The demand for advanced traffic management systems in smart cities has grown in recent years in response to rising urban populations and the increasing importance of environmental preservation. With the use of the Internet of Things (IoT), it investigates how Distributed Ledger Technology (DLT) may be implemented in decentralized virtual traffic signal systems. To overcome the drawbacks of conventional traffic light regulation, the suggested approach decentralizes the decision-making process over a set of interconnected devices. Integration of DLT, like blockchain, improves the system's openness, security, and robustness. Connected sensors and actuators in the IoT make it possible to gather and share real-time data, and techniques for traffic management that is both responsive and aware of its surroundings. To improve traffic flow, the virtual traffic light system's decentralized design helps save resources and lessens the system's overall carbon footprint. This article describes the system's technological architecture, explores its potential advantages, and explains how it may revolutionize urban traffic management for creating smart cities that are both sustainable and resilient.
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.
In the absence of external interference, independent multi-agent systems evolve over time, leading to a transition from an ordered to a chaotic state within the internal dynamics of the scene. To assist in maintaining the stability of dynamic systems, this paper explores the design and composition of the scenario. A category of altruistic agents with selfless attributes is introduced into the multi-agent environment, and the decentralized algorithm I-DDPG is employed to train these altruistic agents to perform tasks with altruistic tendencies. Weakly connected self-organization is used to enable cooperation among altruistic agents in addressing internal issues. The study demonstrates a positive correlation between the overall system stability and individual information entropy, as well as the method of obtaining individual altruistic rewards. To investigate the connection between overall scene stability and self-organization among internal individuals, a traffic scenario is constructed with both human-driven and autonomous vehicles. Autonomous vehicles (AVs) represent a type of altruistic agent, and experiments are conducted to explore the relationship between scene stability and internal self-organization among individuals. The study concludes that using I-DDPG improves the learning efficiency of agents compared to DDPG and demonstrates a higher safety level in stress tests. Post-training, agents utilizing weakly connected self-organization in scenarios involving merging and exiting obtain higher altruistic rewards. This approach aids human-driven vehicles in achieving their intended goals, contributing to an overall improvement in the stability of dynamic systems.
This letter discusses the security considerations of cross-space collaborative perception based on Decentralized Autonomous Organizations (DAOs). It explores the inter-space collaborative perception in three spaces, namely the vehicles' configuration space, social society space, and cyberspace. A DAO-based cross-space collaborative perception system also enables data generation and interaction among these spaces. The letter addresses the security and privacy protection of the interaction data and the challenges of cross-regional cooperation caused by the privatization and centralization of data interaction protocols in the collaborative perception process. The letter analyzes the causes of these issues and explores a DAO-based cross-space collaborative perception system. By combining DAOs with the collaborative perception system, the safety, efficiency, and sustainability of intelligent vehicles can be improved, which is of significant importance for the autonomous management capability of intelligent transportation systems.
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.
Rapid development of AI technologies has propelled the seamless integration of physical and cyber worlds with various kinds of online/offline information collected from millions of multimodal sensing systems. The complexity, diversity and uncertainty inherited in such systems, such as Intelligent Transportation Systems (ITSs), have gone far beyond human capacity of managing and controlling. Our team is among the first to propose the idea of utilizing the nearly unlimited computational resources in cyberspace to construct a bottom-up and top-down combined artificial ITSs for testing, experimenting, representation, verification, and validation of physical ITSs. Especially, the parallel transportation has been developed for safer, smarter, greener, and more reliable transportation services. After three decades of research and field studies, the DeCAST in Transverse, i.e., Decentralized/Distributed Autonomous Operations/Organizations (DAO) in transportation systems, has been envisioned. In this paper, we introduce its architecture, operational processes, software and hardware platforms, and real world applications. Specifically, a transportation foundation model driven by artificial transportation systems, parallel learning and federated intelligence, named TengYun, is outlined for DeCAST.
The application of blockchain technology in the Internet of vehicles has becomes an effective solution. Blockchain's consensus process is a key component that has a direct impact on the reliability and security of on-chain data. The design of the consensus mechanism is an efficient technique to encourage the integration of blockchain into IoV. Traditional Proof of Work and Proof of Stake consensus relies on the competition while wasting the majority of resources or may lead to unfair circumstances. Practical Byzantine Fault Tolerance consensus method has been proved to adapt to IoV well while rotation of primary node selection seems not suitable for IoV environment. Data in the Internet of Vehicles is highly regional and consensus with IoV is expected to be adapt to those specific data. To address the aforementioned issues to some extent, this paper suggests a consensus mechanism called Proof of Traffic Condition consensus. In order to better reflect the road data distribution, the proposed consensus mechanism suggests a new primary node election procedure based on road and historical on-chain information. Simulation experiments are carried out to prove the proposed consensus mechanism can effectively classify RSUs and appropriately describes roads.
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.
