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 today’s 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 people’s 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.
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.
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.
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 model’s 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.
Purpose:The objectives of this paper are, first, to synthesize the main events that have influenced the development and volatility of bitcoin, and second to present and apply the Rogers and Satchell range model (1993) for the measurement of bitcoin volatility.Desigh/methodology/Approach: Findings: The results are useful for risk management, searching profitable investments, diversification of portfolios, and the application of a reliable risk parameter for the valuation of bitcoin financial and real options.The evidence suggests further studies extending it, considering leptokurtosis and other moments of the distribution of the series. Practical implications: Finally, the study highlights the need for clear regulations on bitcoin and other cryptocurrencies to ensure a fruitful future co-existence with digital currencies created by the central banks. The work suggests the creation of additional IMF's SpecialDrawing Rights in lieu of a global cryptocurrency.That would help to overcome the problems created by the Covid-19 pandemic and member countries would retain their monetary sovereignty.Originality value: These objectives underline the originality and contribution of the work.This is the first time this model is fully recognized and used, highlighting its advantages.
Krishan Kumar, Jenifer Mahilraj, D. Swathi, R. Rajavarman · 8 authors
Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The proposed PPSF-BODL model includes the collection of primary data using sensing tools. Besides, z-score normalization is also utilized to transform the actual data into useful format. Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions. CSO is employed for optimal hyperparameter tuning of ABiLSTM model. At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server. This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods. A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.
Information sharing is one of the huge topics in social media platform regarding the daily news related to events or disasters happens in nature or its human-made. The automatic urgent need identification and sharing posts and information delivery with a short response are essential tasks in this area. The key goal of this research is developing a solution for management of disasters and emergency response using social media platforms as a core component. This process focuses on text analysis techniques to improve the process of authorities in terms of emergency response and filter the information using the automatically gathered information to support the relief efforts. Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to extract real-time content related to the emergency events to comfort the fast response in a critical situation. Similarly, the blockchain framework used in this process for trust verification of the detected events and eliminating the single authority on the system. The main reason of using the integrated system is to improve the system security and transparency to avoid sharing the wrong information related to an event in social media.
E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.
In this paper, I present B-DRIVE—a blockchain-based distributed IoT (Internet of Things) network for smart urban transportation. The network is designed to connect a large fleet of IoT devices, installed on various vehicles and roadside infrastructures, to distributed data storage centers, called as Full-Nodes, to log and disseminate sensor generated data. It connects devices from around the city to multiple Full-Nodes to log timestamped data into the blockchain. These sensors vary from GPS (Global Positioning System), air quality meter, gyrometer to speed cameras in order to facilitate efficient urban mobility. The three identified hardware layers that comprise the network are the IoT layer, Storage layer, and User layer. They consist of Moving/Static-Nodes, Full-Nodes, and Smart devices, respectively. The Moving/Static-Nodes are primarily made up of moving vehicles and road-side infrastructures, respectively, thus acting as various data sources. Whereas, Full-Nodes and Smart devices are institutions and mobile phones, acting as data handler/disseminator and navigator/data visualizer, respectively. The data, or data blocks, received by Full-Nodes get appended into Full and Running-Blockchain, meant for specific purposes. The network is designed to be free from any block mining activity. It provides open access to anonymous sensor data to end-users, especially scientists, policy-makers and entrepreneurs, to develop innovative urban transportation solutions. It is believed that a system like B-DRIVE, along with existing VANETs (Vehicular Ad-hoc NETworks), is capable of answering some of the current urban transportation issues around traffic congestion, navigation, and vehicle parking. Other applications of blockchain data could vary from user activity mapping to VGI (volunteered geographic information) data quality assessment. Two identified limitations of the presented architecture are the low processing power of current IoT devices and the lack of urban IoT infrastructure.
Noura Metawa, Mohamemd I. Alghamdi, Ibrahim M. El‐Hasnony, Mohamed Elhoseny
Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.
Muhammad Waseem, Khawaja Arslan Ahmed, Muhammad Talha Azeem
Blockchain technology is widely studied in these days and has vital role in the ITS and Vehicular network. Intelligent Transport System (ITS) have resolved several issues of transportation like congestion, electronic toll collection, traffic light cameras, traffic updates, and environment forecasting. The vehicular network is the ever-increasing network it is not only facilitates us but also brings new challenges with it. The mobile nature of vehicular networks it is very important to collect and broadcast information of traffic events in real-time. A little delay to broadcast important information or deciding on this information can cause a serious situation in the mobile vehicular network. Moreover, malicious vehicles in the network broadcasting false information about these traffic events cause a disturbance in the network. In large-scale scenarios, the transmission of malicious messages offers a lot of danger to the system. They can wrongly claim the roads and provide false information about the incident. These traffic events can be life-threatening and cause unwanted situations like accidents, wastage of time and other resources. Therefore, it is very much important to provide real-time information on recent traffic events and real-time authentication of vehicles that broadcast information in the network. Traditional studies are unable to solve these security issues and contain a single point of failure issue. These studies are centralized and dependent on a single higher authority. Moreover, they have serious security concerns that are harmful for vehicular network. Moreover, any vehicles are unwilling to share their private information while broadcasting information about traffic events because they are strangers to each other. And if a vehicle does not want to share its private information like name, id, etc. It is not possible to authenticate this vehicle and manage trust in the network. It means that it is very crucial to prevent vehicles to broadcast wrong information in the network while preserving their privacy at the same time. Therefore, there is a need to authenticate vehicles and manage trust in the network while preserving their privacy simultaneously. Blockchain can offer better solution to solve these issues due to its secure distributed environment and features that ensure immutability about actions. The purpose of this report is to provide real-time security and privacy in the network. It is also ensured that vehicles get real-time authenticated information about traffic incidents from legitimate vehicles while simultaneously preserving their privacy. It means that only authenticated and legitimate entities (vehicles) can participate in vehicular network and privacy of both sender and receiver is secured in the network. Details of conducted experiments are given, and shreds of evidence are provided to evaluate the performance of architectures for authentication and trust management. The shreds of evidence show that these blockchain-based systems can solve security and trust issues more effectively.
