Internet of Things and Cyber-Physical Systems最新文献

Digital Twin (DT) has been considered one of the most promising technologies promoting the development of the industry and has successfully achieved significant applications in many fields including national defense, intelligent manufacturing, smart cities, and so on. In addition, DT is gradually used in the research of automobiles which are necessary means of transportation in people's life. A systematic review of the definitions of DT and its applications in automotive-related fields reported in the literature is presented in this paper, with the overall goal to summarize almost all the related research achievements and stimulate innovative thinking. The digital twin, its structure, and its evolution are systematically reviewed. And all reviewed automotive applications of DT are divided into three categories: automotive industry, transportation, and batteries which is the research hotspot in the automotive field. Besides, these methods and ideas will be emphasized in this paper. Particularly, future development directions of digital twins in the automotive field are highlighted.

This paper proposes a blockchain-based decentralized collaborative learning method for the Industrial Internet environment to solve the trust and security issues in Federated Learning. Deploy a decentralized network for collaborative learning based on the alliance chain, design a block data structure suitable for asynchronous learning, and model three stages of computing event triggering, computing task distribution, and computing result integration for cross-domain device collaborative learning. List the critical steps for network deployment, including inspection, tearing down old networks, creating organizational encryption material, creating channels, and deploying chaincode. It also introduces the development of crucial chaincode such as initialization, creation, query, and modification. Finally, the correlation between the number of data pieces of the network, the number of communications, and the time of communications are analyzed through experiments. This paper also proposes a decentralized asynchronous collaborative learning algorithm, develops chaincode middleware between the blockchain network and Artificial Intelligence training, and conducts experimental analysis on the industrial steam volume prediction data set in thermal power generation. The performance on the data set, and the experimental results prove that the asynchronous collaborative learning algorithm proposed in this paper can achieve a good convergence effect. It is also compared with the single-machine single-card regression prediction algorithm, proving that the proposed model has better generalization.

In recent years, artificial intelligence (AI) and machine learning have been transforming the landscape of scientific research. Out of which, the chatbot technology has experienced tremendous advancements in recent years, especially with ChatGPT emerging as a notable AI language model. This comprehensive review delves into the background, applications, key challenges, and future directions of ChatGPT. We begin by exploring its origins, development, and underlying technology, before examining its wide-ranging applications across industries such as customer service, healthcare, and education. We also highlight the critical challenges that ChatGPT faces, including ethical concerns, data biases, and safety issues, while discussing potential mitigation strategies. Finally, we envision the future of ChatGPT by exploring areas of further research and development, focusing on its integration with other technologies, improved human-AI interaction, and addressing the digital divide. This review offers valuable insights for researchers, developers, and stakeholders interested in the ever-evolving landscape of AI-driven conversational agents. This study explores the various ways ChatGPT has been revolutionizing scientific research, spanning from data processing and hypothesis generation to collaboration and public outreach. Furthermore, the paper examines the potential challenges and ethical concerns surrounding the use of ChatGPT in research, while highlighting the importance of striking a balance between AI-assisted innovation and human expertise. The paper presents several ethical issues in existing computing domain and how ChatGPT can invoke challenges to such notion. This work also includes some biases and limitations of ChatGPT. It is worth to note that despite of several controversies and ethical concerns, ChatGPT has attracted remarkable attentions from academia, research, and industries in a very short span of time.

Surface electromyography (sEMG) signals can be used to quantitatively assess muscle fatigue, thereby directly and objectively reflecting the functional state of neuromuscular activity. Effective fatigue diagnosis can prevent muscle damage, thereby improving the safety of rehabilitation exercise. Traditional fatigue diagnosis has certain limitations, including strong subjectivity and poor accuracy. This paper designs a sEMG signals acquisition circuit and collects the sEMG signals of the upper limb biceps brachii and triceps brachii in the force-relaxation state in a dual-channel form. Muscle fatigue classification assessment using Dynamic Time Warping-K Nearest Neighbor (DTW-KNN) and three deep learning algorithms. The experimental results show that compared with traditional machine learning algorithms, deep learning algorithm can achieve higher accuracy and time efficiency. In addition, this study introduces an attention mechanism to dynamically and reasonably assign network weights to achieve high level feature learning. The Attention-Long Short-Term Memory (Attention Based LSTM) neural network achieves 93.5% assessment accuracy with a time overhead of only 3.73s, allowing for real-time assessment of muscle fatigue.

