Internet of Things and Cyber-Physical Systems最新文献

Renewable energy systems have mushroomed in the form of microgrids and Virtual Power Plants (VPP). In Australia itself, there are over three million Distributed Energy Resources (DERs). Integrating these new energy ecosystems into current systems is becoming a horrendous task as multi-dimensional energy flows and new energy business models are evolving. The stakeholders in the energy sector are focused on reducing costs prematurely while integration standards are still evolving, and interoperability of Internet of things (IoT) with legacy systems has outstanding security concerns. In this paper, the changing energy landscape is examined, and cybersecurity issues associated with the operation of VPPs and renewable energy-based microgrids are highlighted. The security and operational scenarios of new ecosystems are outlined, with an emphasis on DER interoperability, security, and integration. The design and development of these evolving standards into these new energy ecosystems is detailed based on observations from experiments on real-world DER installations. This paper is an extension of work originally reported in proceedings of the 31st Australasian Universities Power Engineering Conference [1].

This work presents a systematic review of computer vision techniques for locating mobile robots. Its main objectives are to analyze the latest technology in use to locate mobile robots. In addition, this work violated the advances achieved so far, assesses the challenges to be overcome and provides an analysis of future prospects. There is a lot of research related to the location of mobile robots, but the number of review articles on the topic is small, which makes this work of remarkable value. The research covered the works published in Web of Science, Scopus, Science Direct and IEEEXplore until June 2021. Each work found proposes a vision-based localization technique. After being analyzed individually, it can be concluded that it is necessary to assess the level of accuracy of these techniques through a single standard, it is necessary to assess the cost-benefit of the computational cost and the need for more powerful computers or systems to perform the localization task. Finally, the main contributions of this work are the creation of a table summarizing the main methods of localization by computer vision, the statistical analysis performed on the keywords of the selected works and providing an overview of this line of research that can be a basis for future research.

The implementation of Industry 4.0 technologies has improved the flexibility of the entire manufacturing system. These technologies are the Internet of Things (IoT), big data, Artificial Intelligence (AI), Additive Manufacturing (AM), advanced robotics, virtual reality, cloud computing, simulation, and among others, have arisen to improve the flexibility in the entire manufacturing system. Industry 4.0 is becoming recognised as a unique industrial paradigm. It is predicated on the widespread adoption of communication and information technology, which would lead to improved organisational performance and flexibility. The incorporation of Industry 4.0 is the true game-changer in terms of flexibility and customisation. Manufacturers may utilise this technology to build digital twins of items used by consumers in the real world. The digital twin gets real-time information from sensors on the actual objects. Manufacturers benefit from digital twin and simulation technology, including predictive maintenance and making errors easier and faster to rectify. This paper discusses Industry 4.0 and its Flexible Manufacturing System (FMS) capability. Different dimensions and technologies of Industry 4.0 Practices for improving FMS performance are studied,d and then discusses several flexible approaches using Industry 4.0 technologies are. One of the most significant benefits of adopting virtual infrastructure maintained by a service provider is improved flexibility. Cloud services allow auto-scaling, which means that the underlying computer resources automatically adjust to changing utilisation rates. Industry 4.0 increases production flexibility, allowing a facility to respond to market changes quickly. A plant control system automatically varies output depending on shifting utility rates, lowering production costs. Industry 4.0 offers some incredible benefits and has gone a long way in the last several years.

This work aims to study the role of Digital Twins (DTs) technology combined with the Metaheuristic Optimization Algorithm in manufacturing energy efficiency optimization. Firstly,a machine tool model is established based on DTs technology to study the energy consumption of the milling process of Computer Numerical Machine Tools. Besides, the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the milling parameters of the machining process. Meanwhile, the tool machining path is optimized by combining the Optimized Genetic algorithm and Simulated Annealing algorithm and find the optimal solution of the machining path. On the premise of ensuring the machining quality, this scheme improves the machining efficiency, reduces the energy consumption of the processing process, and improve the energy efficiency. The results demonstrate that the optimized milling parameters can ensure the lowest milling power while considering the maximum material removal rate. Take the plane model as an example. The Improved Genetic Algorithm-Simulated Annealing algorithm can significantly reduce the number of empty walking knife by adopting projection machining and helical machining. The total length of the milling path is reduced by 69.45 ​mm at most, a relative reduction of 10.01%. The average measured energy consumption of milling is reduced by 5.62W∗h compared with the empirical value; the measured average energy consumption of the optimized idle tool is reduced by 0.17W∗h; the total measured energy consumption of milling processing is reduced by 5.73W∗h compared with the empirical value. It can be seen that the optimization algorithm can improve the processing efficiency and reduce the energy consumption.

