Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems.

Accounting fraud, usually difficult to detect, can cause significant harm to stakeholders and serious damage to the market. Effective methods of accounting fraud detection are needed for the prevention and governance of accounting fraud.In this study, we develop a novel accounting fraud prediction model using XGBoost, a powerful ensemble learning approach. We respectively select 12 financial ratios, 28 raw accounting numbers and 99 raw accounting numbers available from Chinese listed firms’ financial statements, as the model input. To assess the performance of fraud prediction models, we select two evaluation metrics - AUC and NDCG@k, and two benchmark models - the Dechow et al. (2011) logistic regression model based on financial ratios, and the Bao et al. (2020) AdaBoost model based on raw accounting numbers.Results show that: 1) our XGBoost-based prediction model outperforms two benchmark models by a large margin whatever model inputs and evaluation metrics; 2) the XGBoost-based prediction model with raw accounting numbers input outperforms the one with financial ratios input; 3) the XGoost-based prediction model with 99 raw accounting numbers input outperforms the one with 28 raw accounting numbers input.

Along with the development of network and virtual reality (VR), VR-based telerobotic systems have been widely applied in many fields, especially in hazardous or uncertain environments. But the existing navigation methods of remote robot are implemented by operators using keyboard, mouse joystick, or walking-in-place devices in the VR environments that is telepresence of remote environments. In this paper, we propose a new navigation method for VR-based telerobotic system via operators' real walking in a limited physical space, wherein the motion of a remote robot is synchronized with that of an operator who is actually walking in a limited physical space while naturally interacting (head and body interaction) with the robot and experiencing the remote environment with the help of VR video real-timely captured by the robot. This makes the operators have better sense of presence at the remote sites than the existing methods. We design and implement a system based on this technology, and the test shows that it can make the robot flexible to be navigated in remote complex and dynamic environments.

This special issue (SI) aims to present recent advances in the design and analysis of communication interfaces for Industry 4.0. The Industry 4.0 paradigm aims to integrate advanced manufacturing techniques with Industrial Internet-of-Things (IIoT) to create an agile digital manufacturing ecosystem. The main goal is to instrument production processes by embedding sensors, actuators and other control devices which autonomously communicate with each other throughout the value-chain[1].

The integration knowledge of wearable sensors, wireless communications, and artificial intelligence have brought forth the smart health systems, which empower the consumer’s to make a difference to their well-being by connecting data to personalized analysis to timely insights. Therefore, the real-time data obtained directly reflects the personal status of interest and can be used in a variety of healthcare applications in the Internet of Things (IoT), from preventive treatment to diagnostics and rehabilitation, as well as in virtual and augmented reality environments.

Hosting multiple applications in a shared infrastructure of wireless sensor networks is a trend nowadays, and sharing sensory data for answering concurrent applications is a promising and energy-efficient strategy. To address this challenge, this paper proposes an energy-efficient query optimization mechanism for supporting multiple concurrent applications leveraging our two-tier cooperative caching mechanism. Specifically, query requests for concurrent applications are represented as binary strings, which are reduced to a single one for avoiding the reprocessing of shared subquery requests. This reduced query request is answered through our cooperative caching mechanism, where sensory data, which are highly possible to be reused for answering forthcoming query requests, are cached at the sink node (SN). Besides, the gray model GM(1, 1) is adopted for forecasting sensory data units which may be interested mostly by forthcoming query requests. These units of sensory data may be prefetched from the network and cached at the SN. Experimental evaluation shows that this approach can reduce the energy consumption significantly, and improve the network capacity to an extent, especially when the number of concurrent query requests is relatively large.

As a virtual backbone structure, connected dominating sets (CDSs) play an important role in topology control for wireless networks. In this paper, we develop a distributed self-stabilizing CDS construction algorithm under the SINR model (also known as the physical interference model), a more practical yet more challenging interference model for distributed algorithm design. Specifically, we propose a randomized distributed algorithm that can construct a CDS in O (log n) timeslots with a high probability, where n is the total number of nodes in the network. The constructed CDS achieves constant approximation in both density and diameter. To the best of our knowledge, this is the first known asymptotically optimal self-stabilizing result in terms of both density and diameter for distributed CDS construction under the practical SINR model.

Semantic extraction based on key words is essential method to find relationships of entities. Enterprise relations analysis is becoming more and more popular in financial field, because it gives explanation on business processes and decision-making reference on investments. Most of previous studies focused on the analysis of specific situations and built relations on targeted enterprises. Hence, there is no method based on key words through decomposing sentence to figure out semantics. In this paper, we adopt EOLN model to build enterprise map (EM), taking explicit knowledge and implied relations extraction into consideration. Explicit knowledge means the knowledge can be directly found without reasoning, and implied relations need to be generated by reasoning and computation. We perform comprehensive experiments on data set collected from 2515 different companies, and build the complete EM using the key words based relationship extraction. Experimental results validate the robustness of our proposed approach.

