Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

Augmented Spaces: Introducing a Technology-Supported Home Environment to Improve our Mental Health during "Work from Home” Isolation

During the COVID-19 pandemic many restrictions were implemented to prevent the spread of the disease. These restrictions included working from home (WFH) and self-isolation. However, this situation had a negative impact on our mental health, causing depression and anxiety in many employees around the world. In this context, we hypothesized that our home spaces could become a catalyst of positive emotions through the use of technology-supported home environments, which use cyber-physical systems to reduce mental health symptoms during the lockdown. We used a qualitative approach, through interviews and cultural probes, to understand the experience of people who were forced to work from home during the lockdown. Additionally, we used a design science approach to explore technology-supported solutions that could enhance our home spaces. The result is a system that mixes analog and digital elements to create interactive rooms, which have a positive impact on people's well-being. © 2022 IEEE Computer Society. All rights reserved.

Cyber-physical customer management for internet of robotic things-enabled banking

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymise the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations. Author

A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Geo-Spatial Context Provision for Digital Twin Generation

Light detection and ranging technology allows for the creation of detailed 3D point clouds of physical objects and environments. Therefore, it has the potential to provide valuable information for the operation of various kinds of cyber-physical systems that need to be aware of, and interact with, their surroundings, such as autonomous vehicles and robots. Point clouds can also become the basis for the creation of digital representations of different assets and a system's operational environment. This article outlines a system architecture that integrates the geo-spatial context information provided by LiDAR scans with behavioral models of the components of a cyber-physical system to create a digital twin. The clear distinction between behavior and data sets the proposed digital twin architecture apart from existing approaches (that primarily focus on the data aspect), and promotes contextual digital twin generation through executable process models. A vaccine logistics automation use case is detailed to illustrate how information regarding the environment can be used for the operation of an autonomous robot carrying out transport preparation tasks. Besides supporting operation, we propose to combine context data retrieved from the system at different points in the logistics process with information regarding instances of executable behavior models as part of the digital twin architecture. The twin can subsequently be used to facilitate system and process monitoring through relevant stakeholders and structure context data in a user-centric fashion.

Industry 5.0: From Manufacturing Industry to Sustainable Society

Industry 4.0 brought a new revolution in industries by making them fully automated via innovative technologies, without considering human-power. Industry 4.0 aims to establish 'smart manufacturing industry' by emphasizing on Information Technology (IT), Internet of Things (IOT), Cyber Physical System (CPS), Industrial Internet of Things (IIOT), Artificial Intelligence (AI), Big Data, and Robotics. This highly automated industry neglected human's intellectual and cognitive skills, causing an increase in unemployment rate and devastation of ecosystem. In this paper, we proposed a framework of emerging technologies of Industry 5.0. Here, we examined how Industry 5.0 will further extend the development of Industry 4.0 and how humans can contribute to its manufacturing process. In addition, prestigious and significant skills for workforce in manufacturing industry are also explored. We also investigated how the Covid-19 epidemic was associated to Industry 5.0 and the idea of sustainable development goals (SDGs). Finally, we highlighted some of the challenges facing the industrial sector as research direction of Industry 5.0. © 2022 IEEE.

Uncertainty-aware transfer learning to evolve digital twins for industrial elevators

Digital twins are increasingly developed to support the development, operation, and maintenance of cyber-physical systems such as industrial elevators. However, industrial elevators continuously evolve due to changes in physical installations, introducing new software features, updating existing ones, and making changes due to regulations (e.g., enforcing restricted elevator capacity due to COVID-19), etc. Thus, digital twin functionalities (often built on neural network-based models) need to evolve themselves constantly to be synchronized with the industrial elevators. Such an evolution is preferred to be automated, as manual evolution is time-consuming and error-prone. Moreover, collecting sufficient data to re-train neural network models of digital twins could be expensive or even infeasible. To this end, we propose unceRtaInty-aware tranSfer lEarning enriched Digital Twins LATTICE, a transfer learning based approach capable of transferring knowledge about the waiting time prediction capability of a digital twin of an industrial elevator across different scenarios. LATTICE also leverages uncertainty quantification to further improve its effectiveness. To evaluate LATTICE, we conducted experiments with 10 versions of an elevator dispatching software from Orona, Spain, which are deployed in a Software in the Loop (SiL) environment. Experiment results show that LATTICE, on average, improves the Mean Squared Error by 13.131% and the utilization of uncertainty quantification further improves it by 2.71%. © 2022 ACM.

