nodes exchange sensitive data for urban mobility, such as identification, position, messages, speed, and traffic statistics. Along with developing smart cities come threats to privacy and security through networks. Security is of the highest priority, considering various security‐privacy risks from the wellness, safety, and confidentiality of men and women inside the vehicle. This survey presents a detailed analysis of state‐of‐the‐art and evolving security challenges to IoV systems. It handles security challenges, such as data integrity and privacy. It also includes a critical review of the literature to identify gaps in current security mechanisms. It uses complete mathematical modeling and case studies to show the practical effectiveness of the proposed solutions. It aims to guide future development and implementation of more secure, efficient, and resilient IoV systems, particularly in smart city environments. It also introduces a novel Intrusion Detection System (IDS) with Artificial Intelligence (AI), smart contracts, and blockchain technology. These smart contracts ensure instant security with the utmost level of vulnerability through blockchain technology. In addition, we proposed a hybrid multi‐layered framework using Fog to conserve the resources at the vehicle level. We used mathematical proof to assess this framework. Merging blockchain, smart contracts, and AI into IoVs could increase human security by removing significant vulnerabilities.
Optimistic Rollup is a blockchain scalability solution. However, it assumes that transactions are correct unless challenged by a challenger, which leads to the concept of a challenge period. If there is no challenge during this time, the user needs to wait until the end of the challenge period, which is very inconvenient for the user. This is because challengers and task publishers are self-interested and aim to maximize their own benefits, leading to internal incentive issues within Optimistic Rollup. Although zero-knowledge proofs can effectively address this issue, ZK rollups face challenges such as high computational complexity and the need for transaction-specific proofs. Optimistic rollups have low computational requirements and simple design, making the design of an effective incentive mechanism a good choice. This paper proposes a reward-based incentive model to encourage rational workers to actively verify transactions based on game theory. The rewards in this incentive model are related to a time factor, where earlier verification yields greater rewards, motivating verifiers to verify transactions more quickly. We simulate under various proportions of false declarations and the results show that our method could incentivize the workers effectively to exert their best efforts.
The imbalance between parking availability and demand has led to a rise in traffic challenges in many cities. The adoption of technologies like the Internet of Things and deep learning algorithms has been extensively explored to build automated smart parking systems in urban environments. Non-human-mediated, scalable smart parking systems that are built on decentralized blockchain systems will further enhance transparency and trust in this domain. The presented work, PARKTag, is an integration of a blockchain-based system and computer vision models to detect on-field free parking slots, efficiently navigate vehicles to those slots, and automate the computation of parking fees. This innovative approach aims to enhance the efficiency, scalability, and convenience of parking management by leveraging and integrating advanced technologies for real-time slot detection, navigation, and secure, transparent fee calculation with blockchain smart contracts. PARKTag was evaluated through implementation and emulation in selected areas of the MIT Art Design Technology University campus, with a customized built-in dataset of over 2000 images collected on-field in different conditions. The fine-tuned parking slot detection model leverages pre-trained algorithms and achieves significant performance metrics with a validation accuracy of 92.9% in free slot detection. With the Solidity smart contract deployed on the Ethereum test network, PARKTag achieved a significant throughput of 10 user requests per second in peak traffic hours. PARKTag is implemented as a mobile application and deployed in the mobile application store. Its beta version has undergone user validation for feedback and acceptance, marking a significant step toward the development of the final product.
Christos N Kontos, Theodor Panagiotakopoulos, Achilles Kameas
Transportation plays an important role in urban development. Population growth and environmental burden have turned the efforts of cities globally towards smarter and greener mobility. Cooperative and Connected Automated Mobility (CCAM) serves as a concept with the power and potential to help achieve these goals building upon technological fields like Internet of Things, computer vision and distributed computing. However, its implementation is hindered by various challenges covering technical parameters such as performance and reliability in tandem with other issues, such as safety, accountability and trust. To overcome these issues, new distributed and decentralized approaches like blockchain and smart contracts are needed. This paper aims at identifying a comprehensive inventory of CCAM challenges and use it as a framework to describe methodologies using blockchain and smart contracts to address them. It provides a comparative analysis of the findings to draw useful conclusions and discuss future directions in CCAM and relevant blockchain applications. The paper contributes to intelligent transportation systems’ research by offering an integrated view of the difficulties in substantiating CCAM and providing insights on the most prominent blockchain and smart contract technologies that tackle them.
