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In the rapidly evolving realm of the Industrial Internet of Things (IIoT), securing shop floor operations, especially in audit processes, is of critical importance. This paper confronts the challenge of ensuring data integrity and trust in IIoT systems by leveraging the capabilities of blockchain technology. The unique characteristics of blockchain, such as its immutable and decentralized ledger, establish a solid and transparent foundation for verifying shop floor transactions and activities. We introduce a privacy-centric approach, meticulously designed to comply with stringent data privacy regulations. This method allows auditors to authenticate both IIoT data and devices, ensuring confidentiality and adhering to regulatory standards. Our practical implementation strategy, tailored for shop floor environments, not only enhances the security of device and data integrity but also showcases robustness against specific adversarial threats, including network intrusion, data tampering, and unauthorized access. The findings indicate that our approach not only strengthens security protocols but also integrates effortlessly with existing IIoT infrastructures. It presents an efficient, scalable solution that elevates the safety and reliability of IIoT ecosystems, making it a significant step forward in the quest for secure and compliant industrial operations.

The industry is currently experiencing the fourth industrial revolution, characterized by the automation of cyber physical systems and advanced connectivity through the internet of things (IoT). This revolution enables real-time monitoring of machines status on the shop floor by leveraging cyber-physical and IoT technologies. This paper describes the results of research that develops IoT and cloud-based visualization for a machining manufacturing system shop floor. Our proposed solution involves an internet of things device equipped with two current sensors to detect machine and spindle current. The sensor connected to an Arduino Nano, which is then connected to Wemos D1 for wireless transmission of data to the cloud. The cloud has been developed to store data and provide visualization applications, in the form of machines layout map to monitor machines conditions in the form of machines ON, machines OFF, spindles ON and spindles OFF in real time. [ABSTRACT FROM AUTHOR]

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Augmented Reality (AR) is a pillar of the transition to Industry 4.0 and smart manufacturing. It can facilitate training, maintenance, assembly, quality control, remote collaboration and other tasks. AR has the potential to revolutionize the way information is accessed, used and exchanged, extending user's perception and improving their performance. This work proposes a Pervasive AR tool, created with partners from the industry sector, to support the training of logistics operators on industrial shop floors. A Human-Centered Design (HCD) methodology was used to identify operators difficulties, challenges, and define requirements. After initial meetings with stakeholders, two distinct methods were considered to configure and visualize AR content on the shop floor: Head-Mounted Display (HMD) and Handheld Device (HHD). A first (preliminary) user study with 26 participants was conducted to collect qualitative data regarding the use of AR in logistics, from individuals with different levels of expertise. The feedback obtained was used to improve the proposed AR application. A second user study was realized, in which 10 participants used different conditions to fulfill distinct logistics tasks: C1 — paper; C2 — HMD; C3 — HHD. Results emphasize the potential of Pervasive AR in the operators' workspace, in particular for training of operators not familiar with the tasks. Condition C2 was preferred by all participants and considered more useful and efficient in supporting the operators activities on the shop floor. [ABSTRACT FROM AUTHOR]

We examine the effect of plant managers on productivity using unique matched manager-plant panel data on U.S. auto-assembly plants during 1993–2007. Our econometric approach is two-pronged. Our first approach relies on using the panel nature of our data to measure variation in productivity due to managerial influence. We estimate the interquartile range of the effect of individual plant managers on average hours-per-vehicle to be about 30%. Further, we find that plant managers' experience with the models that are in production ameliorates the negative impact of new model introductions on productivity. We also observe evidence that managers' plant-specific tenure has a positive impact on productivity. In our second approach, we use high-frequency time-series data, along with structural-break tests and machine-learning methodologies, to predict variation in production using plant-manager switches. We find that a plant manager's identity is predictive of changes in both the mean and variance of production, further highlighting their channels of managerial influence. These findings are robust to narrowing the sample to focus on retirements as an exogenous source of managerial switches. This paper was accepted by Serguei Netessine, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.4427. [ABSTRACT FROM AUTHOR]

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors.

