In the era of smart cities, Intelligent Transportation System (ITS) are necessary towards the success of smart cars in the modern societies. ITS rely on Vehicular Ad-hoc Networks (VANETs), which enable communication between cars to relay safety messages exchange. However, VANETs can be exposed to the issue of decentralization and high mobility of cars. Therefore, VANETs are vulnerable to a variety of attacks such as black-hole and grey-hole attacks. These attacks have a significantly dangerous influence on the availability of ITS, causing traffic disruption. In this paper, a blockchain-based model is proposed to provide a convenient and secure solution for ITS. Furthermore, it enables decentralized cooperation between cars and mutual trust is created using smart contracts. The experimental evaluation shows that the PDR rates of the proposed protocols achieve good results compared to previous routing protocols. However, the VCBC method gives a high rate of PDR than SCBC reaching 70% because of the high awareness of the sender car.
As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.
Seyed Mohammad Hashemi, Seyed Mohammad Hashemi, Seyed Ali Hashemi, Seyed Ali Hashemi ยท 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 Ehรฉcatl 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.
Piyush Kumar Yadav, Rajnish Bhasker, Albert Alexander Stonier, Geno Peter ยท 6 authors
Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, metaโlearning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.
In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combina-tion of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The sec-ond key discovery was that a strong combination forecasting model that responds to the multiple fluctua-tions that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction accuracy criterion and matching curve-fitting in this work demonstrated that if the residuals of the revised model are white noise, the forecasts are unbiased. Future work investigating robust hybrid model forecasting using fuzzy neural networks would be very interesting.
Blockchain technology has been widely used in finance, transportation, education, medical treatment, network security, management science, and other industries due to its characteristics of decentralization, high reliability, and traceability. Unlike other studies on Urban Intelligent Transportation Systems (UITS) in the past, we present a model framework of the Urban Intelligent Transportation Systems (UITS) applied by blockchain in developing countries. After a detailed elaboration of the situation of three representative Urban Intelligent Transportation Systems (UITS) in China, blockchain technology has been applied to build a new architecture model of the big data platform for urban intelligent transportation, as well as the design concept and conceptual model for the new generation of the Urban Intelligent Transportation Systems (UITS) in developing countries. Finally, this paper elaborates on the important direction of future development, areas, of concern, and open research challenges, which could be explored by researchers and urban intelligent transportation designers to make further advances in this field.
As the adoption of cryptocurrencies, especially Bitcoin (BTC) continues to rise in todayโs digital economy, understanding their unpredictable nature becomes increasingly critical. This research paper addresses this need by investigating the volatile nature of the cryptocurrency market, mainly focusing on Bitcoin trend prediction utilizing on-chain data and whale-alert tweets. By employing a Q-learning algorithm, a type of reinforcement learning, we analyze variables such as transaction volume, network activity, and significant Bitcoin transactions highlighted in whale-alert tweets. Our findings indicate that the algorithm effectively predicts Bitcoin trends when integrating on-chain and Twitter data. Consequently, this study offers valuable insights that could potentially guide investors in informed Bitcoin investment decisions, thereby playing a pivotal role in the realm of cryptocurrency risk management.
Human and social factors are essential to transportation systems, yet top-down management fails to consider them sufficiently. Consequently, management strategies are not tailored to human needs and are inadequate in providing transportation intelligence. This article investigates a management architecture based on decentralized/distributed autonomous operations/organizations (DAOs) that considers both the technical and societal aspects in our transportation metaverse, TransVerse. This design maps peopleโs transportation needs in physical space to their digital counterparts in cyberspace, utilizing blockchain technology to guarantee the secure exchange of information and ultimately bring about the Internet of Minds (IoM). With the federated intelligence that emerged in IoM, we can devise reliable and prompt traffic decisions by incorporating consensus, community voting, and smart contracts into the organizational, coordination, and execution structure. Details on operational procedures and key technologies are also covered. To demonstrate the efficacy of DAOs-based management, a case study of world model-driven cooperative signal control is provided, indicating its promising application in future transportation management.
In recent years, the application and development of the Internet of Things (IoT) has increased significantly. In general, the IoT requires many technologies. Usually, a large amount of IoT sensor information is collected through IoT devices, and devices are connected to each other through IoT communication technology. The Internet exchanges and transmits information, issues instructions and commands to the device, and then conducts prediction and decision making by artificial intelligence (AI) after big data analysis, which is called theAI of Things(AIoT) system.
Smart cities are our aspiration for a better life where transportation intelligence is indispensable. Recent technological advances in intelligent transportation systems have opened up new possibilities for smart mobility in smart cities. Here we present TengYun, a transportation foundation model designed and developed with parallel learning and federated intelligence for our transportation metaverse called TransVerse. TengYun enables decentralized/distributed autonomous organizations with decentralized/ distributed operations, as well as various federated technologies, from federated security, federated control, federated management, federated services, to federated ecology for transportation intelligence in smart cities. An example for a federation of transportation transformers is discussed for illustrating the operating procedure of TengYun.
Ride-sharing services (RSSs) using centralization methods experience various challenges like single point-of-failure, privacy violation, lack of security, and distributed denial of services (DDoS) attack, etc. So, blockchain-based RSSs mitigate such problems through decentralization. Relying on the blockchain only leads to problems such as increase in application response time, chain size, and a high computational cost due to the increase in data storage in blockchain and thus increase the service costs to end users. Additionally, the blockchain lacks to scalability of data because of the inability to store large-sized data and accommodate the grows of ride-sharing data. To overcome these problems, a novel decentralized ride-sharing system that exploits blockchain and Interplanetary File System (IPFS) is proposed. The goal of the proposed system is to move all ride-sharing data outside the blockchain and replacing it with a small hash. The blockchain manages the application state and users. In addition, it automates processes through smart contracts. While the IPFS stores data for blockchain in immutable and integral way. Wherefore, the proposed ride-sharing system integrates IPFS with blockchain for RSSs to retain the provided assurance by the blockchain and provide efficient service to end users. Experimental results proved the applicability and efficiency of RSS based on blockchain and IPFS which provides efficient storage of ride-sharing data, immutable history, and generally better efficiency in a decentralized manner.