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems.

Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.

Rapid medical guideline systems for COVID-19 using database-centric modeling and validation of cyber-physical systems

Database-centric representation is a vital subfield of cyber-physical systems in healthcare. Medical guideline systems have the ultimate objective of improving the safety of the patient. COVID-19 was recognized as a pandemic in March, 2020 by the World Health Organization. A recent study shows the importance of the rapid medical guidelines system in the medical field. Safe and effective patient care is a significant challenge during the COVID-19 pandemic. The excellent knowledge and experiences patients care about are required in the nation’s developed places and as essential effective proper treatments for critical patients in the rural areas when it comes to an emergency. Medical guideline systems can help to improve patient care. The research community of system software development in healthcare is more focused on cyber-physical systems. Restricting the development environment and reducing the flexibility of design by enforcing a specific software platform with less database provides fewer quality systems in healthcare. To improve patient care by assisting the medical professional’s detailed design with the validation of the systems at the design level are required. This research focuses on database-centric software development steps with the validation technique of the cyber-physical system for COVID-19. In particular, the study focuses on the medical guidelines system development process with validation and verification at the design level. The development of rapid medical guideline systems for COVID-19 has been discussed with database-centric modeling with the knowledge that expects validation and artificial intelligence verification. Thus the medical professional’s study-based design of the cyber-physical system for COVID-19 can reduce development time, cost of deployment, testing, and maintenance during the repaid development process in the global pandemic period. © 2022 Elsevier Inc. All rights reserved.

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

Cyber-Physical Verification of Intermittently Powered Embedded Systems

Intermittently powered embedded systems are a foundational and growing component of the Internet-of-Things. It is essential to rigorously prove these systems’correctness because they arise both in safety-critical applications and applications where quality-of-service is essential to social good. Such proofs are challenging because they are simultaneously cyber-physical and time-sensitive: correctness is affected by physical properties that change with time. This paper introduces a new general-purpose formal verification approach for cyber-physical properties of intermittent systems. We define a high-level modeling and specification language for intermittent systems, define its formal semantics, and prove that the language reduces to hybrid games, enabling application of existing theorem-proving software. Cold storage for COVID vaccines serves as a running example;we provide a machine-checked proof that safe temperatures are maintained under suitable assumptions. The crux of our proof approach is to identify power and timing assumptions under which sufficient power is available to complete time-sensitive tasks. Orthogonal to approaches that prove new guarantees on power or timing, our work rigorously shows which power and timing assumptions are needed for cyber-physical correctness. IEEE

Medical Cyber-Physical Systems Enabled with Permissioned Blockchain

Global health security concerns have gained vast importance in recent times with outbreak of COVID-19. Today, the growing interdependence among countries and states has effected into accelerated growth of pandemics. A global need for rugged medical systems on a common platform is deemed today. Pandemics will not stop, they will resurrect again, they will happen irrespective till such times the medical world attains a disease less world in future. But till then, we can attempt to decelerate the pandemics growth enabled with new generation technologies. Medical cyber-physical systems are marred by a number of challenges and this paper proposes a model to negate these identified challenges enabled on multichain blockchain platform that imparts peculiar blockchain characteristics to the network of effected systems. The proposed model also enables to share encrypted data on select blockchain nodes granted defined access controls with proven encryption algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Inspection Data Exchange and Visualization for Building Maintenance using AR-enabled BIM

