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Christos Emmanouilidis carries over 20 years of experience from positions in Industry, Academia, Research, and Innovation supporting organisations. He has had multiple PI and Co-I roles for projects related to Industrial Informatics, Context-Aware Computing, AI & Cognitive Systems, Robotics & Automation, Data Analytics, as well as Cyber-Physical Systems and Internet of Things Technologies, resulting in over 100 publications in refereed journals and proceedings. His teaching portfolio includes Internet of Things & Industry 4.0, Data Analytics, Technology-Enabled Innovation, Information Management, and Asset Management. He is an Associate Professor at the University of Groningen, a Fellow of the UK Higher Education Academy (FHEA), Senior IEEE Member, Founding Fellow of the International Society of Engineering Asset Management (ISEAM), member of IFIP WG5.7 ‘Advances in Production Management Systems’, and member of CEN TC319 standardisation committee on Maintenance. He Chairs the IFAC TC5.1 WG A-MEST on Advanced Maintenance Engineering Services and Technologies and is Vice Chair of IFAC TC5.1 on Manufacturing Plant Control. He has had past appointments in Industry (Zenon SA Automation Technologies), and Public Research Organisations (ATHENA Research & Innovation Centre). He has served as Innovation expert for Regional Government and the EC/JRC regarding the introduction of ICT solutions in support of Smart Specialisation Strategies, and Industrial Transitions.
The growth of the Internet of Things (IoT) offers numerous opportunities for developing industrial applications such as smart grids, smart cities, smart manufacturers, etc. By utilising these opportunities, businesses engage in creating the Industrial Internet of Things (IIoT). IoT is vulnerable to hacks and, therefore, requires various techniques to achieve the level of security required. Furthermore, the wider implementation of IIoT causes an even greater security risk than its benefits. To provide a roadmap for researchers, this survey discusses the integrity of industrial IoT systems and highlights the existing security approaches for the most significant industrial applications. This paper mainly classifies the attacks and possible security solutions regarding IoT layers architecture. Consequently, each attack is connected to one or more layers of the architecture accompanied by a literature analysis on the various IoT security countermeasures. It further provides a critical analysis of the existing IoT/IIoT solutions based on different security mechanisms, including communications protocols, networking, cryptography and intrusion detection systems. Additionally, there is a discussion of the emerging tools and simulations used for testing and evaluating security mechanisms in IoT applications. Last, this survey outlines several other relevant research issues and challenges for IoT/IIoT security.
Nasr Abosata; Saba Al-Rubaye; Gokhan Inalhan; Christos Emmanouilidis. Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications. Sensors 2021, 21, 3654 .
AMA StyleNasr Abosata, Saba Al-Rubaye, Gokhan Inalhan, Christos Emmanouilidis. Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications. Sensors. 2021; 21 (11):3654.
Chicago/Turabian StyleNasr Abosata; Saba Al-Rubaye; Gokhan Inalhan; Christos Emmanouilidis. 2021. "Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications." Sensors 21, no. 11: 3654.
In times of large-scale crises, seemingly streamlined supply chains could become prone to unforeseen disruptions, leading to interruption in the provision of vital supplies. This could lead to severe consequences if such interruptions include vital products, such as lifesaving medical supplies or healthcare workers’ protective gear. Shortages of vital supplies could occur due to unexpected sharp spike in demand, where manufacturers are unable to produce the necessary quantities required to meet the unusual demand. They could also be the result of a disruption in the product’s supply chain, originating in another country, or even continent, worse affected by the crisis. In either case, localized production, enabled by efforts and resources of local establishments and individuals, could provide a contingency means to produce such vital products to serve local needs, temporarily. Motivated by the growing availability of advanced manufacturing technologies, in particular additive manufacturing (AM), this paper aims to develop a decision-making framework for the design of AM enabled local manufacturing networks in times of crises. The framework consists of complementing interrelated optimization and simulation models that operate iteratively in an uncertain environment, until a local production network, producing the desired performance targets, emerges. Finally, a case study inspired by the shortages of medical supplies, and healthcare workers’ personal protective equipment (PPE), during the worldwide 2020 outbreak of the COVID-19 coronavirus is employed to demonstrate the applicability of the framework.