In recent years, attacks against various Internet-of-Things systems, networks, servers, devices, and applications witnessed a sharp increase, especially with the presence of 35.82 billion IoT devices since 2021; a number that could reach up to 75.44 billion by 2025. As a result, security-related attacks against the IoT domain are expected to increase further and their impact risks to seriously affect the underlying IoT systems, networks, devices, and applications. The adoption of standard security (counter) measures is not always effective, especially with the presence of resource-constrained IoT devices. Hence, there is a need to conduct penetration testing at the level of IoT systems. However, the main issue is the fact that IoT consists of a large variety of IoT devices, firmware, hardware, software, application/web-servers, networks, and communication protocols. Therefore, to reduce the effect of these attacks on IoT systems, periodic penetration testing and ethical hacking simulations are highly recommended at different levels (end-devices, infrastructure, and users) for IoT, and can be considered as a suitable solution. Therefore, the focus of this paper is to explain, analyze and assess both technical and non-technical aspects of security vulnerabilities within IoT systems via ethical hacking methods and tools. This would offer practical security solutions that can be adopted based on the assessed risks. This process can be considered as a simulated attack(s) with the goal of identifying any exploitable vulnerability or/and a security gap in any IoT entity (end devices, gateway, or servers) or firmware.

This article aims to discuss the consensus mechanism of software-defined blockchain in the Internet of Things, analyze the characteristics of the traditional consensus mechanism algorithms, on the basis of comparing the advantages of each model, the traditional consensus mechanism algorithm is improved. Later, a supervisable consensus scheme based on improved DPOS-PBFT (Delegated proof of stake-Practical Byzantine Fault Tolerance) was proposed. In the context of the development of the Internet of Things technology, the decentralized distributed computing paradigm is used to improve the blockchain smart contract technology, and the DPOS blockchain consensus mechanism is optimized based on the DPOS protocol of the credit model. In addition, through the dynamic grouping algorithm of credibility, the research ranks the credit level of the consensus nodes of the blockchain network, thus further realizing the supervision of the Internet of Things system. The results of the case analysis show that the success rate of the mechanism algorithm can still be maintained at about 97% after 3000 user requests, the maximum delay remains below 8s after 3000 user requests, the minimum delay is always around 3s, the average delay is 2.38s, the overall performance of the algorithm is superior. It can ensure the final consistency of data transmission of each node in the Internet of Things, the research on the blockchain consensus mechanism in the Internet of Things has practical reference value.

Edge-Cloud Datacenters (ECDCs) have been massively exploited by the owners of technology and industrial centers to satisfy the user demand. At the same time, the amount of energy used by these data centers is considerable. To address this challenge, Virtual Machine (VM) placement of the ECDCs plays an important role; therefore, assigning VM properly to physical machines (PM) can significantly decrease the amount of energy consumption. The applied assigning technique simultaneously must consider additional objectives involving traffic and power usage of the network elements, which makes it a challenging problem. This paper proposes a multi-objective VM placement approach in edge-cloud data centers, which uses Seagull optimization to optimize power and network traffic together. In this strategy, the network traffic among PMs is reduced by concentrating the communications of VMs on the same PMs to reduce the amount of transferred data through the network and reduce the PMs’ power consumption by consolidating VMs to fewer PMs, which consumes less energy. We evaluate with simulations in CloudSim and test two different network topologies, VL2 (Virtual Layer 2) and three-tier, to validate that the proposed approach can effectively reduce traffic and power consumption in ECDCs. The experimental results show that our proposed method can decrease energy consumption by 5.5% while simultaneously reducing network traffic by 70% and the power consumption of the network components by 80%.

When Facebook changed its name to Meta, the upsurge of the metaverse was violently set off. Every company starts claiming they're working on the Metaverse, and everybody talks about the Metaverse. There isn't any formal definition of the Metaverse. This article first introduces the relationship between human beings and the universe, then uses philosophical methods to explore the definitions of the universe and the metaverse, and then tells several philosophical stories related to the metaverse to map the definition of the metaverse.

Web3, the next generation web, promises a decentralized and democratized internet that puts users in control of their data and online identities. However, Web3 faces significant challenges, including scalability, interoperability, regulatory compliance, and energy consumption. To address these challenges, this review paper provides a comprehensive analysis of Web3, including its key advancements and implications, as well as an overview of its major applications in Decentralized Applications (DApps), Decentralized Finance (DeFi), Non-fungible Tokens (NFTs), Decentralized Autonomous Organizations (DAOs), and Supply Chain Management and Provenance Tracking. The paper also discusses the potential social and economic impact of Web3, as well as its integration with emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), and smart cities. This article then discusses importance of zero-trust architecture for Web3. Ultimately, this review highlights the importance of Web3 in shaping the future of the internet and provides insights into the challenges and opportunities that lie ahead.

Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a prominent AI language model. In this study, we examine the foundations, vision, research challenges of ChatGPT. This article investigates into the background and development of the technology behind it, as well as its popular applications. Moreover, we discuss the advantages of bringing everything together through ChatGPT and Internet of Things (IoT). Further, we speculate on the future of ChatGPT by considering various possibilities for study and development, such as energy-efficiency, cybersecurity, enhancing its applicability to additional technologies (Robotics and Computer Vision), strengthening human-AI communications, and bridging the technological gap. Finally, we discuss the important ethics and current trends of ChatGPT.