After the COVID-19 outbreak, a new concept of building maintenance (BM) systems is needed because current approaches highly rely on physical contact between workers, engineers, and managers. It imposes health and safety risks as increasing concerns about infections and spreads. This adds burdens to take unavoidable close contact and health risks to building owners, occupants, workforce, and society at large. In this respect, a new BM system was developed that enables reliable virtual communication and reduces BM response times by filling gaps between users and building managers. The proposed system is based on a concept of a cyber-physical system (CPS) using augmented reality (AR) and building information modeling (BIM) to promote non-contact building management. In this system, AR plays an important role in inspecting and visualizing defects in the real world, and the detected defect information is stored and managed by cloud-based BIM in cyberspace. This paper focuses on data visualization and management in the CPS-based non-contact building management system. A cloud-based database and mobile application are developed for data management purposes. In addition, this paper presents BIM data exchange and visualization in AR applications. Target image-based localization and tracking in BIM are also tested. The test results showed that the model alignment and localization accuracy are reliable for building maintenance works. Using the new BM mechanism, we expect that the related workers, building owners, and occupants will experience a reliable building maintenance process based on CPS-based information exchange from both users and facility managers while maintaining social distance. © 2022 International Association on Automation and Robotics in Construction.

Design and Implementation of OPC UA-Based VR/AR Collaboration Model Using CPS Server for VR Engineering Process

In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is highly growthable. In particular, the core technologies that enable digital transformation VR (Virtual Reality)/AR (Augmented Reality) technologies have developed rapidly in recent years, but have not yet achieved any particular results in industrial engineering. In the manufacturing industry, digital threads and collaboration systems are needed to reduce design costs that change over and over again due to the inability to respond to various problems and demands that should be considered when designing products. To this end, we propose a VR/AR collaboration model that increases efficiency of manufacturing environments such as inspection and maintenance as well as design simultaneously with participants through 3D rendering virtualization of facilities or robot 3D designs in VR/AR. We implemented converting programs and middleware CPS (Cyber Physical System) servers that convert to BOM (Bill of Material)-based 3D graphics models and CPS models to test the accuracy of data and optimization of 3D modeling and study their performance through robotic arms in real factories.

Healthcare Data Security Using Lightweight Protocol for Cyber Physical System

Healthcare systems are equipped with the latest technological advancement and remotely diagnose the patients. In critical conditions, the patients need continuous monitoring by health experts, which is almost impossible in many cases—for example- in the recent COVID-19 crisis when the hospitals are full of infected people. The advanced cyber-physical system (CPS) based medical devices supplement this monitoring system. Health specialists can connect with patients remotely and receive updated health reports simultaneously using Internet-enabled CPS devices. Due to the openness of security protocols, transferring information in the CPS module is a challenging task. Securing health data, on the other hand, is critical. Existing data security techniques, such as RSA and DSA, have drawbacks;one of the most prominent drawbacks of all existing data security strategies is a lack of resources. This study proposed a lightweight data security technique for sharing information in real-time to address this problem. The proposed approach is generalized, as it will work with all categories of data and provide security to the critical information of healthcare data. Additionally, the model is tested with the cross-platform dataset of different categories like.txt, .pdf, .doc, .png, etc., and found promising outcomes. IEEE

Guest Editors' Introduction: Special Issue on 2021 Top Picks in Hardware and Embedded Security

Hardware is the foundation of many systems ranging from embedded systems, and Internet of Things devices, to cyber-physical systems. The increasing design complexity of hardware continues to challenge our ability to provide robust security guarantees, thereby undermining the security of systems and resulting in security breaches and leakage of private information. The direct and indirect costs of addressing security vulnerabilities (e.g., root cause analysis, deploying fixes and mitigations, and risk of product recalls) not only damage the reputation of a company, but also prolong time-to-market deadlines, thereby squeezing the supply chain. To this end, researchers from academia and industry have been developing tools and techniques that can help identify and mitigate security issues in hardware, thereby building a bedrock for system security. One important task toward this ambitious goal is to identify the best set of attack and defense tools and techniques in hardware and embedded security, which typically spans many communities ranging from devices to circuits to architecture to CAD to cryptography. This special issue presents the articles selected during the third edition of the workshop 'Top Picks in Hardware and Embedded Security' (shortly, Top Picks) held virtually (due to COVID-19) on November 5, 2021, 'co-located' with the IEEE/ACM International Conference on Computer- Aided Design. © 2013 IEEE.