Yousef Haddad; Konstantinos Salonitis; Christos Emmanouilidis. Design of emergency response manufacturing networks: a decision-making framework. Procedia CIRP 2021, 96, 151 -156.
AMA StyleYousef Haddad, Konstantinos Salonitis, Christos Emmanouilidis. Design of emergency response manufacturing networks: a decision-making framework. Procedia CIRP. 2021; 96 ():151-156.
Chicago/Turabian StyleYousef Haddad; Konstantinos Salonitis; Christos Emmanouilidis. 2021. "Design of emergency response manufacturing networks: a decision-making framework." Procedia CIRP 96, no. : 151-156.
On line change detection is a key activity in streaming analytics, which aims to determine whether the current observation in a time series marks a change point in some important characteristic of the data, given the sequence of data observed so far. It can be a challenging task when monitoring complex systems, which are generating streaming data of significant volume and velocity. While applicable to diverse problem domains, it is highly relevant to monitoring high value and critical engineering assets. This paper presents an empirical evaluation of two algorithmic approaches for streaming data change detection. These are a modified martingale and a Bayesian online detection algorithm. Results obtained with both synthetic and real world data sets are presented and relevant advantages and limitations are discussed.
Bernadin Namoano; Christos Emmanouilidis; Cristobal Ruiz-Carcel; Andrew G Starr. Change detection in streaming data analytics: A comparison of Bayesian online and martingale approaches. IFAC-PapersOnLine 2020, 53, 336 -341.
AMA StyleBernadin Namoano, Christos Emmanouilidis, Cristobal Ruiz-Carcel, Andrew G Starr. Change detection in streaming data analytics: A comparison of Bayesian online and martingale approaches. IFAC-PapersOnLine. 2020; 53 (3):336-341.
Chicago/Turabian StyleBernadin Namoano; Christos Emmanouilidis; Cristobal Ruiz-Carcel; Andrew G Starr. 2020. "Change detection in streaming data analytics: A comparison of Bayesian online and martingale approaches." IFAC-PapersOnLine 53, no. 3: 336-341.
Internet of things (IoT)-generated data from industrial systems are often collected in non-actionable form, thus not directly aiding maintenance actions. Context information management is often seen as an enabler for interoperability and context-based service adaptation, acting as a mechanism for linking data with knowledge to adaptive data and services. Ontology-based approaches for semantic maintenance have been proposed in the past as a data and service mediation mechanism and are adopted here as the starting point employed to develop a context resolution service for industrial diagnostics. The underlying ontology of the context resolution mechanism is relevant to failure analysis of mechanical components. The terminology and relationship between concepts are structured on the basis of relevant standards with a reliability-oriented knowledge grounding. A reasoning mechanism is employed to deliver context resolution and the derived context can add a metadata layer on data or events generated by automated and human-driven means. The approach is applied on a gearbox test rig appropriate for emulating complex misalignment cases met in many manufacturing and aerospace applications.
Ali Al-Shdifat; Christos Emmanouilidis; Muhammad Khan; Andrew Starr. Ontology - based context resolution in internet of things enabled diagnostics. IFAC-PapersOnLine 2020, 53, 251 -256.
AMA StyleAli Al-Shdifat, Christos Emmanouilidis, Muhammad Khan, Andrew Starr. Ontology - based context resolution in internet of things enabled diagnostics. IFAC-PapersOnLine. 2020; 53 (3):251-256.
Chicago/Turabian StyleAli Al-Shdifat; Christos Emmanouilidis; Muhammad Khan; Andrew Starr. 2020. "Ontology - based context resolution in internet of things enabled diagnostics." IFAC-PapersOnLine 53, no. 3: 251-256.
The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered, not just for the reduction of its own direct impact but also for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research acts as a primer for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose.