The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system's efficacy and performance compared with other state-of-art models.
Since its founding in 2008, Bitcoin (financial code: BTC) has emerged as a digital currency in market cap and continues to attract investors and policymakers' attention. In recent years, BTC has high price volatility, a substantial increase in 2016, followed by a significant decline in 2018. Unlike stock markets, BTC is open for 24x7 dan has no closing period. It means everyone can trade it for any time. However, this flexibility carries investment risk. This research attempts to forecast BTC's price by considering the blockchain's information to minimize the risk. We employ Long-Short Term Memory (LSTM), the artificial Recurrent Neural Network (RNN) architecture. Its model can avoid long-term problems. The data used is BTC's price and blockchain information data from August 4, 2018, to January 21, 2020. The model with 20 neurons and 500 epochs has the smallest MSE value. Then a prediction has an accuracy rate of 91.07%.
Reem K. Alkhodhairi, Shahad R. Aljalhami, Norah K. Rusayni, Jowharah F. Alshobaili · 6 authors
Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study proposes time series-based deep learning models. In particular, two deep learning algorithms were applied, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). Using real-time data, the Bitcoin candlesticks were predicted for three intervals: the next 4 h, the next 12 h, and the next 24 h. The results showed that the best-performing model was the LSTM-based model with the 4-h interval. In particular, this model achieved a stellar performance with a mean absolute percentage error (MAPE) of 0.63, a root mean square error (RMSE) of 0.0009, a mean square error (MSE) of 9e-07, a mean absolute error (MAE) of 0.0005, and an R-squared coefficient (R2) of 0.994. With these results, the proposed prediction model has demonstrated its efficiency over the models proposed in previous studies. The findings of this study have considerable implications in the business field, as the proposed model can assist investors and traders in precisely identifying Bitcoin sales and buying opportunities.
Dinh Dung Nguyen, József Rohács, Dániel Rohács, Anita Boros
Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.
Data privacy and sharing has always been a critical issue when trying to\nbuild complex deep learning-based systems to model data. Facilitation of a\ndecentralized approach that could take benefit from data across multiple nodes\nwhile not needing to merge their data contents physically has been an area of\nactive research. In this paper, we present a solution to benefit from a\ndistributed data setup in the case of training deep learning architectures by\nmaking use of a smart contract system. Specifically, we propose a mechanism\nthat aggregates together the intermediate representations obtained from local\nANN models over a blockchain. Training of local models takes place on their\nrespective data. The intermediate representations derived from them, when\ncombined and trained together on the host node, helps to get a more accurate\nsystem. While federated learning primarily deals with the same features of data\nwhere the number of samples being distributed on multiple nodes, here we are\ndealing with the same number of samples but with their features being\ndistributed on multiple nodes. We consider the task of bank loan prediction\nwherein the personal details of an individual and their bank-specific details\nmay not be available at the same place. Our aggregation mechanism helps to\ntrain a model on such existing distributed data without having to share and\nconcatenate together the actual data values. The obtained performance, which is\nbetter than that of individual nodes, and is at par with that of a centralized\ndata setup makes a strong case for extending our technique across other\narchitectures and tasks. The solution finds its application in organizations\nthat want to train deep learning models on vertically partitioned data.\n
Abstract Even though there is continuous improvement in road and vehicle safety, road traffic incidents have been increasing over last few decades. There is a need to reduce traffic incidents like accidents through predictive analysis and timely warnings while at the same time data related to accidents and traffic violations need to be maintained in a tamper proof storage system that can be retrieved for forensic analysis and law enforcement at a later stage. The Secure Incident and Evidence Management Framework (SIEMF) proposed in this work address these two challenges of predictive modeling for timely warning and secure evidence management for forensics analysis in case of accidents and traffic violations. The system proposes a deep learning based predictive incident modeling with blockchain and CP-ABE based access control for the incident data stored in blockchain.
Santiago Figueroa-Lorenzo, Jon Goya, Javier Añorga, Iñigo Adín · 6 authors
The European Union is moving toward the “smart” era having as one of the key topics the smart mobility. What is more, the European union (EU) is moving toward Mobility as a Service (MaaS). The key concept behind MaaS is the capability to offer both the traveler's mobility and goods' transport solutions based on travel needs. For example, unique payment methods, intermodal tickets, passenger services, freight transport services, etc. The introduction of new services implies the integration of many Internet-of-Things (IoT) sensors. At this point, security gains a key role in the railway sector. Considering an environment where sensor data are monitored from sensor events, and alarms are detected and emitted when events contain an anomaly, this document proposes the development of an alarms collection system, which ensures both traceability and privacy of these alarms. This system is based on Ethereum blockchain events-log, as an efficient storage mechanism, which guarantees that any railway entity can participate in the network, ensuring both entity security and information privacy.
European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.