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.