C. Turner; O. Okorie; C. Emmanouilidis; J. Oyekan. A Digital Maintenance Practice Framework for Circular Production of Automotive Parts. IFAC-PapersOnLine 2020, 53, 19 -24.
AMA StyleC. Turner, O. Okorie, C. Emmanouilidis, J. Oyekan. A Digital Maintenance Practice Framework for Circular Production of Automotive Parts. IFAC-PapersOnLine. 2020; 53 (3):19-24.
Chicago/Turabian StyleC. Turner; O. Okorie; C. Emmanouilidis; J. Oyekan. 2020. "A Digital Maintenance Practice Framework for Circular Production of Automotive Parts." IFAC-PapersOnLine 53, no. 3: 19-24.
Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.
Ali Al-Shdifat; Christos Emmanouilidis; Muhammad Khan; Andrew G. Starr. Ontology-Based Context Modeling in Physical Asset Integrity Management. Frontiers in Computer Science 2020, 2, 1 .
AMA StyleAli Al-Shdifat, Christos Emmanouilidis, Muhammad Khan, Andrew G. Starr. Ontology-Based Context Modeling in Physical Asset Integrity Management. Frontiers in Computer Science. 2020; 2 ():1.
Chicago/Turabian StyleAli Al-Shdifat; Christos Emmanouilidis; Muhammad Khan; Andrew G. Starr. 2020. "Ontology-Based Context Modeling in Physical Asset Integrity Management." Frontiers in Computer Science 2, no. : 1.
Food production chains have to respond to disrupted global markets and dynamic customer demands. They are coming under pressure to move from a supply to a demand-driven business model. The inherent difficulties in the lifecycle management of food products, their perishable nature, the volatility in global and regional supplier and customer markets, and the mix of objective and subjective drivers of customer demand and satisfaction, compose a challenging food production landscape. Businesses need to navigate through dynamically evolving operational risks and ensure targeted performance in terms of supply chain resilience and agility, as well as transparency and product assurance. While the industrial transition to digitalised and automated food production chains is seen as a response to such challenges, the contribution of industry 4.0 technology enablers towards this aim is not sufficiently well understood. This paper outlines the key features of high performing food production chains and performs a mapping between them and enabling technologies. As digitalisation initiatives gain priority, such mapping can help with the prioritisation of technology enablers on delivering key aspects of high performing food production chains.
Christos Emmanouilidis; Serafim Bakalis. Digital Technology Enablers for Resilient and Customer Driven Food Value Chains. Collaboration in a Hyperconnected World 2020, 649 -657.
AMA StyleChristos Emmanouilidis, Serafim Bakalis. Digital Technology Enablers for Resilient and Customer Driven Food Value Chains. Collaboration in a Hyperconnected World. 2020; ():649-657.
Chicago/Turabian StyleChristos Emmanouilidis; Serafim Bakalis. 2020. "Digital Technology Enablers for Resilient and Customer Driven Food Value Chains." Collaboration in a Hyperconnected World , no. : 649-657.
Remote monitoring services are required to meet the very high demands on availability and efficiency of industrial systems. The fast evolution of technologies associated with the deeper penetration of Internet of Things in industry creates considerable challenges for such services. These are related to the whole data lifecycle, encompassing data acquisition, real-time data processing, transmission, storage, analysis, and higher added value service provision to users, with adequate data management and governance needed to be in place. The sheer complexity of such activities the need to ground such processing on sound domain knowledge emphasises the need for context information management. The aim of this paper is to survey and analyse recent literature that addresses Internet of Things context information management, mapping how context-aware computing addresses key challenges and supports delivering appropriate monitoring solutions.
Ali Al-Shdifat; Christos Emmanouilidis; Andrew Starr. Context-Awareness in Internet of Things - Enabled Monitoring Services. Recent Advances in Computational Mechanics and Simulations 2020, 889 -896.
