Introduction/Background This study explores the limitations of conventional encryption in real-world communications due to resource constraints. Additionally, it delves into the integration of Deep Reinforcement Learning (DRL) in autonomous cars for trajectory management within Connected And Autonomous Vehicles (CAVs). This study unveils the resource-constrained real-world communications, conventional encryption faces challenges that hinder its feasibility. This introduction sets the stage for exploring the integration of DRL in autonomous cars and the transformative potential of Blockchain technology in ensuring secure data transfer, especially within the dynamic landscape of the transportation industry. Materials and Methods The research methodology involves implementing DRL techniques for autonomous car trajectory management within the context of connected and autonomous CAVs. Additionally, a detailed exploration of Blockchain technology deployment, consensus procedures, and decentralized data storage mechanisms. Results Results showcase the impracticality of conventional encryption in resource-constrained real-world communications. Moreover, the implementation of DRL and Blockchain technology proves effective in optimizing autonomous car subsystems, reducing training costs, and establishing secure, globally accessible government-managed transportation for enhanced data integrity and accessibility. Discussion The discussion delves into the implications of the study's findings, emphasizing the transformative potential of DRL in optimizing autonomous car subsystems. Furthermore, it explores the broader implications of Blockchain technology in revolutionizing secure, decentralized data transfer within the transportation industry. Conclusion In conclusion, the study highlights the impracticality of conventional encryption in real-world communications and underscores the significant advancements facilitated by DRL in autonomous vehicle trajectory management. The integration of Blockchain technology not only ensures secure data transfer but also paves the way for a globally accessible transportation blockchain, reshaping the future landscape of the industry.
Muhammad Saleem, Muhammad Sajid Farooq, Tariq Shahzad, Arfa Hassan · 8 authors
In the recent era, the practical implementation of Autonomous Vehicular Networks (AVNs) with the vulnerable Vehicle-to-Vehicle (V2V) communication of autonomous vehicles and inadequate intelligent decision-making systems has become a primary concern in smart city mobility. This has led to the traffic congestion concerns such as time wastage, compromised safety, decreased durability and reliability of transportation infrastructure and V2V communication short delay and Roadside Units (RSUs), and reduced traffic flow. To address these issues, secure AVN communication and smart decision-making for autonomous vehicles in smart cities are of utmost importance. It ensures safety on roads, durability of the infrastructure, transparency, reliability, traffic congestion reduction and transportation efficiency. MapReduce is a reliable distributed computing paradigm which is able to analyze and process enormous AVN data in parallel. It contributes to smoother traffic flow by identifying the patterns and providing actionable insights for real-time decision making to decrease congestion. A private blockchain AVN can efficiently solve the problems of data security and reliability by providing tamper-proof record of all the transactions, hence enhancing reliability, and also offering a trusted solution of unauthorized access in real-time V2V communication. Explainable Artificial Intelligence (XAI) which is an efficient way to analyze fairness in traffic data over time providing transparency and availability of intricate traffic patterns, improving real-time traffic management with V2V communication and RSUs and reducing short delays that may occur as well as enabling traffic flow and the development of predictive traffic models that assist in decision making. This research proposed an XAI-based transparent model integrating MapReduce for processing large amounts of data and private blockchain technology for secured and tamper-proof vehicular communication. This proposed model is a promising solution for addressing the AVN data security issues and reliability of the system, mitigating negative effects of traffic congestion, and improving the transparency of decision making on the transport efficiency in smart cities. The proposed model provides a better performance than the previous approaches and gets 96% of the accuracy and 4% of miss rate.
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.
Mohamed Hasan Omar, Islam Taj-Eddin, Nagwa M. Omar, Hosny Ibrahim
Traffic road violations are increasing continuously in crowded and big cities, which requires an automatic system for monitoring and detecting. This system should be accurate and secure against poisoning attacks that intend to delete some or all traffic violations. Accordingly, this study proposes a secure road traffic management system using the Internet of Things (IoT) and blockchain technology. The system uses a network of sensors, traffic signals, and cameras to track violating vehicles and record their violations in the blockchain. In the proposed system, we used deep learning models to recognize the vehicle identification number and type of traffic violations, and we stored the traffic violation data on the Ethereum test network. The proposed system consists of three steps: (1) vehicle information detection. (2) Type of violation detection. (3) Violation storage in the blockchain. The proposed secure road traffic management system uses blockchain, IoT, encryption, and authentication to increase violation detection and recording, processing speed, and communication delay, ultimately improving service quality and customer experience. The experimental results show that the proposed system is more accurate and secure than the other systems that have been proposed in recent research.
Federated Learning (FL) is a machine-learning approach enabling collaborative model training across multiple decentralized edge devices that hold local data samples, all without exchanging these samples. This collaborative process occurs under the supervision of a central server orchestrating the training or via a peer-to-peer network. The significance of FL is particularly pronounced in industries such as healthcare and finance, where data privacy holds paramount importance. However, training a model under the Federated learning setting brings forth several challenges, with one of the most prominent being the heterogeneity of data distribution among the edge devices. The data is typically non-independently and non-identically distributed (non-IID), thereby presenting challenges to model convergence. This report delves into the issues arising from non-IID and heterogeneous data and explores current algorithms designed to address these challenges.