Offsite construction (OSC) is an innovative approach where building components (e.g., panels or modules) are manufactured in a shop floor environment, then transported to, and installed at the site. Although there are numerous benefits inherent to the OSC approach, practitioners still struggle to provide tailored projects to their clients due to the low level of flexibility in production caused by uncertainty, multiple projects, and variable market demands. Indeed, the lack of production flexibility limits shop floors to manufacture projects efficiently in an ever-changing environment, especially when processes are still labor-intensive and are not leveraged by autonomous systems, such as a digital twin (DT). Hence, this paper proposes the use of a DT to improve production on OSC shop floors by increasing flexibility, i.e., the ability to adapt to uncertainty, through the automated reassignment of multiskilled workers based on data pertaining to production status that are updated in near real-time. The present study presents key metrics adopting a lean thinking approach for waste identification that quantifies the improved production performance attributable to the proposed DT. Using simulation as a surrogate system, this research evaluates the production performance on the shop floor according to different simulated scenarios varying the number of interventions made by the DT and multiskilling configurations. Moreover, this research considers significant aspects of multiskilling such as reduced productivity, increased cost, and the time spent when moving between workstations during reassignment. The primary findings from the system's practical application indicate a significant improvement in production due to the reduction of waiting waste, total production duration, and total production cost being reduced by 62%, 40%, and 25%, respectively. Finally, the present study presents a novel approach to increasing flexibility on shop floors while also demonstrating the benefits attributable to the use of a DT to manage multiskilled workers in OSC. [ABSTRACT FROM AUTHOR]

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors. [ABSTRACT FROM AUTHOR]

Motivated by the increasing demand and highly customized products, accurate and up-to-date information about the manufacturing process become essential to meet these requirements. In manual assembly activities, performing theoretical planning in simulation environments is a crucial procedure to detect and avoid unreasonable assembly operations. However, the deviations between theoretical and actual assembly actions would result in the failure of the manual assembly planning. Therefore, the verification for the manual assembly planning is significant to ensure the correctness of the actual assembly operations, performing a convergence between the cyber and physical world. The challenges involved in retrieving and utilizing the actual data about the manual activities on a shop floor. In this paper, a self-contained wearable tracking system is proposed and applied to collect the shop-floor data during the manual assembly operations. And then, an unsupervised classification method is applied to empower semantic knowledge to the shop-floor data derived from the workplace. Thus, an automatic spatial–temporal verification for manual assembly planning is carried out, providing indicators to optimize the current manual assembly planning. Experimental results illustrate that the proposed work can perform the spatial–temporal verification for manual assembly task and indicate evidence to improve the manual assembly planning objectively. [ABSTRACT FROM AUTHOR]

The development of simulation models for improving performances is trendy in manufacturing industries. This paper presents simulation model for evaluating and improving performances of a utensils manufacturing plant. Internal benchmarking is employed for comparison under exactly similar conditions. The data of all input variables is collected and statistical models for each process is developed using Arena input analyzer. The simulation model is then developed using SIMIO to optimize throughput, work-in-process, and cycle time. The experiments have been performed under various scenarios, i.e., at different values of the input parameters. The areas for improvement have been highlighted on the basis of results. Furthermore, best and worst case scenarios have been discussed in detail to provide insights for managerial performance improvements by (1) optimizing inter-arrival time, (2) increasing production rate, and (3) reducing the number of rejected parts without disturbing the existing setup of the manufacturing facility. [ABSTRACT FROM AUTHOR]

In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.

The evaluation of process improvements measures in offsite construction shop floors often relies on experts' opinion, with limited use of empirical data gathered by sensors in real-time. To address this issue, there is a need for methods that integrate expert's tacit knowledge with robust data analysis techniques. This paper describes the application of exploratory data analysis techniques to evaluate improvement suggestions proposed by expert's, supported by data collected by sensors on the shop floor and building information models. The presented method involves a quantitative and qualitative digitalization-based approach where improvement suggestions are modelled and validated though machine learning algorithms and hypothesis testing. The contribution of this study is a method that combines real-time data, building information models, and knowledge modeling from experts to evaluate process improvement on offsite construction shop floors. • A method to assess improvements based on experts input and real-time data. • Machine learning is applied to analyze data from RFID sensors and BIM models. • The automation in workstations is rated based on production balance and efficiency. • Strategies to increase production flexibility are rated using statistical analysis. [ABSTRACT FROM AUTHOR]