AMA StyleAli Al-Shdifat, Christos Emmanouilidis, Andrew Starr. Context-Awareness in Internet of Things - Enabled Monitoring Services. Recent Advances in Computational Mechanics and Simulations. 2020; ():889-896.
Chicago/Turabian StyleAli Al-Shdifat; Christos Emmanouilidis; Andrew Starr. 2020. "Context-Awareness in Internet of Things - Enabled Monitoring Services." Recent Advances in Computational Mechanics and Simulations , no. : 889-896.
The changing nature of manufacturing, in recent years, is evident in industries willingness to adopt network connected intelligent machines in their factory development plans. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by Internet of Things create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of comprehensive framework for its processing, analysis and use should be an important goal in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of a comprehensive framework for its processing analysis and use. The concept of ‘Human in the loop’ is also reinforced with the use of audit trails, allowing streamlined access to decision making and providing the ability to mine decisions.
Chris J. Turner; Christos Emmanouilidis; Tetsuo Tomiyama; Ashutosh Tiwari; Rajkumar Roy. Intelligent Decision Support for Maintenance: A New Role for Audit Trails. Recent Advances in Computational Mechanics and Simulations 2020, 396 -403.
AMA StyleChris J. Turner, Christos Emmanouilidis, Tetsuo Tomiyama, Ashutosh Tiwari, Rajkumar Roy. Intelligent Decision Support for Maintenance: A New Role for Audit Trails. Recent Advances in Computational Mechanics and Simulations. 2020; ():396-403.
Chicago/Turabian StyleChris J. Turner; Christos Emmanouilidis; Tetsuo Tomiyama; Ashutosh Tiwari; Rajkumar Roy. 2020. "Intelligent Decision Support for Maintenance: A New Role for Audit Trails." Recent Advances in Computational Mechanics and Simulations , no. : 396-403.
COVID-19 pandemic is clearly challenging the entire food system. A collective action is needed in order to build food systems that are resilient. Food systems are multidisciplinary and highly interconnected. Food security is important in times of shocks and crises. Download : Download full-size image
Serafim Bakalis; Vasilis P. Valdramidis; Dimitrios Argyropoulos; Lilia Ahrne; Jianshe Chen; P.J. Cullen; Enda Cummins; Ashim K. Datta; Christos Emmanouilidis; Tim Foster; Peter Fryer; Ourania Gouseti; Almudena Hospido; Kai Knoerzer; Alain LeBail; Alejandro G. Marangoni; Pingfan Rao; Oliver K. Schlüter; Petros Taoukis; Epameinondas Xanthakis; Jan F.M. Van Impe. Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms. Current Research in Food Science 2020, 3, 166 -172.
AMA StyleSerafim Bakalis, Vasilis P. Valdramidis, Dimitrios Argyropoulos, Lilia Ahrne, Jianshe Chen, P.J. Cullen, Enda Cummins, Ashim K. Datta, Christos Emmanouilidis, Tim Foster, Peter Fryer, Ourania Gouseti, Almudena Hospido, Kai Knoerzer, Alain LeBail, Alejandro G. Marangoni, Pingfan Rao, Oliver K. Schlüter, Petros Taoukis, Epameinondas Xanthakis, Jan F.M. Van Impe. Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms. Current Research in Food Science. 2020; 3 ():166-172.
Chicago/Turabian StyleSerafim Bakalis; Vasilis P. Valdramidis; Dimitrios Argyropoulos; Lilia Ahrne; Jianshe Chen; P.J. Cullen; Enda Cummins; Ashim K. Datta; Christos Emmanouilidis; Tim Foster; Peter Fryer; Ourania Gouseti; Almudena Hospido; Kai Knoerzer; Alain LeBail; Alejandro G. Marangoni; Pingfan Rao; Oliver K. Schlüter; Petros Taoukis; Epameinondas Xanthakis; Jan F.M. Van Impe. 2020. "Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms." Current Research in Food Science 3, no. : 166-172.
O. Mörth; Christos Emmanouilidis; N. Hafner; M. Schadler. Cyber-physical systems for performance monitoring in production intralogistics. Computers & Industrial Engineering 2020, 142, 1 .
