Internet of Things and Cyber-Physical Systems最新文献

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.

This paper proposes a solar-powered portable water pump (SPWP) for IoT-enabled smart irrigation system (IoT-SIS). A NodeMCU microcontroller with a Wi-Fi interface and soil moisture, temperature, and humidity sensors are exploited to monitor and control the water pump and build an IoT-based irrigation system. The proposed irrigation system updates the gathered information from sensors (moisture, temperature, and humidity) using an integrated algorithm to the Blynk IoT cloud in real-time. Farmers can access this information and control the water pump accordingly via a user-friendly interface using their smartphones. The portable and eco-friendly water pump is powered via a solar panel and can be controlled using Blynk mobile application, which is also used to monitor the surroundings. The fabricated pump is inspired by wheeled travel luggage and is equipped with a water filter and multi-nozzles sprayer. The developed solar-based water pump has managed to save electricity and mitigate operational costs. Furthermore, the integration of the IoT concept has facilitated real-time monitoring and control of the pump; thus, enhancing the water usage efficiency and enabling convenient farming operations. The system functionality has been practically tested in a real environment, and its performance has been evaluated.

Secure systems must be built in a systematic and holistic way, where security is an integral part of the development lifecycle and cuts across all architectural layers. This need is more evident in Cyber Physical Systems (CPSs), where attacks may target not only the information model of the system but also its physical entities. CPS systems are heterogeneous and often highly complex. Their possibly numerous components and cross-domain complexity make attacks easy to propagate and security difficult to implement. Moreover, this complexity results in a considerable variety of vulnerabilities and a large attack surface. To design secure CPS systems a good approach is to abstract their complexity and develop a common framework, namely a Reference Architecture (RA), to which we add security mechanisms in appropriate places to stop its threats to define a Security Reference Architecture (SRA). An SRA is an abstract architecture describing a conceptual model of security that provides a way to specify security requirements for a wide range of derived concrete architectures. An important type of CPS is a maritime container terminal, a facility where cargo containers are transported between ships and land vehicles for onward transportation, and vice versa. We present here an SRA for cargo ports built out of patterns, which goes beyond existing models in providing a global view and a more precise description than just block diagrams. Starting from an RA, we analyze security issues in each activity of the processes of the system and enumerate its threats. We describe threats using misuse patterns, and from them we select security patterns that realize defensive solutions.

Framework and objectives

COVID-19 epidemic has sparked concern and has elevated the need for therapeutic tools, health equipment's, and day-to-day necessities for healthcare workers' well-being. The goal of this study is to uncover the operational problems that suppliers encounter when it comes to offering effective services. The research also intends to offer an Industry 4.0 strategy for reducing COVID-19's effect. The problems are weighed and priority is assigned by multi-criteria decision making to identify the most essential parameter which impacts the suppliers.

Methods

A comprehensive literature assessment on the rampant eruption of COVID 19 and supply chain is conducted with the aid of literatures available on SCOPUS, Science Direct, and Google Scholar using appropriate keywords. To get further insights, certain pertinent and applicable industry reports and blogs are also used. Problems were analysed with AHP method and priority was assigned by technique for order performance by similarity to ideal solution (TOPSIS). Weights are calculated by AHP method and assigned to each criteria attribute.

Results

We recognized eleven key problems that serve as an operational obstacle in the retail industry and proposed the use of Industry 4.0 technology to address them. The contemporary study is accomplished by using hybrid combination of two Multi Criteria Estimators methods- Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Further, the most significant problem comes out to be Maintenance of an appropriate balance among supply and demand followed by Lack of Viability.

Key findings

Prioritization of supply chain problems are arranged in descending order Maintenance of an appropriate balance among supply and demand ​> ​Lack of Viability ​> ​Absence of government funding ​> ​Lack of access ​> ​Absence of Confidence ​> ​Scarcity of work force ​> ​Lack of security and safety ​> ​Deficiency of surplus medical amenities ​> ​Consumer attitude ​> Absence of Supply Chain flexibility ​> ​Communication problems.

Conclusion

In order to combat the pandemic, Industry 4.0 can play a key role in lowering the effect of identified issues on retailers. For the successful administration of healthcare basics, trust and openness are required. To enhance services, suppliers, distributors and policy makers should make informed decisions during COVID-19 and other comparable events. Therefore, suggested guidelines and framework will offer upcoming directions for research in fields of pandemic check, business logistics management, and catastrophe administration. Balance in supply and demand is the most significant attri