Seyed Mohammad Hashemi, Ruxandra Mihaela Botez, Georges Ghazi
View Video Presentation: https://doi.org/10.2514/6.2023-3757.vid This paper presents a novel consensus algorithm for Unmanned Aircraft System Traffic Management (UTM). Blockchain as a highly secured consensus protocol was used for airspace allocation. A smart contract in charge of airspace allocation was designed based on the Ethereum blockchain. Hence, the action of sharding could be done to reduce the computational complexity in the swarm flight zone. According to the sharded flight zone, a decentralized voting platform was developed using two main approaches, namely Proof of Work (PoW) and Proof of Stake (PoS). A swarm flight zone was arranged using a number of 1000 UAS-S4 at different locations and heading angles. The performance of the designed decentralized consensus system was evaluated in terms of error rate and validation time. Although the PoS approach was faster than the PoW, the comparison study showed that the PoW approach was better than the PoS in terms of error rate.
The aviation sector has been leading the way in implementing new technologies and is currently adopting blockchain technology. Blockchain is a revolutionary technology that enables secure data encryption and storage through a distributed ledger system. This system ensures that multiple nodes can access the data hashes at any time. Any tampering or modifications result in a new hash, making it easy to verify the authenticity of data files and track the node where the changes occurred. The use of blockchain technology holds great promise for digital data security, particularly for digital transactions involving information or money. Like other industries that exchange information between multiple stakeholders, the aviation industry has found many applications for blockchain technology. Although the industry is progressing in adopting this technology, there is still a long way to go. This paper examines the possible application and adaptation the blockchain technology to air traffic management from a safety and efficiency perspective.
With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of nonlinearity, nonstationary, and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the autoregressive integrated moving average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real data sets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-the-art baselines.
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.
Benjamin Kraner, Nicolò Vallarano, Caspar Schwarz-Schilling, Claudio J. Tessone
This paper presents a study of the Poof-of-Stake (PoW) Ethereum consensus protocol, following the recent switch from Proof-of-Work (PoS) to Proof-of-Stake within Merge upgrade. The new protocol has resulted in reduced energy consumption and a shift in economic incentives, but it has also introduced new threat sources such as chain reorganizations and balancing attacks. Using a simple and flexible agent-based model, this study employs a time-continuous simulation algorithm to analyze the evolution of the blocktree and assess the impact of initial conditions on consensus quality. The model simulates validator node behavior and the information propagation throughout the peer-to-peer network of validators to analyze the resulting blockchain structure. Key variables in the model include the topology of the peer-to-peer network and average block and attestation latencies. Metrics to evaluate consensus quality are established, and means to observe the model's responsiveness to changes in parameters are provided. The simulations reveal a phase transition in which the system switches from a consensus state to a non-consensus state, with a theoretical justification presented for this observation.
Ahmad J. Alkhodair, Saraju P. Mohanty, Elias Kougianos
Due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. Internet of Vehicles (IoV) is the term that describes the interconnection for every single node, device, sensor, and actuator that are Internet enabled, whether attached or unattached to vehicles. A single smart vehicle will generate a huge amount of data. Concurrently, it needs an instant response to avoid accidents since vehicles are fast-moving objects. In this work, we explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS. Multiple distributed ledger networks are currently in operation. Some are used in finance or supply chains, and others are used for general decentralized applications. Despite the secure and decentralized nature of the blockchain, each of these networks has trade-offs and compromises. Based on the analysis of consensus algorithms, a conclusion has been made to design one that fits the requirements of ITS-IOV. FlexiChain 3.0 is proposed in this work to serve as a Layer0 network for different stakeholders in the IoV. A time analysis has been conducted and shows a capacity of 2.3 transactions per second, which is an acceptable speed to be used in IoV. Moreover, a security analysis was conducted as well and shows high security and high independence of the node number in terms of security level per the number of participants.
Seyed Mohammad Hashemi, Seyed Mohammad Hashemi, Seyed Ali Hashemi, Seyed Ali Hashemi · 6 authors
This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm.
Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi
View Video Presentation: https://doi.org/10.2514/6.2023-2190.vid This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm is designed for Aircraft Trajectory Prediction (ATP) based on Autoencoder architecture. The Autoencoder is used due to its excellent fault-tolerant ability when input data provided by the GPS is deficient. After conflict detection, P2P Blockchain is utilized for securely decentralized decision-making. The meta-controller composed of the Autoencoder and P2P blockchain performed the ATMC task very well. The validation studies were done relying on a comprehensive database of trajectories constructed using our UAS-S4 Ehécatl. The ATP accuracy was evaluated for a variety of data failures, and the performance index confirmed the Autoencoder’s excellent efficiency. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our designed Smart Contract developed on the Ethereum platform. Toward decentralized processing, the edge computing approach improved the P2P Blockchain performance in terms of computational complexity and processing time in real-time operations.