AMA StyleO. Mörth, Christos Emmanouilidis, N. Hafner, M. Schadler. Cyber-physical systems for performance monitoring in production intralogistics. Computers & Industrial Engineering. 2020; 142 ():1.
Chicago/Turabian StyleO. Mörth; Christos Emmanouilidis; N. Hafner; M. Schadler. 2020. "Cyber-physical systems for performance monitoring in production intralogistics." Computers & Industrial Engineering 142, no. : 1.
The opportunity to shift from corrective and preventive to data-driven Predictive Maintenance has received a significant boost with the deeper penetration of Internet of Things (IoT) technologies in industrial environments. Processing IoT generated data nonetheless creates challenges for data management and actionable data processing. One way to handle such complexity is to introduce context information modelling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. In this paper, context information management is considered on the basis of a valid knowledge construct for reliability-oriented maintenance management. The aim is to produce a viable semantic organization of data for maintenance services. It is applied on an industrial case linked to maintenance of a distributed fleet of connected production grade industrial printers. The complexity of translating the data generated by such production assets to actionable information is significant, as the status of a single asset is characterised by several hundreds of failure modes and a multitude of event codes. To assess the viability of the ontology for the targeted application, a qualitative usability evaluation study of the ontology is performed.
C Emmanouilidis; M. Gregori; A. Al-Shdifat. Context Ontology Development for Connected Maintenance Services. IFAC-PapersOnLine 2020, 53, 10923 -10928.
AMA StyleC Emmanouilidis, M. Gregori, A. Al-Shdifat. Context Ontology Development for Connected Maintenance Services. IFAC-PapersOnLine. 2020; 53 (2):10923-10928.
Chicago/Turabian StyleC Emmanouilidis; M. Gregori; A. Al-Shdifat. 2020. "Context Ontology Development for Connected Maintenance Services." IFAC-PapersOnLine 53, no. 2: 10923-10928.
The concept of a reconfigurable manufacturing system (RMS) has been introduced to enable production systems to continuously evolve and respond rapidly to unpredicted and fluctuating market environments. To achieve this goal, RMS needs to exhibit six core characteristics: modularity, integrability, scalability, diagnosability, convertibility and customisation. These characteristics are required to ensure manufacturing systems’ resilience while maintaining productivity and quality. Assessing these characteristics at both the design and operating phase can be aided by the digital twinning (DT) of physical systems. To this end, the DT-RMS concept is introduced in this paper as a dynamic cyber-replica of the physical production environment, enabling a high-level of transparency about data, performance, and relevant reconfiguration decisions. As a result, DT-RMS responds to the need to integrate requirements and performance targets for the RMS characteristics at design and operating-time.
J. Tang; C. Emmanouilidis; K. Salonitis. Reconfigurable Manufacturing Systems Characteristics in Digital Twin Context. IFAC-PapersOnLine 2020, 53, 10585 -10590.
AMA StyleJ. Tang, C. Emmanouilidis, K. Salonitis. Reconfigurable Manufacturing Systems Characteristics in Digital Twin Context. IFAC-PapersOnLine. 2020; 53 (2):10585-10590.
Chicago/Turabian StyleJ. Tang; C. Emmanouilidis; K. Salonitis. 2020. "Reconfigurable Manufacturing Systems Characteristics in Digital Twin Context." IFAC-PapersOnLine 53, no. 2: 10585-10590.
The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions.
C. J. Turner; C. Emmanouilidis; T. Tomiyama; A. Tiwari; R. Roy. Intelligent decision support for maintenance: an overview and future trends. International Journal of Computer Integrated Manufacturing 2019, 32, 936 -959.
AMA StyleC. J. Turner, C. Emmanouilidis, T. Tomiyama, A. Tiwari, R. Roy. Intelligent decision support for maintenance: an overview and future trends. International Journal of Computer Integrated Manufacturing. 2019; 32 (10):936-959.