Parallel transportation management and control was proposed three decades ago as a new paradigm for conducting complex transportation operations and has led to todayโs DeCAST in TransVerse platform designed and constructed according to the principle of decentralized/distributed autonomous operations and organizations. This article presents an overview of its architectures, processes, operating procedures, and major applications. The developments and applications have demonstrated clearly that parallel transportation systems are effective for networked traffic control and distributed logistical operations. The existing challenges and emerging opportunities are also addressed. A transportation foundation model based on parallel learning and federated intelligence is proposed as a potential path to the next-generation parallel intelligent transportation systems.
The real-time intelligent perception and prediction of traffic situation can assist connected automated vehicles (CAVs) in path planning and reduce traffic congestion in Cognitive Internet of Vehicles (CIoVs). The centralized traffic congestion prediction solutions generally fail to adapt to the dynamic traffic environment and lead to significant communication overheads. Blockchain technology has attracted great attention in the information sharing of vehicular networks for its advantages in decentralization, transparency, traceability, and tamper-proof capability. However, due to the bottlenecks, such as high computational cost, current blockchains are incapable actuate on efficient online traffic situational cognition and prediction for CIoVs. Motivated by this, we propose a blockchain-enabled cognitive segments sharing framework for online multistep congestion duration prediction. We design a cognitive model of traffic situation based on anomaly detection and filtering mechanism to guarantee the accuracy of the cognitive segments before being packaged into the block. Furthermore, to improve the consensus efficiency, we design a credit evaluation mechanism and propose a credit-based delegated Byzantine fault tolerance (CDBFT) algorithm. Finally, we propose an online multistep prediction algorithm based on long short-term memory (LSTM) to predict future traffic congestion duration. Experimental results demonstrate that the proposed algorithms achieve shorter consensus latency and higher predictive accuracy than the existing algorithms.
Jun Liu, Lei Zhang, Chunlin Li, Jingpan Bai ยท 6 authors
The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access controlโs accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.
In recent years, Bitcoin cryptocurrency has become a growing trend in the world. For this reason, researchers from many fields are examining various artificial intelligence models to predict Bitcoin rates. In particular, Deep Learning algorithms have been shown to outperform traditional models in predicting cryptocurrency rates. However, very few studies have examined the effect of parameters used in deep learning algorithms on the algorithm. Optimization and loss functions are very important, which affect the algorithm's ability to make a successful prediction. In this study, Long-Short Term Memory, a deep learning algorithm, is used to predict daily Bitcoin prices and the effect of optimization/loss functions on the accuracy rate is evaluated. Experimental results showed that the Long-Short Term Memory model made the best predictions as a result of working with the Adam optimization function and the Mean Square Error loss function.
T. Shanthi, M. Ramprasath, A. Kavitha, T. Muruganantham
The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS). Despite the IATS's benefits, security remains a significant challenge. Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies. Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development. In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details. Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation. Additionally, the chaotic krill herd technique is used to tune the DBN modelโs hyperparameters. The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions. The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.
Intelligent Transportation Systems (ITS) have gained popularity due to smart services and applications to facilitate the users on the roads. The increasing growth of users in these networks created new and complex data processing, storage, security, and privacy concerns. These networks are using centralized edge, fog, or cloud architecture for data management. User privacy is compromised in these networks due to the increasing demands and service providerโs services. To ensure the data privacy, the centralized architectures are used without privacy regulations. In this paper, we present a Blockchain-based Privacy-Preserving Authentication (BPPAU) model for ITS networks to ensures users privacy and security. The proposed model provides data storage, data accessing, and processing management by using a blockchain smartcontract system, access control policy and on demand based functions. The proposed model is tested in a simulation environment to check its performance in terms of transaction cost with data size, transaction per second analysis with block time, and computational time analysis with several transactions.
Blockchain is one of the leading 4.0 technologies because of its potential applications in many fields, such as real estate. Real estate is an old market and little changes. Therefore, buying and selling real estate brings many risks because it is difficult to find and verify information. Real estate transactions are complex, time-consuming and have high intermediary fees. However, Blockchain technology has opened up many ways to change this. Therefore, we study Blockchain technology and propose Real Estate Transaction Trace (RETT) system model to change and improve the way real estate transactions work in Vietnam. The RETT system model is capable of managing and tracking the entire process of real estate transactions of all participants (owners, buyers, sellers, notaries). The government or real estate investor consortium can easily manage and trace all the purchase or lease transaction history of a property (land, town-house, apartment). We designed Blockchain network architecture, distributed ledger and smart contract. As a result, our method can digitize assets on the Blockchain, store decentralized transaction history, enable encryption, and direct transactions between sellers and buyers. In addition, our system model can reduce the problem of data explosion, execute multiple transactions at the same time, prevent data tampering and sensitive information disclosure. We have built a prototype of this system model based on the Ethereum Blockchain platform. Thereby, we have proven the effectiveness of RETT system model and practical applicability through experimental transactions. Our method improves transparency, eliminates intermediaries, saves costs and increases mutual trust in real estate transactions. Our system model can manage real estate transactions comprehensively and can be able to overcome the weaknesses of the current real estate transaction management system according to the Client-Server model. Therefore, our system model can be deployed in the smart city to manage and trace the entire real estate transaction.