Internet of Things and Cyber-Physical Systems最新文献

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.

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.

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 ​% in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 ​%.

When consumers purchase toys from retail stores, the majority of toys are packaged, making it difficult for them to observe the toys comprehensively. This limitation may hinder their ability to make informed purchase decisions. To address this challenge, this paper introduces an immersive toy experience program utilizing augmented reality (AR) technology. The program utilizes the camera on mobile devices to scan and identify the toy's cover image, subsequently showcasing corresponding virtual toy models in a simulated environment. Additionally, interactive controls enable users to manipulate the viewing angles. In terms of methodology, we have specifically designed an expandable collection of toy images, allowing the recognition of recently introduced toys by adding them to the database, enhancing the scalability of our application. In comparison to previous research, our work transcends the constraints of traditional toy shopping, providing a more intuitive, interactive, and personalized experience through AR technology.

Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.

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.