Chicago/Turabian StyleC. J. Turner; C. Emmanouilidis; T. Tomiyama; A. Tiwari; R. Roy. 2019. "Intelligent decision support for maintenance: an overview and future trends." International Journal of Computer Integrated Manufacturing 32, no. 10: 936-959.
Maintenance and repair activities from the perspective of OEMs are both considerable sources of revenue and expenses, particularly when part of a Product Service System (PSS). It is therefore necessary for an OEM that provides services bundled with products to ensure timely response without significant impact on cost. This paper proposes a make-to-order spare parts supply chain strategy through the adoption of Redistributed Manufacturing (RdM) where the supply chain is shortened and total cost is decreased. An agent-based model that portrays an OEM’s response to repair a failed equipment is developed to exhibit the potential time and cost savings gained by OEMs.
Yousef Haddad; Konstantinos Salonitis; Christos Emmanouilidis. Redistributed manufacturing of spare parts: an agent-based modelling approach. Procedia CIRP 2019, 81, 707 -712.
AMA StyleYousef Haddad, Konstantinos Salonitis, Christos Emmanouilidis. Redistributed manufacturing of spare parts: an agent-based modelling approach. Procedia CIRP. 2019; 81 ():707-712.
Chicago/Turabian StyleYousef Haddad; Konstantinos Salonitis; Christos Emmanouilidis. 2019. "Redistributed manufacturing of spare parts: an agent-based modelling approach." Procedia CIRP 81, no. : 707-712.
The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments.
Stefano Tedeschi; Christos Emmanouilidis; Jörn Mehnen; Rajkumar Roy. A Design Approach to IoT Endpoint Security for Production Machinery Monitoring. Sensors 2019, 19, 2355 .
AMA StyleStefano Tedeschi, Christos Emmanouilidis, Jörn Mehnen, Rajkumar Roy. A Design Approach to IoT Endpoint Security for Production Machinery Monitoring. Sensors. 2019; 19 (10):2355.
Chicago/Turabian StyleStefano Tedeschi; Christos Emmanouilidis; Jörn Mehnen; Rajkumar Roy. 2019. "A Design Approach to IoT Endpoint Security for Production Machinery Monitoring." Sensors 19, no. 10: 2355.
Industrial Cyber-Physical Systems have benefitted substantially from the introduction of a range of technology enablers. These include web-based and semantic computing, ubiquitous sensing, internet of things (IoT) with multi-connectivity, advanced computing architectures and digital platforms, coupled with edge or cloud side data management and analytics, and have contributed to shaping up enhanced or new data value chains in manufacturing. While parts of such data flows are increasingly automated, there is now a greater demand for more effectively integrating, rather than eliminating, human cognitive capabilities in the loop of production related processes. Human integration in Cyber-Physical environments can already be digitally supported in various ways. However, incorporating human skills and tangible knowledge requires approaches and technological solutions that facilitate the engagement of personnel within technical systems in ways that take advantage or amplify their cognitive capabilities to achieve more effective sociotechnical systems. After analysing related research, this paper introduces a novel viewpoint for enabling human in the loop engagement linked to cognitive capabilities and highlighting the role of context information management in industrial systems. Furthermore, it presents examples of technology enablers for placing the human in the loop at selected application cases relevant to production environments. Such placement benefits from the joint management of linked maintenance data and knowledge, expands the power of machine learning for asset awareness with embedded event detection, and facilitates IoT-driven analytics for product lifecycle management.
Christos Emmanouilidis; Petros Pistofidis; Luka Bertoncelj; Vassilis Katsouros; Apostolos Fournaris; Christos Koulamas; Cristobal Ruiz-Carcel. Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems. Annual Reviews in Control 2019, 47, 249 -265.
AMA StyleChristos Emmanouilidis, Petros Pistofidis, Luka Bertoncelj, Vassilis Katsouros, Apostolos Fournaris, Christos Koulamas, Cristobal Ruiz-Carcel. Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems. Annual Reviews in Control. 2019; 47 ():249-265.
