Internet of Things and Cyber-Physical Systems最新文献

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.

As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT.

A RepNet-based wireless self-powered sensor system is designed by just two components with deep learning algorithm, which has simple structure and high accuracy even without integrated circuit. Triboelectric nanogenerator (TENG) directly power the artificial intelligence sensor, and the algorithm extracts and encodes the convolutional features and local temporal information from a video. To test this model, we assemble a test dataset of 192 videos, comprising 32 frequencies of TENG. We then show the real-time detection backend based on the RepNet. This deep-learning-based backend also works well and demonstrates great feasibility and potential in the applications such as counting the number of LED flashing, estimating the possibility of LED flashing and detecting the changes of frequency. It is a potential and novel approach for sensing and transmited information of TENG-based self-powered sensors.

Block-chain-based Identity and access management framework is a promising solution to privacy and security issues raised during the exchange of patient data in the healthcare industry. This technology ensures the confidentiality and integrity of sensitive information by providing a decentralized and immutable ledger. In our research, we propose an identity and access management system that employs Hyper-ledger Fabric and OAuth 2.0 for improved security and scalability. This combination allows for transparency and immutability of user transactions and minimizes the risk of fraud and unauthorized access. Additionally, Hyper-ledger Fabric's privacy, security, and scalability features enable granular access control to sensitive information, while OAuth 2.0 authorizes only trusted third-party applications to access specific data on the Fabric network. The proposed approach can handle large volumes of data and support multiple applications, thus providing a secure and scalable solution for managing access to the Fabric network. Moreover, our solution employs Role-based access control based on the patient's role, ensuring privacy and confidentiality. Our statistical analysis demonstrates that the proposed approach can efficiently and securely manage patient identity and access, potentially transforming the healthcare industry by enhancing data interoperability, reducing fraud and errors, and improving patient privacy and security. Furthermore, our solution can facilitate compliance with regulatory requirements such as HIPAA and GDPR.

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and mapping them according to task can also be an option to deal with such task failure or mismanaged resource allocation. Density-based spatial clustering of applications with noise (DBSCAN) is a stochastic approach-based algorithm which has the capability to cluster the resources in a cloud environment. The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. The simulation is cone using CloudsSim 5.0 tool kit. The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib ​= ​g bang Big Crunch) and WHO(Whale optimization algorithm).