In a cyber-physical system (CPS) built on Internet-of-things (IoT) technologies, whenever measurement and control signals are transferred over communication networks across cyber and physical systems, it potentially becomes a target for adversaries. The problem becomes especially serious if the adversaries are insiders. A single layer of defense may not be strong enough in such a case, as it is difficult to assess the extent of knowledge that the inside attackers may have known about the physical and cyber system configurations, communication networks/protocols, and their respective vulnerabilities. Hence, it is paramount to have a reliable and fail-safe defense-in-depth architecture to fence off would-be-attackers. In this paper, a multi-layer defense-in-depth approach has been developed. For an inside attacker with legitimate access, the first line of defense, such as access control, may have already been compromised. Given this fact, the focus of the current paper has been on detection and mitigation. Both data-driven and model-based techniques are considered to catch stealthy attacks and stop them in their tracks. Effective mitigation techniques can then be deployed to minimize the adverse effects. To demonstrate this design philosophy and validate the effectiveness of the developed methodologies, a lab-scale cyber-physical system platform based on industry-grade communication networks and physical sensors has been used for validation.

The unmanned vehicles (UAVs) are most useful communication elements in current technology platform. The emerging communication applications like IoT, cloud and Datamining are most prominent to surveillance. This research study is concentrated on IoT (Internet if things) based UAVs design based on information collection methodology. Autonomous flying vehicles with IoT connectivity to ensure site health and safety applications. It goes through the fundamental limits and flaws of current state-of-the-art solutions for the same goal, such as route planning optimization challenges, lightweight machine learning (ML) and machine vision algorithms, coordination in IoT communications, and IoT network scaling. As a result, this article will assist the reader in delving deeper into a variety of open research questions.

When the Internet of Things (IoT) is used in a typical manufacturing system, the industrial plant can be controlled remotely through the Internet. This enables manufacturing and execution systems to obtain real-time work orders directly from the Enterprise Resource Planning (ERP) system. Therefore, workflows for development, production, and manufacturing can be integrated with sales, market, and finance business processes. The possibility of implementing this integration, however, is dependent on the trust, security, and authentication of IoT devices. Many IoT devices face significant security risks such as device hijacking and data leaks due to limited resources and inadequate self-protection capabilities. Despite the fact that several studies have been conducted using the physical unclonable function to protect communication between IoT devices from the aforementioned security threats, current solutions rely on the participation of the server to distribute the key parameters, which requires high message overhead and has a significant impact on efficiency. To fill this gap, this article proposes a Consistent Round Hash optimized SRP-6a-based end-to-end mutual authentication for secure data transfer technique with single-share trusted device collaboration can detect an unauthenticated device. In addition, our proposed technique ensures the overall system's integrity and stability during a scaling-out phenomenon, which is becoming increasingly common in complex industrial environments. Furthermore, we present a formal and informal security analysis of the proposed protocol. According to the results of the performance analysis, our proposed technique has the lowest communication overhead, computational cost, and round-trip time when compared to other state-of-the-art schemes.

Sustainability 4.0 is being enabled through the effective adoption of modern technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Machine Vision (MV), Data Analytics (DA), Additive Manufacturing (AM) and other modern technologies. These technologies enable services at significantly lower prices due to the effective use of energy and resources with lesser wastage. Manufacturers are constantly looking for methods to lower the operating expenses associated with production processes. Manufacturers might optimise their value chain's production and associated processes by adopting sustainability 4.0 technologies. Such technologies will help manufacturers select their optimal facilities and employees, lower operational costs, enhance productivity and resource utilisation and provide a picture of process gaps that can be addressed. Sustainability is based on the effective use and reuse of resources across the product life cycle, from materials and processes to equipment and skills. Sustainable manufacturing produces manufactured goods using economically viable procedures that reduce negative environmental consequences while preserving energy and natural resources. This paper briefs Sustainability 4.0 and its significant needs. Various fundamental technologies and futuristic research aspects for Sustainability 4.0 are discussed diagrammatically. Finally, we identified and discussed significant applications of Sustainability 4.0 in manufacturing. Sustainability 4.0 refers to a long-term vision for enterprises that allows them to continue perpetually without depleting resources faster than they can be replaced. Sustainability 4.0 entails empowering prosumers to co-create to reshape the economy and society toward social inclusion and environmental sustainability. The use of sustainability and digitisation to solve environmental, social, and economic problems appears hopeful and exhausting.