Towards Adaptive Quality Assurance in Industrial Applications

We propose the AQUILA framework (Adaptive Quality Assurance in Industrial Applications), a concept for digitalization in Industry 4.0 to support the entire industrial manufacturing chain, laying the groundwork for adaptive quality assurance in times of disrupted supply chains and, due to the COVID-19 pandemic, restricted travel possibilities. To that end, our proposed framework allows for the definition and description of industrial processes, quality assurance and testing protocols, and training scenarios in a comprehensive notation based on BPMN, and supports users in task execution, documentation, and evaluation by providing smart glass-based HCI with eye tracking technology, delivering a combination of process documentation, context-sensitive AR visualization, gaze-based interaction schemes, and remote maintenance and assistance functionality. © 2022 IEEE.

MIST-based Tuning of Cyber-Physical Systems Towards Holistic Healthcare Informatics

The entire world seems shaken and disrupted since the strike of Covid-19 ever since its outbreak towards the end of 2019 and its continued perils. During this unprecedented event of the century, people's health emerged as the most vulnerable and affected area either directly or indirectly by the coronavirus and its new variants. Disrupting almost all spheres of life, patients' health and care systems required timely support from healthcare professionals to provide the needed medical advice on one hand and a prescriptive mechanism to avoid another impending casualty. Similarly, a proactive approach became desirable from the health ministry, pharmaceutical firms, medical insurance companies, and other stakeholders in fine-tuning their offerings to the patients as per the recommender systems. The devices to measure the vitals of a person, became more efficient and ergonomically sound with the advent of wearable gadgets. These devices monitored the physical activities of the user and transferred the vital signals wirelessly to any base computing device and cloud-based repositories. This mechanism, however, was reported to fail in addressing the issues with non-communicating or stand-alone devices that were used by the masses in developing countries including India. If the real-time data could be used from these devices, the healthcare diagnosis and analysis of a patient's medical condition could have taken a progressive dimension with the addition of missing data points. This research thus aims to fill the information gap and proposes a transforming approach towards existing non-communicating devices used to measure the vitals like blood pressure, oxygen level, blood sugar, etc. The proposed MIST-based Cyber-Physical System shall create extensive scalability towards the retrieval of the vital details from the devices which were otherwise captured offline previously and were unused at multiple critical points of healthcare processes. © 2022 IEEE.

Designing a Cyber-Physical-Systems and Human Factors Engineering Course for Industry 4.0

This research presents a novel methodology and instructional, curricular design for the Cyber-Physical Systems and Human Factors Engineering course for an Industrial Engineering program in Higher Education. The research proposal offers a Competency-Based Education Model, Challenge-based Learning, and Experiential Learning design to create a curricular adaptation to prepare the future workforce for Industry 4.0, driven by digital technologies and strengthening the structure of Education 4.0 in pandemic times. The curricular design was explored and implemented in a national Industrial Engineering virtual course in five different facilities of a Higher Education Institution. Five professors participated in the exploratory study with 265 students in four country regions. The quantitative analysis provided positive findings regarding knowledge delivery and student competency development, confirming the good practices and standards in the proposed curricular design methodology. The final student evaluation results for the course have been favorable. They emphasized the importance of developing skills and knowledge about the enablers and components of Industry 4.0, such as Cyber-Physical-Systems and machine learning. Moreover, they remarked on the importance of human factors to develop a more sustainable society. The research contributes new ideas and perspectives for professors and instructional designers to shape the future of Higher Education. Furthermore, these new research paradigms for competencies in educational innovation shape the emerging virtual and hybrid educational practices in the COVID-19 pandemic and post-pandemic era.

Using industrial 4.0 technologies to combat the COVID-19 pandemic.

The COVID 19 (Coronavirus) pandemic has led to a surge in the demand for healthcare devices, pre-cautions, or medicines along with advanced information technology. It has become a global mission to control the Coronavirus to prevent the death of innocent people. The fourth industrial revolution (I4.0) is a new approach to thinking that is proposed across a wide range of industries and services to achieve greater success and quality of life. Several initiatives associated with industry 4.0 are expected to make a difference in the fight against COVID-19. Implementing I4.0 components effectively could lead to a reduction in barriers between patients and healthcare workers and could result in improved communication between them. The present study aims to review the components of I4.0 and related tools used to combat the Coronavirus. This article highlights the benefits of each component of the I4.0, which is useful in controlling the spread of COVID-19. From the present study, it is stated that I4.0 technologies could provide an effective solution to deal with local as well as global medical crises in an innovative way.