Internet of Things and Cyber-Physical Systems最新文献

Green buildings are designed and constructed according to the principles of sustainable development and are an inevitable trend in future architectural development. Nowadays, many works have studied the application of intelligence or intelligent technology in green intelligent buildings, but there is still insufficient discussion on how to integrate intelligent technology into all aspects of buildings. In view of this, this paper summarizes the design concepts of modern green buildings and takes this as the starting point to explore the classification and construction of the core needs for achieving sustainable development throughout the life cycle of buildings from five aspects: building design, building materials, building construction, building renewal and management, and building damage, and analyze the integration of relevant intelligent technologies in buildings under different needs.

The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive real time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.

The concept of a smart city is geared towards enhancing convenience and the efficient management of city areas through innovation. As Metaverse rises in the 2020s, providing the possible direction for a new generation of the Internet, it has a huge number of opportunities to promote smart cities. The Metaverse can empower smart cities in various aspects. In this article, we provide a detailed review of smart cities based on Metaverse technologies. Firstly, we introduce the Metaverse and smart cities and describe the future vision and applications of smart cities, which are based on the Metaverse. In addition, we discuss the essential technologies for smart cities in the Metaverse and the currently available solutions. Additionally, we have some concerns regarding the potential of Metaverse and there are still unresolved issues that should be addressed. The purpose of this article is to provide researchers and developers with essential guidance and opportunities to propel the development of the Metaverse and smart cities.

In response to an alarming shortage of workers in cybersecurity and a growing skills gap, the U.S. Department of Defense is taking steps to build cybersecurity capacity through workforce training and education. In this paper, we present an approach to address this shortage and skills gap through the development of cybersecurity education courseware for internet of things (IoT) applications.

To attract students and workers into the field of cybersecurity, it is important to design courseware that is exciting and tied to real-world problems. We describe our design for an embedded systems course taught at the graduate level for engineering and computer science students. The innovation in our approach is to select the fast-growing domain of healthcare and feature different IoT sensors that are seeing increased usage. These include barcode scanners, cameras, fingerprint sensors, and pulse sensors. These devices cover important functions such as patient identification, monitoring, and creating electronic health records. We use a password protected MySQL database as a model for electronic health records. We also demonstrate potential vulnerabilities of these databases to SQL injection attacks.

We administered these labs and collected survey data from the students. We found a significant increase in student understanding of cybersecurity issues. The mean confidence level of the students in cybersecurity issues increased from 2.5 to 4.1 on a 5-point scale after taking this course, which represents a 65% increase. The instructional lab material has been uploaded to the web portal https://clark.center designated by the National Security Agency for dissemination. Our approach, design, and experimental validation methodology will be useful for educators, researchers, students, and organizations interested in re-skilling their workforce.

The localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) is inaccurate due to the various underwater forces (viscous, hydrodynamic forces, perturbation of underwater). The false measurements in the sensor network cause position errors and velocity errors which disrupt the localization of the moving target. A randomly fluctuated spillover effect is introduced in the present paper. The absorption losses generated due to the spillover effect cause false measurements of the moving target. Theorem 1 describes the genesis of these absorption losses and their consequences in UAWSN. The measurements from each moving target in the presence of absorption losses are formulated in the elliptical region. A joint probabilistic data association (JPDA) method is proposed to quantify the false measurements in the elliptical region. A moving target state estimation (MTSE) algorithm is proposed to eliminate the false measurements from the moving targets and to measure the localization of moving targets with the help of the propagation speed of targets. The theoretical measurements of position RMSE and velocity RMSE are verified with standard methods. The proposed MTSE method improves the localization performance of the moving targets by 29.42 % and reduces 32.16 % of position errors and 36.23 % of velocity errors up to 550 ​m. The proposed algorithm will be useful for the sub-aquatic Internet of underwater things (IoUT).