Chicago/Turabian StyleChristos Emmanouilidis; Petros Pistofidis; Luka Bertoncelj; Vassilis Katsouros; Apostolos Fournaris; Christos Koulamas; Cristobal Ruiz-Carcel. 2019. "Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems." Annual Reviews in Control 47, no. : 249-265.
With global competition and technological progress, there have been growing demands by industry for more efficiency in monitoring the health status of the manufacturing equipment in real time. Remote monitoring services in the era of Industry 4.0 are nonetheless faced some challenges such as big data’s 4V challenges (volume, velocity, variety, veracity), scalability, data heterogeneity, as well as relevant to integrating data with domain knowledge. While all these pose problems in conventional monitoring, they become even more challenges when integrating IoT and cloud computing to deliver advanced services to offer infrastructure availability and ubiquitous accessibility. Although it offers many benefits and solution enablers, substantial effort is required to manage and exploit the data generated by "things". Among the key instruments to tackle such complexity is the concept of context information management. This paper proposes a conceptual context-aware framework for the integration of Internet of Things and cloud computing for remote monitoring services.
Ali Al-Shdifat; Christos Emmanouilidis. Development of a Context-aware framework for the Integration of Internet of Things and Cloud Computing for Remote Monitoring Services. Procedia Manufacturing 2018, 16, 31 -38.
AMA StyleAli Al-Shdifat, Christos Emmanouilidis. Development of a Context-aware framework for the Integration of Internet of Things and Cloud Computing for Remote Monitoring Services. Procedia Manufacturing. 2018; 16 ():31-38.
Chicago/Turabian StyleAli Al-Shdifat; Christos Emmanouilidis. 2018. "Development of a Context-aware framework for the Integration of Internet of Things and Cloud Computing for Remote Monitoring Services." Procedia Manufacturing 16, no. : 31-38.
Christos Emmanouilidis. TAKING THE LEAP: The Methods and Tools of the Linked Engineering and Manufacturing Platform (LEAP). Production Planning & Control 2018, 30, 345 -346.
AMA StyleChristos Emmanouilidis. TAKING THE LEAP: The Methods and Tools of the Linked Engineering and Manufacturing Platform (LEAP). Production Planning & Control. 2018; 30 (4):345-346.
Chicago/Turabian StyleChristos Emmanouilidis. 2018. "TAKING THE LEAP: The Methods and Tools of the Linked Engineering and Manufacturing Platform (LEAP)." Production Planning & Control 30, no. 4: 345-346.
This paper argues based on evidence from the literature that learning analytics, when undertaken by higher education institutions, is not considered within a holistic knowledge management strategy, which could provide significant improvement to the outcomes of learning analytics. Particularly, a synthesis of knowledge extraction via learning analytics and appropriate handling of such knowledge via knowledge management is not typically implemented in higher education practices, but it constitutes a promising path to improving it, and eventually contributes to improving learning services. Essentially, knowledge management can support improvements and innovation in analytics tools, translate an organisation's strategic vision into action, and enable sharing of information among different actors. These are all necessary requirements for effective learning analytics.
Abdullah Alenezi; Christos Emmanouilidis; Ahmed Al-Ashaab. Knowledge Management to Support Learning Analytics in Higher Eduction. 2018 IEEE 5th International Congress on Information Science and Technology (CiSt) 2018, 362 -367.
AMA StyleAbdullah Alenezi, Christos Emmanouilidis, Ahmed Al-Ashaab. Knowledge Management to Support Learning Analytics in Higher Eduction. 2018 IEEE 5th International Congress on Information Science and Technology (CiSt). 2018; ():362-367.
Chicago/Turabian StyleAbdullah Alenezi; Christos Emmanouilidis; Ahmed Al-Ashaab. 2018. "Knowledge Management to Support Learning Analytics in Higher Eduction." 2018 IEEE 5th International Congress on Information Science and Technology (CiSt) , no. : 362-367.