The increasing number of distinct blockchains has led to a growing need for data exchange and asset transfer across various isolated blockchains. To address this, cross-chain bridges have emerged as a critical mechanism for enabling interoperability and facilitating data and asset exchange across diverse blockchains. Among these bridges, the Layer-0 bridge stands out as a scalability solution that enhances blockchain performance at the foundational layer of data transition, without altering the blockchain's structure. Stargate is a notable Layer-0 Lock-and-Unlock cross-chain bridge that supports transactions across various EVM-based blockchains, with the highest Total Value Locked (TVL) among cross-chain bridges of the same kind. While previous cross-chain research has primarily focused on Layer-2 bridges, this study specifically examines Stargate and analyzes its dynamics as well as potential vulnerabilities. We collect transaction data of Stargate on six blockchains including Ethereum, Polygon, Binance Smart Chain, Avalanche, Arbitrum and Optimism. Our findings reveal the transaction patterns and evidence of exploitations of Stargate by investigating its transaction dynamics over time.
In this paper we present the design for a smart-mask to mitigate the impact of an airborne virus such as COVID-19. The design utilises recent results from feedback control theory over a distributed ledger that have been developed to enforce compliance in a pseudo-anonymous manner. The design is based on the use of the IOTA distributed ledger. A hardware-in-the-loop simulation based on indoor positioning, paired with Monte-Carlo simulations, is developed to demonstrate the efficacy of the designed prototype.
Francisco A. Pujol, Higinio Mora, Tamai Ramírez-Gordillo, Carlos Rocamora · 5 authors
The development of smart vehicles has been a major focus of the automotive industry in recent years. Smart vehicles, equipped with advanced sensors and communication technologies, represent a transformative paradigm in modern transportation systems. Some of the challenges associated with the introduction of smart vehicles include developing reliable sensors, creating robust communication networks, and ensuring the security of vehicle systems. This paper proposes a Blockchain-based framework for accident prevention on the Internet of Vehicles, where vehicles monitor the state of the route and transmit information about hazardous situations to the Blockchain network. Once the event is confirmed, warnings are sent to all vehicles, and their speed is automatically reduced to avoid accidents. The tests in a 3D graphical simulator, combined with Hyperledger Besu technology for the creation of the Blockchain network, demonstrated both horizontal and vertical scalability, validating the potential of this framework for real-world integration. Moreover, it presents a fast reaction to anomalous situations on a route compared to human reactions under similar circumstances.
The collaborative path planning and scheduling can overcome the limitations of single vehicle intelligence to obtain a globally optimal decision strategy in cognitive internet of vehicles (CIoVs). The collaboration of vehicles necessitates the exchange of environmental and decision information, generating massive collaborative computing tasks with strict latency requirements. Leveraging mobile edge computing (MEC) technology, computing tasks can be processed near the vehicles to reduce latency. However, traffic congestion and computational load imbalance seriously affect traffic efficiency and computational latency. In hybrid driving scenarios, it is challenging to fulfill the diverse service requirements of vehicles with different intelligence levels. Moreover, non-collaborative tend to result in traffic congestion due to vehicle aggregation effects, while centralized solutions lack flexibility and have high computational complexity. To address these concerns, a distributed multi-agent reinforcement learning (DMARL) algorithm is proposed for collaborative path planning and scheduling in a blockchain-based collaboration framework. In this framework, we model the communication, traffic situation and task processing of the system and formulate a joint optimization problem to minimize both travel time and computation latency. Last, we convert the scheduling problem for different types of vehicles into Markov decision processes (MDPs) and propose Q-learning-based DMARL algorithm to achieve proactive load balancing of both road infrastructures and MEC nodes (MECNs). Simulation results demonstrate that the proposed approach outperforms the comparison schemes in terms of load balance indexes of roads and MECNs, travel time, and computation latency.
B. Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz · 5 authors
Abstract Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task—filled with conflicts—is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we show stable traffic simulations with up to 5000 agents, and experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a 250 m wide circular area.
Abstract Deploying self‐organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision‐making and decentralized or distributed control structures are combined. Such control structures reduce the complexity of decision‐making, require less computational effort, and are therefore faster, reducing the risk that changes during decision‐making render the solution invalid. These benefits of self‐organizing systems are of interest to many practitioners involved in solving real‐world problems in the logistics sector. This study, therefore, identifies and classifies research related to self‐organizing logistics (SOL) with a focus on transportation. SOL is an interdisciplinary study across many domains and relates to other concepts, such as agent‐based systems, autonomous control, and decentral systems. Yet, few papers directly identify this as self‐organization. Hence, we add to the existing literature by conducting a systematic literature review that provides insight into the field of SOL. The main contribution of this paper is two‐fold: (i) based on the findings from the literature review, we identify and synthesize 15 characteristics of SOL in a typology, and (ii) we present a two‐dimensional SOL framework alongside the axes of autonomy and cooperativity to position and contrast the broad range of literature, thereby creating order in the field of SOL and revealing promising research directions.
Vehicle-to-Everything (V2X) technology relies on wireless communication and coordination, aiming to improve road safety and traffic efficiency by orchestrating the interaction among the vehicles, infrastructures, and various entities. However, existing organizational and operational forms of V2X infrastructures encounter significant challenges, such as the capability to guarantee communication security, privacy, and efficiency especially when a centralized management system is used. The emerging Decentralized Autonomous Organizations and Operations (DAOs) present a solution to these challenges. DAOs embed managerial and operational protocols within a blockchain via smart contracts, facilitating smooth and efficient management of organizations without the necessity for centralized oversight. This letter introduces DAOs into the coordination of V2X infrastructures. Through a tripartite functional architecture including organization, coordination, and execution, we can achieve intelligent collaborative management among road infrastructures and vehicles. The DAO-secured V2X infrastructures are anticipated to augment the safety and efficiency of vehicular management in complex road scenarios, holding applicable potential for developing the intelligent roadways of tomorrow.
Ángel Jaramillo-Alcázar, Jaime Govea, William Villegas-Ch
The growing adoption of Artificial Intelligence of Things technologies in smart cities generates significant transformations to address urban challenges and move towards sustainability. This article analyzes the economic, social, and environmental impacts of Artificial Intelligence of Things in urban environments, focusing on a case study on optimizing vehicular traffic. The research methodology is based on a comprehensive analysis of academic literature and government sources, followed by the creation of a simulated city model. This framework implemented a vehicle-traffic optimization system integrating artificial intelligence algorithms, computer vision, and blockchain technology. The results obtained in this case study are highly encouraging: artificial intelligence algorithms processed real-time data from security cameras and traffic lights, resulting in a notable 20% reduction in traffic congestion during peak hours. Furthermore, implementing blockchain technology guarantees the security and immutability of traffic data, strengthening trust in the system and promoting sustainability in urban environments. These results highlight the importance of combining advanced technologies to effectively address modern cities’ complex challenges and move towards more sustainable and livable cities.
Boqian Ma, Vir Nath Pathak, Lanping Liu, Sushmita Ruj
A sparse Merkle tree is a Merkle tree with fixed height and indexed leaves given by a map from indices to leaf values. It allows for both efficient membership and non-membership proofs. It has been widely used as an authenticated data structure in various applications, such as layer-2 rollups for blockchains. zkSync Lite, a popular Ethereum layer-2 rollup solution, uses a sparse Merkle tree to represent the state of the layer-2 blockchain. The account information is recorded in the leaves of the tree. In this paper, we study the sparse Merkle tree algorithms presented in zkSync Lite, and propose an efficient batch update algorithm to calculate a new root hash given a list of account (leaf) operations. Using the construction in zkSync Lite as a benchmark, our algorithm 1) improves the account update time from $\mathcal{O}(\log n)$ to $\mathcal{O}(1)$ and 2) reduces the batch update cost by half using a one-pass traversal. Empirical analysis of real-world block data shows that our algorithm outperforms the benchmark by at most 14%.