Modern civil engineering structures, instrumented with Internet-of-Things-enabled smart sensors and actuators, are considered cyber-physical systems that integrate physical processes with computational and communication elements. This short communication aims to portray a milestone in the field of monitoring and inspection of civil infrastructure, collaboratively conducted by autonomous, robotic devices orchestrated in robotic fleets. It is expected that robot-based civil infrastructure assessment will revolutionize structural maintenance of the deteriorating building stock, which is increasingly exacerbated by the effects of climate change and develops into a major societal challenge.

In recent years, the Internet of Things (IoT) has experienced extensive adoption in industrial environments, healthcare, smart cities, and more, playing a vital role in these domains. Within IoT-based systems, wireless sensor networks (WSNs) have emerged as a crucial method for collecting peripheral environmental data within industries, owing to their self-organizational attributes. Nevertheless, the enormous volume of heterogeneous data from various sensing devices presents many challenges for IoT-enabled WSNs, encompassing high transmission delay times (TD) and excessive battery energy consumption (EC). To address these challenges, it is imperative to prioritize efficiency and optimize energy utilization. Moreover, enhancing energy efficiency within the Industrial Internet of Things (IIoT) realm hinges significantly on factors such as data transmission modes and the allocation of cluster head nodes. Numerous researchers have proposed algorithms to minimize transmission time and energy consumption, specifically focusing on industrial environments. This paper introduces an inventive clustering-based data transmission algorithm for IIoT, LEACH-D, to enhance efficiency. The LEACH-D algorithm improves the transmission task duration while maintaining consistent battery energy consumption. It also seeks to elevate performance in metrics such as average transmission time during the first node death (FND). Numerous experimental results provide strong evidence that the algorithm introduced in this paper has effectively reduced the average transmission time by remarkable percentages: 51.32%, 12.12%, 12.96%, and 5.42%, while simultaneously increasing the number of FND rounds by significant margins: 222.43%, 36.63%, 33.72%, and 7.81%, respectively. These improvements stand in stark contrast to the performance of existing algorithms, including FREE_MODE, LEACH, EE-LEACH, and ETH-LEACH.

The Internet of Things (IoT) has revolutionized modern tech with interconnected smart devices. While these innovations offer unprecedented opportunities, they also introduce complex security challenges. Cybersecurity is a pivotal concern for intrusion detection systems (IDS). Deep Learning has shown promise in effectively detecting and preventing cyberattacks on IoT devices. Although IDS is vital for safeguarding sensitive information by identifying and mitigating suspicious activities, conventional IDS solutions grapple with challenges in the IoT context. This paper delves into the cutting-edge intrusion detection methods for IoT security, anchored in Deep Learning. We review recent advancements in IDS for IoT, highlighting the underlying deep learning algorithms, associated datasets, types of attacks, and evaluation metrics. Further, we discuss the challenges faced in deploying Deep Learning for IoT security and suggest potential areas for future research. This survey will guide researchers and industry experts in adopting Deep Learning techniques in IoT security and intrusion detection.

Natural disasters (NDs) have always been a major threat to human lives and infrastructure, causing immense damage and loss. In recent years, the increasing frequency and severity of natural disasters have highlighted the need for more effective and efficient disaster management strategies. In this context, the use of technology has emerged as a promising solution. In this survey paper, we explore the employment of recent technologies in order to relieve the impacts of various natural disasters. We provide an overview of how different technologies such as Remote Sensing, Radars and Satellite Imaging, internet-of-things (IoT), Smartphones, and Social Media can be utilized in the management of NDs. By utilizing these technologies, we can predict, respond, and recover from NDs more effectively, potentially saving human lives and minimizing infrastructure damage. The paper also highlights the potential benefits, limitations, and challenges associated with the implementation of these technologies for natural disaster management purposes. While the use of technology can significantly improve NDM, there are also various challenges that need to be addressed, such as the cost of implementation and the need for specialized knowledge and skills. Overall, this survey paper provides a comprehensive overview of the use of technology in managing NDs and sheds light on the important role such technologies can play in NDM. By exploring the potential applications of different technologies, this paper aims to contribute to the development of more effective and sustainable disaster management strategies.