Discrete manufacturing faces challenges to improve quality and efficiency, reduce labour and costs, as well as the shifting market from customized manufacturing to short-series production, just to name a few. CPS which combines physical world and cyber world is a key technology of coping these challenges particularly in the shop floor level. However, due to its complexities and heterogeneities, CPS development and implementation in discrete manufacturing field is still in rudimentary phase which requires feasible methods to evaluating the maturity of current system. This paper proposes an evolution process of CPS in discrete manufacturing which can be characterized by two-dimensional perspectives concerning MT (manufacturing technology) and ICT (information and communication technology) elements. A maturity model (CPSMM) is presented to conceptualize the maturity of CPS implementation process in discrete manufacturing shop floor. The importance weights of indicators in the maturity model are acquired based on AHP with group decision. A semantic grading approach is proposed to guide and constrain accessing process which allows using triangle fuzzy numbers. Procedures and algorithms on calculating CPS-AHP are given and applied in a mechanical shop floor for illustration. [ABSTRACT FROM AUTHOR]

The introduction of innovative digital tools for supporting manufacturing processes has far-reaching effects at an organizational and individual level due to the development of Industry 4.0. The FACTS4WORKERS project funded by H2020, i.e., Worker-Centric Workplaces in Smart Factories, aims to develop user-centered assistance systems in order to demonstrate their impact and applicability at the shop floor. To achieve this, understanding how to develop such tools is as important as assessing if advantages can be derived from the ICT system created. This study introduces the technology of a workplace solution linked to the industrial challenge of self-learning manufacturing workplaces. Subsequently, a two-step approach to evaluate the presented system is discussed, consisting of the one used in FACTS4WORKERS and the one used in the “Heuristics for Industry 4.0„ project. Both approaches and the use case are introduced as a base for presenting the comparison of the results collected in this paper. The comparison of the results for the presented use case is extended with the results for the rest of the FACTS4WORKERS use cases and with future work in the framework.

The shop floor has always been an important application object for the digital twin. It is well known that production, process, and product are the core business of the shop floor. Therefore, the digital twin shop floor covers multi-dimensional information and multi-scale application scenarios. In this paper, the digital twin shop floor is constructed according to the modeling method of the complex digital twin proposed in Part I. The digital twin shop floor is firstly divided into several simple digital twins that focus on scenarios of different scales. Two simple application scenarios are constructed, including tool wear prediction and spindle temperature prediction. Main functions in different application scenarios, such as data acquisition, data processing, and data visualization, are implemented and encapsulated as components to construct simple digital twins. Secondly, ontology models, knowledge graphs, and message queues are used to assemble these simple digital twins into the complex digital twin shop floor. And two complex application scenarios are constructed, including machining geometry simulation considering spindle temperature and production scheduling considering tool wear. The implementation of the complex digital twin shop floor demonstrates the feasibility of the proposed modeling method. [ABSTRACT FROM AUTHOR]

Large scale emergence of mature cloud solutions, ranging from software-as-a-service based solutions for business management, to very sophisticate private cloud solutions; offer the building blocks for constructing extremely flexible enterprise systems that can respond to environmental changes with great agility. Manufacturing enterprises need to adopt these new technologies to advance in a new era of mass customization where flexibility, scalability and agility are the differentiating factors. In this context, this paper introduces the virtualized MES and shop floor architecture as an intermediate layer in the manufacturing stack and discusses the advantages offered by this approach for manufacturing enterprises. A classification of MES and shop floor devices is presented focusing on the virtualization techniques suitable for each device type, considering the level of distributed intelligence and the virtualization overhead. Shop floor virtualization through shop floor profiles is presented and discussed underlying the flexibility of the solution. A pilot multi-agent implementation for virtual shop floor configuration based on the CoBASA reference architecture is presented and discussed. The shop floor profiles which define the virtual layout and mappings of the robotized manufacturing system are also provided in this context. The pilot implementation using six Adapt robots and a IBM CloudBurst 2.1 private cloud, is described and virtualization overhead in terms of event propagation delays is measured and presented in several scenarios of resource workload collocation on physical cloud blades [ABSTRACT FROM AUTHOR]

Paper deals with new trends regarding implementation of Information systems as one of the most important parts of manufacturing infrastructure. The main attention is paid to Manufacturing Execution Systems and Shop Floor integration regarded from the viewpoint of their implementation, manufacturing functions and integration.