The risk prediction of financial markets is of paramount importance, with investor sentiment playing a critical role. However, current research appears to be lacking in-depth exploration of this particular aspect within the Bitcoin market. This study aims to explore the impact of market participants’ sentiment on risk prediction in the bitcoin market. We first applied ChatGPT to analyze the sentiment of crawled Bitcoin-related news headlines. Meanwhile, Monte Carlo simulation was employed to calculate value at risk (VaR). And we selected five conventional factors, including Bitcoin price, transaction volume, market share, hash rate, and average difficulty of mining. Finally, K-Nearest Neighbors (KNN) regression model was used to construct the model for predicting the risk of bitcoin market. We made a comparison between the accuracy outcomes when considering and not considering sentiment as factors. The results show that market participant’s sentiment is significantly associated with market risk, and the inclusion of sentiment can significantly improve the accuracy of the risk prediction model.
Forecasting the value of Ethereum (ETH) or any other cryptocurrency is a formidable undertaking owing to the inherent volatility and speculative characteristics shown by these digital assets. Nevertheless, it is possible to create price forecasts by using machine learning techniques, namely Recurrent Neural Networks (RNNs), which are capable of capturing temporal relationships within the data. The challenge of forecasting the price of Ethereum (ETH) or any cryptocurrency is a multifaceted endeavour that encompasses aspects of finance, economics, and data science. The practise of technical analysis is the examination of past price charts, patterns, and technical indicators in order to make forecasts about future price fluctuations. The underlying assumption is that previous pricing patterns had the capacity to provide valuable insights into future developments. Nevertheless, it is essential to acknowledge that the effectiveness of technical analysis within the realm of cryptocurrency trading is a subject that engenders much scholarly discourse. The primary objective of this research is to examine the utilisation of Ethereum cryptocurrency and forecast its behaviour via the use of machine learning methodologies, namely Recurrent Neural Networks. The suggested approach demonstrates a high level of accuracy, reaching 95 percent. This significant level of precision will be beneficial for future academics working on this technology.
Jutur Manogna, Gogineni Sravan Chowdary, Gogineni Meghana, Priyanka C. Nair
Bitcoin is a decentralized digital currency that has received a lot of interest in recent years because of its unique qualities and possibilities as an alternative investment. Trading in Bitcoin might be difficult owing to the extreme volatility of its price, which is impacted by a variety of variables such as market sentiment and regulatory changes. To solve this issue, researchers and traders have been investigating the use of social media data, namely Twitter data, to forecast Bitcoin price changes using sentiment analysis. The work focuses on leveraging real-time Twitter data and bitcoin prices from the Twitter and Coin Market API respectively to predict short-term Bitcoin price movements. Real-time tweets are collected, sentiment is extracted using sentiment analysis methods, and then combined with relevant pricing data. A predictive framework is developed to forecast the next hour's Bitcoin price by employing univariate and multivariate time series forecasting and generating deep learning models such as LSTM, BIGRU, RNN and LSTM+GRU. Multivariate time series forecasting model based on BIGRU has performed well among the deep learning models used, attaining a 130.529 RMSE score. The primary aim of this study is to provide valuable insights to traders, as short-term market sentiment plays a crucial role in their trading strategies.
Blockchain technology provides efficient, transparent and secure street vendor management. The paper aims to investigate the role of blockchain in street vendor management in the city. The study proposes to solve the problem at both ends for municipal corporations to manage street vendors and have a transparent system to address their issues. We suggest a model to improve the functioning of the municipal corporation and help the local administration. The paper focuses on a Blockchain-based Street Vendor Management (SVM) that automates the registration process and allot a street ID to the street vendor. SVM utilised the power of blockchain technology to offer a secure, efficient, immutable and transparent system. Ethereum-based smart contracts are used to build a standard nationwide registration process. The main players of SVM are street vendors, local administrators, and global administrators. Further, the proposed model can be integrated to perform transactions among customers, street vendors, local and global administrators.
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.
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.