The establishment of smart city database requires Internet of things technology. With the continuous expansion of the scale of the Internet of things data center, the energy consumption of the data center is increasing, which greatly limits the development of the Internet of things data center. Energy consumption of Internet of things network data center in China's smart city has increased year by year, and data center network energy conservation has become a current research hotspot. The traditional network nodes are distributed and centralized innovatively to complete centralized control, and the controller completes the collection of the whole network information, the maintenance of network status, the distribution of flow entry, etc. Firstly, the energy-saving model of digital center network and the model data center network traffic prediction algorithm based on the principle of high accuracy traffic prediction are constructed, and an energy-saving multi-layer virtual traffic scheduling algorithm is proposed. Secondly, the two algorithms are fused, and finally an empirical study is carried out. The results show that in Random mode, the energy consumption ratio of energy-efficient multi-layer virtual-software defined networking (EMV-SDN) is the lowest, and the maximum reduction of energy consumption ratio reaches 7.8% compared with equal-cost multi-path (ECMP) algorithm. In Staggered mode and Stride mode, the energy consumption ratio of EMV-SDN algorithm is the lowest. Compared with the actual data flow, the prediction result of K-means-support vector machine (KM-SVM) algorithm is closer to the actual result, and the maximum error between the predicted value and the actual value of KM-SVM algorithm is 1.2 ​Gbps. However, the maximum error of balanced iterative reducing and clustering using hierarchies-support vector machine (B-SVM) algorithm reaches 3.1 ​Gbps; the prediction accuracy of KM-SVM algorithm is always higher than that of B-SVM algorithm in both discontinuous data flow and continuous actual data flow; the accuracy of KM-SVM algorithm in different experiments is high; among the energy consumption ratios of the combined algorithm, the energy consumption ratio of EMV-SDN algorithm is the lowest under the three communication modes, and the performance of the algorithm constructed in this study is higher than that of other algorithms in simulation operation; when the traffic prediction algorithm is combined with the virtual topology energy conservation control algorithm, the network structure change is reduced and the network stability is increased in this study. In the delay comparison of EMV-SDN algorithm, ECMP algorithm, and Dijkstra algorithm in the three communication modes, the EMV-SDN algorithm has the best performance. The results of this study provide an improvement direction for the development of Internet of Things technology and the construction of smart cities.

This exploration is aimed at the system prediction and safety performance of the Digital Twins (DTs) of autonomous cars based on artificial intelligence technology, and the intelligent development of transportation in the smart city. On the one hand, considering the problem of safe driving of autonomous cars in intelligent transportation systems, it is essential to ensure the transmission safety of vehicle data and realize the load balancing scheduling of data transmission resources. On the other hand, convolution neural network (CNN) of the deep learning algorithm is adopted and improved, and then, the DTs technology is introduced. Finally, an autonomous cars DTs prediction model based on network load balancing and spatial-temporal graph convolution network is constructed. Moreover, through simulation, the performance of this model is analyzed from perspectives of Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that in comparative analysis, the accuracy of road network prediction of the model reported here is 92.70%, which is at least 2.92% higher than that of the models proposed by other scholars. Through the analysis of the security performance of network data transmission, it is found that this model achieves a lower average delay time than other comparative models. Besides, the message delivery rate is basically stable at 80%, and the message leakage rate is basically stable at about 10%. Therefore, the prediction model for autonomous cars constructed here not only ensures low delay but also has excellent network security performance, so that information can interact more efficiently. The research outcome can provide an experimental basis for intelligent development and safety performance improvement in the transportation field of smart cities.