Internet of Things and Cyber-Physical Systems最新文献

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.

With the rapid advancement of Internet of Things (IoT) technology, its applications in road infrastructure have garnered attention. However, challenges persist when applying IoT to road infrastructure monitoring, including insufficient durability of front-end sensors, pavement damage due to sensor embedding, and the redundancy of a vast amount of real-time data, hindering the long-term real-time monitoring of pavements. To address these challenges, this study developed a self-powered distributed intelligent pavement monitoring system based on IoT, encompassing a sensor network, cloud platform, communication network, and power supply system. Considering the specific characteristics of slipform paving for cement concrete pavements, an integrated paving process was proposed, merging embedded sensors with pavement material structures. Through on-site engineering monitoring, the system actively collects and analyzes various data types such as system energy consumption, temperature and humidity, environmental noise, wind speed and direction, and pavement structural vibrations, providing data support for pavement design, maintenance, and vehicle-road synergy applications. Future efforts will continue to promote the application of IoT technology in digital road maintenance, traffic safety, and optimized pavement material structure design.

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported “hallucinations” within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

Ransomware attacks have emerged as one of the most significant cyberthreats faced by organizations worldwide. In recent years, ransomware has also started to target critical infrastructure and Cyber-Physical Systems (CPS) such as industrial control systems, smart grids, and healthcare networks. The unique attack surface and safety-critical nature of CPS introduce new challenges in defending against ransomware. This paper provides a comprehensive overview of ransomware threats to CPS. We propose a dual taxonomy to classify ransomware attacks on CPS based on infection vectors, targets, objectives, and technical attributes. Through an analysis of 10 real-world incidents, we highlight attack patterns, vulnerabilities, and impacts of ransomware campaigns against critical systems and facilities. Based on the insights gained, we identify open research problems and future directions to improve ransomware resilience in CPS environments.

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.

Sharding technology can address the throughput and scalability limitations that arise when single-chain blockchain are applied in the Internet of Things (IoT). However, existing sharding solutions focus on addressing issues like malicious nodes clustering and cross-shard transactions. Existing sharding solutions cannot adapt to the performance disparities of edge nodes and the characteristic of three-dimensional data queries in building IoT. This leads to problems such as shard overheating and inefficient data query efficiency. This paper proposes a dual-layer architecture called S-DAG, which combines sharded blockchain and DAG blockchain. The sharded blockchain processes transactions within the building IoT, while the DAG blockchain stores block headers from the sharded network. By designing an Adaptive Balancing Load Algorithm (ABLA) for periodic network sharding, nodes are divided based on their load performance values to prevent the aggregation of low-load performance nodes and the resulting issue of shard overheating. By combining the characteristics of the KD tree and Merkle tree, a block structure known as 3D-Merkle tree is designed to support three-dimensional data queries, enhancing the efficiency of three-dimensional data queries in building IoT. By deploying and conducting simulation experiments on various physical devices, we have verified the effectiveness of the solution proposed in this paper. The results indicate that, compared to other solutions, the proposed solution is better suited for building IoT data management. ABLA is effective in preventing shard overheating issue, and the 3D-Merkle tree significantly enhances data query efficiency.

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO₂ levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO₂. The multi-room monitoring concept of indoor-CO₂ levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes.

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.

Barcelos is a historic city in Portugal with many tourist attractions, attracting more and more visitors who come to the city with the aim of exploring it. The main objective of this article is to boost tourism in the city of Barcelos, specifically highlighting tourist, historical and leisure spots, based on the development of a mobile application using augmented reality technologies and geolocation. This application intends to allow the users to know historical points of interest in Barcelos, as well as interact with a certain point. The results of this investigation were evaluated by testing the application by end users, with the aim of identifying whether the application meets their needs, in particular the promotion of tourist and historical points.

The significance of intrusion detection systems in networks has grown because of the digital revolution and increased operations. The intrusion detection method classifies the network traffic as threat or normal based on the data features. The Intrusion detection system faces a trade-off between various parameters such as detection accuracy, relevance, redundancy, false alarm rate, and other objectives. The paper presents a systematic review of intrusion detection in Internet of Things (IoT) networks using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities and reducing the chances of security attacks. MOAs provide a set of optimized solutions for the intrusion detection process in highly complex IoT networks. This paper presents the identification of multiple objectives of intrusion detection, comparative analysis of multi-objective algorithms for intrusion detection in IoT based on their approaches, and the datasets used for their evaluation. The multi-objective optimization algorithms show the encouraging potential in IoT networks to enhance multiple conflicting objectives for intrusion detection. Additionally, the current challenges and future research ideas are identified. In addition to demonstrating new advancements in intrusion detection techniques, this study attempts to identify research gaps that can be addressed while designing intrusion detection systems for IoT networks.