Temporal Heterogeneous Networks (THNs) are evolving networks that characterize many real-world applications such as citation and events networks, recommender systems, and knowledge graphs. Forecasting THNs involves predicting future connections within a network that evolves over time and comprises diverse types of nodes and interactions with varying temporal dynamics. Although some Graph Neural Networks (GNNs) models have been successfully applied to forecast THNs, there is a lack of a general overview of how the message-passing computation could be extended to treat THNs. Moreover, most of the current solutions exhibit pitfalls in their training and evaluation strategies. Hence, in this work, we propose a graph deep learning framework for THN forecasting. Our framework decomposes the computation of a GNN layer into multiple components and introduces two different schemes to update embedding representations for THNs. This design allows the classification of existing solutions into special instances of our framework and highlights their potential limitations. We also extend the set of benchmarks for THNs by introducing two novel high-resolution temporal heterogeneous graph datasets derived from an emerging Web3 platform and a well-established e-commerce website. Overall, we conducted the first massive evaluation of THNs solutions over four temporal heterogeneous network datasets on two different future link prediction tasks using a fair newly introduced evaluation setting that considers the evolving nature of the data. Based on the limitations of existing solutions, we develop a new model that combines working techniques from previous models and leverages a new embedding update scheme. Experiments show the prediction power of our model compared to current solutions for link prediction in temporal graphs. Moreover, the experimental evaluation highlights the strengths and weaknesses of the different solutions and shows the effectiveness of our framework design.
With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hot spot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting.
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.
With deepening urbanization and Internet of Vehicles (IoV) applications, the number of private cars has been increasing in recent years. However, because the surging number of private cars is not compatible with limited road resources, private car users have had unsatisfactory commute experiences during their daily travel. In this work, we focus on improving private car users’ commute experience based on an analysis of IoV trajectory data in a privacy-preserving way. Our idea is based on the following observations: 1) the commute experience of private car users is closely related to the departure time and the travel cost and 2) most travel costs are spent on urban hot zones. Motivated by these findings, we propose a novel blockchain-enabled model named Deep Improving Commute Experience (DeepICE) to improve private car users’ commute experience by predicting when to depart and when to arrive. In this model, a blockchain with a consensus mechanism is developed to address private car user privacy concerns. In addition, we propose a multitask learning-enabled graph convolution network (GCN) method to capture the highly complex features and relations between two tasks, i.e., the departure time and travel cost, and then develop the model to predict these two tasks. The experimental results demonstrate the superior performance of our proposed model compared to existing approaches. Our model can be applied to efficiently enhance private car users’ commute experience.
Revolutionizing data processing, security and real-time decision making, the move to IoT edge intelligence is advancing the state of the art in how we approach these and all challenges of modern business. Latency, bandwidth constraints, security vulnerability are the traditional pain points of traditional cloud-based service models, edge computing is a critical solution. The IoT systems can be made more responsive, better able to utilize resources more effectively, and more secure by way of integrating ML driven visualization and edge AI strategies. Nevertheless, there are still some challenges about this such as scaling, data privacy, and computational efficiency. These risks can be mitigated with the solutions like federated learning, blockchain integration and then the anomaly detection, and all that data can actually flow seamlessly and securely. Edge AI takes the best of centralized cloud along with cost efficiency of distributed systems and results in reducing dependence on centralized cloud infrastructure, and optimizing data processing by doing the computation locally to lower latency and save bandwidth. Furthermore, ML based visualization tools help in making IoT applications efficient for smart cities, health-care and industrial automation domains. Though the technology was developed years ago, security continues to be a key consideration as blockchain technology ensures secure, tamper proof data management, while federated learning ensures that data is private because it is decentralized during training. It is expected that later IoT edge intelligence can be advanced further from emerging technology such as quantum computing and AI driven automation. Such advancements will enable more scalable, secure and efficient processing frameworks that would lead to making intelligent, autonomous decisioning in the real time environment. As organizations adopt the edge AI solutions, it is important to address their current limitations and exploit the future innovation for the further growth and efficiency of IoT ecosystems.
<title>Abstract</title> The volume of automobiles on roadways keeps growing and crashes increase in frequency, managing traffic routes gets increasingly crucial. Real-time messages are delivered through wireless connections in Intelligent Transport Systems(ITS), although this might raise safety and confidentiality issues. Safety flaws, hefty data processing and transmission costs, and safety vulnerabilities plague current traffic route management ideas. With fog-based ITS's, a simple congestion routing management system was developed to overcome these problems. In this system, automobiles encrypted their travel courses using homomorphic encryption and transfer the secured data onto a fog node. Despite being aware of what specific path was taken by every automobile, Traffic Control Centre (TMC) decodes the received ciphertexts which have been collected by the fog node and manages congestion based on the decoded data. Additionally, the plan makes utilization of the blockchain system to maintain the vehicle's public key. This makes it possible to manage individual vehicle's public key securely and impenetrably, guaranteeing that only authorized cars may join the ITS. The idea was put into operation via the Rinkeby test network based on Ethereum to show that it is feasible. According to the results of the study, this aforementioned approach outperforms other pertinent representative schemes. This lightweight traffic route management system offers a safe and effective method for controlling travel routes in ITSs by utilizing homomorphic encryption and blockchain technology. By addressing the safety and confidentiality concerns raised by sending real-time communications via wireless methods, also lowers the computation and transmission costs of previous ideas. This approach has the potential to improve traffic safety and ease congestion in ITS's.
Hamza Farooq, Ayesha Altaf, Faiza Iqbal, Juan Castanedo Galán · 6 authors
Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization's 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver's impairment level by monitoring the driver's blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model.
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.