A evolução do chão-de-fábrica tem sido significativa nas últimas décadas, quando grandes investimentos têm sido realizados em infra-estrutura, automação, treinamento e sistemas de informação, transformando-o numa área estratégica para as empresas. O chão-de-fábrica gera hoje grande quantidade de dados que, por estarem dispersos ou desorganizados, não são utilizados em todo o seu potencial como fonte de informação. Com vistas nessa deficiência, este trabalho propõe a implantação de um sistema de Business Intelligence por meio do uso de ferramentas de Data Warehouse e OLAP (On-Line Analytical Processing), aplicadas especificamente ao chão-de-fábrica. O objetivo é desenvolver um sistema que utilize os dados resultantes do processo produtivo e os transforme em informações que auxiliem o gerente na tomada de decisões, de forma a garantir a competitividade da empresa. Um protótipo foi construído com dados simulados para testar a proposta.Significant advances have been made on the shop floor in recent decades, involving substantial investments in infrastructure, automation, training and information systems which have transformed the shop floor into a strategic area for companies. Today, the shop floor generates large quantities of data; however, because they are dispersed or disorganized, these data are not used to the best possible advantage as a source of valuable information. In view of this deficiency, this paper proposes the implementation of a Business Intelligence system, using Data Warehouse and OLAP tools specifically applied to the shop floor. Our objective is to develop a system that uses production data, transforming them into information to aid managers in decision-making, thereby enhancing the company’s competitiveness. A prototype of the system was built with simulated data to test the viability of this proposal.

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice. [ABSTRACT FROM AUTHOR]

Existing and evolving trends and paradigms in manufacturing, such as mass customisation and personalisation, call for better communication among product-production design and customisation and production execution. Specifically, the lack of feedback from and to the shopfloor can lead to lower product quality and increased production times. In state-of-the-art industrial practices, the most common visual interface devices for communication comprise control unit terminals, TFT monitors mounted over work stations, as well as the growing trend of mobile PCs and tablets. New technologies, such as Augmented Reality (AR), have also been considered in academic research for process simulation and operator guidance and training. This paper proposes a novel use of AR goggles, coupled with other mobile devices for the communication of people, working on the shopfloor and in the engineering offices. The proposed methodology tries to address the challenges, related to the use of both technologies (and their respective interfaces) by presenting an integrated approach: the use of a mobile device as an input device and as a fiducial marker for the positioning of a virtual screen in front of the user. After the presentation of the concept, its advantages and disadvantages, compared with current practices as well with the rest of the relevant academic work, are presented. The technical implementation to be realised, including specific software frameworks that will be used is also described. Special attention is given to the data models that will support the implementation of this approach as well as to show how they can be integrated into the existing systems and practices. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to explore the specific practices of management in the dispatching unit and to identify mechanisms for supporting transfer of shop floor knowledge embedded in operating manufacturing equipment. Design/methodology/approach -- The paper applies an inductive and a case study approach for exploring three empirical cases that represent different manufacturing facility relocation processes and differences in the applied managerial practices. Findings -- The paper identifies two important gaps in international production literature when firms relocate equipment to other sites; a time gap (from dismantling to re-assembly of production facilities) and a space gap (from the current to the new site abroad). These gaps are important for understanding why relocation processes are difficult and what management can do to facilitate such processes. Practical implications -- The paper identifies four issues that management faces in the dispatching context when relocating manufacturing facilities. Originality/value -- The paper gives new insights to a limited literature of shop floor knowledge transfer when relocating manufacturing facilities. [ABSTRACT FROM AUTHOR]

Abstract: Agility is a critical success factor for manufacturers in today's volatile global environment and requires employees monitoring their performance and reacting quickly to turbulences. Thus, comprehensive information provisioning on all hierarchy levels is neces- sary. Yet, existing IT systems, e. g., Manufacturing Execution Systems, scarcely address information needs of workers on the shop floor level. This causes uncoordinated waiting times, inflexibility and costly communication. To address these issues, we present the Operational Process Dashboard for Manufacturing (OPDM), a mobile dashboard for shop floor workers. We identify process- oriented information needs, develop technical dashboard services and define IT requirements for an implementation. [Copyright &y& Elsevier]