A primary feature of cloud computing is the provision of a variety of transparent services with efficient resource utilization. However, there are concerns with cloud computing in terms of the user's data privacy and security, especially in evidence collection for forensics analysis, because the tangible resources and hardware are out of reach for users who own the data. This paper presents a framework using security information and event management SIEM, to address the issue of efficient evidence collection for crime investigation, such as that for cloud forensics, with respect to the cloud service provider. Indeed, evidence could be shared with cloud users when required. SIEM can be considered as a major player in terms of evidence collection in a virtualized environment. The proposed mechanism using SIEM focuses on passive attacks and provides a solution from the cloud administrator or service provider's point of view. The proposed framework can help in performing detailed cloud forensics in terms of efficient evidence building for crime investigations. Copyright © 2016 John Wiley & Sons, Ltd.

Large-scale machine coordination is a primary approach for flexible manufacturing, enabling large-scale autonomous machines to dynamically coordinate their actions in pursuit of a custom task. One of the key challenges for such large-scale systems is finding high-dimensional coordination decision-making policies. Multitask policy gradient algorithms can be used in search of high-dimensional policies, particularly in collaborative decision support systems and distributed control systems. However, it is difficult for these algorithms to learn online high-dimensional coordination control policies (CCP) from large-scale custom manufacturing tasks. This paper proposes a large-scale online multitask learning and decision-making approach, which can consecutively learn high-dimensional CCP in order to quickly coordinate machine actions online for large-scale custom manufacturing task. A large-scale online multitask leaning algorithm is developed, which is able to learn large-scale high-dimensional CCP in a flexible manufacturing scenario. An online stochastic planning algorithm is proposed, which online optimizes the Markov network structure in order to avoid expensive global search for the optimal policy. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results show the proposed approach to be more efficient when compared with previous works.

Wireless sensor networks (WSNs) are vulnerable to the unfavorable funneling effect. The optimization of WSN clustering is a natural way to suppress the funneling effect. WSN clusters involve the edge effect that was undervalued in existing techniques. We propose an optimal clustering routing protocol GreenOCR to reduce the detrimental influence of the funnel effect and minimize the energy consumption in WSNs. Our work focuses on the approximate unequal optimal clustering and dropping energy consumption arising from the edge effect. First, according to the data repeat rate among overlapped clusters, we estimate the actual data compression ratio to offset the negative influence of the edge effect and save WSN energy. Secondly, we reduce the issue of minimizing the total energy consumption in a WSN to a nonlinear programming (NLP). We have proved that this NLP problem is NP complete. Third, we turn over to exploring an approximate optimal clustering and propose an approximate optimal clustering algorithm. A GreenOCR enabled WSN clustering minimizes the energy consumption in the whole network and extends the lifetime of the WSN. The simulation experiment shows that GreenOCR outperforms its rivals in alleviating the funnel effect.

Target localization and tracking in the Internet of Things (IoT) environment have been paid more and more attention recently. The knowledge and information generated from wireless sensor nodes of the IoT make huge contributions to localization and tracking of targets with high mobility. This paper presents a square-root unscented Kalman filtering (SR-UKF) based localization and tracking algorithm for mobile target in an IoT environment. First, a localization initialization model is proposed for an IoT scenario. Then, according to information of neighboring sensor nodes, we employ the SR-UKF idea for the further localization and tracking of the target. Simulation results demonstrate that the proposed algorithm achieves lower localization and tracking error under the same computational complexity, compared with some conventional extended Kalman filtering (EKF) or UKF based methods. The proposed algorithm is of great significance in the field of IoT information processing.

Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform it to an unconstrained problem and then smooth the derived problem in order to apply usual optimization methods. Numerical experiments prove its successful application in real life credit card dataset.

As the e-commerce is done faster, there is a continuous flourishing of e-marketplaces. Matchmaking is an important aspect of e-commerce interactions. Recently, an approach has been taken to service matchmaking based on semantic Web technologies; the designed matching rule can be used to find the sellers' compatible advertisements for buyers. In this paper, we define three categories of attributes for the matchmaking service. Then we present three factors: semantic matching degree, semantic support and relational confidence to capture the semantic characteristics and relationships of the attributes. And we design a semantic ranking MSC combining the three factors to rank the results of advertisements matchmaking. MSC can capture the semantic aspect of matchmaking results; evaluation shows that MSC makes the process of matchmaking more accurately and the advertisement with the highest MSC is better