Tariq Jamil Saifullah Khanzada, Muhammad Farrukh Shahid, Ahmad Mutahhar, Muhammad Ahtisham Aslam · 8 authors
The intelligent transport system (ITS) has transformed urban transportation, enhancing daily commutes with services like congestion management, vehicle crash prevention, traffic control, roadside safety, breakdown assistance, ticket booking, vehicle registration, and insurance. However, in urban bus transportation, the ITS faces security threats, such as data forgery and manipulation. To counter these challenges, a blockchain-based framework for bus transportation approval is proposed, ensuring data integrity and security. The framework’s performance is evaluated based on processing time, central processing unit (CPU), graphical processing unit (GPU), cloud usage, and memory consumption, and compared to Ethereum and Aurora testnet, in terms of gas cost, security, and performance. Stochastic algorithms, including the genetic algorithm and Tabu search, are used for time complexity analysis, to obtain an optimized solution. The decision-making trial and evaluation laboratory (DEMATEL) analysis is also performed to assess factors like transaction costs, execution time, memory consumption, and security. The results show that execution time, memory consumption, and processing time are crucial, while transaction cost, reliability, and transparency positively impact the system’s effectiveness. By reducing the risk of false data presentation and ensuring accurate records, the proposed framework contributes to a more efficient and reliable transportation system.
With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS.
Nicolò Vallarano, Tiziano Squartini, Claudio J. Tessone
The open availability of the entire history of the Bitcoin transactions opens up the possibility to study this system at an unprecedented level of detail. This contribution is devoted to the analysis of the mesoscale structural properties of the Bitcoin User Network (BUN), across its entire history (i.e. from 2009 to 2017). What emerges from our analysis is that the BUN is characterized by a core-periphery structure a deeper analysis of which reveals a certain degree of bow-tieness (i.e. the presence of a Strongly-Connected Component, an IN- and an OUT-component together with some tendrils attached to the IN-component). Interestingly, the evolution of the BUN structural organization experiences fluctuations that seem to be correlated with the presence of bubbles, i.e. periods of price surge and decline observed throughout the entire Bitcoin history: our results, thus, further confirm the interplay between structural quantities and price movements observed in previous analyses.
Emanuel Vieira, João Almeida, Joaquim Ferreira, Paulo Bartolomeu
Cooperative, Connected and Automated Mobility (CCAM) applications, enabled by vehicular communications and vehicle automation technologies, are set to increase traffic safety and efficiency. An important feature of CCAM is the potential to decide and coordinate maneuvers among automated vehicles in a more efficient and secure manner, when compared to human drivers. However, maneuver decision and coordination is a rather complex topic due to the multitude of possible maneuvers and the occasional cooperation failures, as caused by issues derived from the use of wireless communications in the vehicular ecosystem, like packet loss or even misbehaving entities. This work focuses on the latter problem by introducing a secure communication design for the decision of general cooperative maneuvers and the distributed storage of related data for accountability purposes using mechanisms based on distributed ledger technologies, while meeting user and data privacy requirements. We present the VERCO (VERifiable COoperation), a scalable geographical-based multi-layered blockchain architecture able to support the high volume of shared vehicular data in order to enhance the security and accountability of cooperative maneuvers, as well as potentially being able to support other vehicular services. To support this architecture, we also provide a performant communication protocol for the decision and negotiation of cooperative maneuvers, based on a new message: the Verifiable Cooperation Message (VCM). The architecture and communication protocol are analyzed and tested employing hardware-in-the-loop (HiL) simulations using two ETSI ITS standard compliant on-board units (OBUs) while deciding maneuvers in a lane-merging scenario. To test the scalability of the architecture a roadside unit (RSU) is put under different stress loads using a variable number of simulated vehicles in the same lane-merging scenario. Experimental results show the feasibility of the communication protocol, with an average delay of 34.68 ms, and the lightweightness of the proposed architecture, with an average overhead of only 2.62 ms for two vehicles and less than 6 ms for dozens of vehicles.