In order to react to volatile market demand and uncertain production objectives, distributed manufacturing resources have to be dynamically configured. However, the dynamics of process planning and shop floor information makes shop floor reconfiguration a challenging problem. The shortcomings of the existing research on shop floor reconfiguration are first discussed. Then a multi-agent-based framework for dynamic shop floor reconfiguration is presented, and a mathematical programming model taking process planning into consideration is constructed as well. To coordinate the resource assignment among agents, a cooperative coevolutionary algorithm is also put forward to find the optimal solution of the reconfiguration model. The advantages of the proposed model are using combined multi-agent and mathematical programming method to decompose and optimize the problem of reconfiguration and considering alternative process plans. Furthermore, a prototype system for dynamic shop reconfiguration is developed. The results of this research will help solve the problem of shop floor reconfiguration with complex and dynamic interactive structure. [ABSTRACT FROM AUTHOR]

Abstract: Today''s highly competitive market places a great emphasis on improving efficiency and reducing costs. Simulation modelling is being widely used as a successful tool to design and analyze manufacturing systems. Enhancing the product design is an important issue. In addition, generating efficient simulation models with shorter lead times has become equally important to winning customer satisfaction. To meet these criteria, a new hybrid modelling approach for generating a simulation model and virtual environment from a process plan is presented. The proposed approach integrates simulation and virtual reality in order to render a dynamic shop floor model automatically using the basic process configurations. This is accomplished using an input database. The model interface is user-friendly so that the new users of simulation and administration can use the model easily once the input database requirement is fulfilled. Moreover, the model allows the user to trace the performance criteria of any process on the shop floor level at any instant as well as to experiment any production scenario. The virtual environment is used to verify through visualization the simulation model and also to provide a better understanding of the activities of the shop floor. [Copyright &y& Elsevier]

Concentrating mostly on France, this paper considers the changing interests and the renewal of academic alliances in the sociology of work and labor over the last 50 years. Three periods result from the combined dynamics of sociology's internal agenda and societal changes: (1) analysis of blue-collar labor as the downgrading process of complete work in taylorist industry; (2) sociology of labor markets and employment, and analysis of the bargaining processes of rules, identities and the value of work; (3) reconciliation of work and labor in distributed and flexible organizations, based on mobile and involved workers. [ABSTRACT FROM AUTHOR]

The digital twin is currently recognized as a key technology allowing the digital representation of a real-world system. In smart manufacturing, the digital twin enables the management and analysis of physical and digital processes, products, and people in order to foster the sustainability of their lifecycles. Although past research addressed this topic, fragmented studies, a lack of a holistic view, and a lack of in-depth knowledge about digital twin concepts and structures are still evident in the domain of the shop floor digital twin. Manufacturing companies need an integrated reference framework that fits the main components of both physical and digital space. On the basis of a systematic literature review, this research aims to investigate the characteristics of the digital twin for shop floor purposes in the context of smart manufacturing. The "hexadimensional shop floor digital twin" (HexaSFDT) is proposed as a comprehensive framework that integrates all the main components and describes their relationships. In this way, manufacturing organizations can rely on an inclusive framework for supporting their journey in understanding the shop floor digital twin from a methodological and technological viewpoint. Furthermore, the research strengthens the reference literature by collecting and integrating relevant contributions in a unique framework. [ABSTRACT FROM AUTHOR]

Two basic measures, model complexity and model construction efficiency, are usually used to evaluate the implementability of a methodology for modeling the control of a discrete event manufacturing system (DEMS) on the shop floor. Many well-recognized methods are used to represent and analyze the dynamics of DEMs, but not many relevant applications have been found in developing control software for the shop floor due to their shortcomings in satisfying these two measures. The paper explores a methodology for modeling the control of a DEMS, which leads to ease of control software development, rather than a new representational/analytical tool, by significantly reducing the model complexity (in terms of the number of required control states) and improving the model construction efficiency. First, an extended finite machine, called a deterministic finite capacity machine (DFCM) with parallel computing capability is developed. Based on DFCMs, the complexity growth function of a DEMS control model is linear in the number of synthesized control components. Then, an automaton structure of a DFCM control model, called structured adaptive supervisory control (SASC), is developed. By referring to supervisory control theory, an SASC model is created with three function layers: acceptance, adaptive supervision, and execution. The well-defined structure ensures that the control model can be constructed systematically [ABSTRACT FROM PUBLISHER]

In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case. [ABSTRACT FROM AUTHOR]