Internet of Things and Cyber-Physical Systems最新文献

Media transmission in the context of constrained IoT devices is of critical importance to support several solutions that enable interaction with the physical environment. This includes solutions that range from scenarios of occupancy estimation in building automation to traditional audio and image processing in remote sensing. Many modern access side IoT networks follow Event Driven Architecture (EDA) topologies that rely on brokers to forward messages between endpoints. The Message Queuing Telemetry Transport (MQTT) is one such protocol that can be used to encapsulate well known Real Time Protocol (RTP) audio and media packets. Unfortunately, MQTT relies on TCP transport that is not well suited in IoT constrained environments. This paper introduces a scheme that leverages some of the MQTT features to enable the reliable transmission of media in the context of Lossy Low Power Networks (LLNs). Specifically, this scheme is presented, analyzed, optimized and compared against other state-of-the-art mechanisms.

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices.

In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and general implementation problems, presenting several sectors that can benefit the most from IoT development. Discussion over the findings of this review reveals open issues and challenges and specifies the next steps required to expand and support IoT systems in a secure framework.

This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network.

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.