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Development of a Context-aware framework for the Integration of Internet of Things and Cloud Computing for Remote Monitoring Services
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7th International Conference on Through-life Engineering Services 7th International Conference on Through-life Engineering Services
Development of a Context-aware framework for the Integration of Development of a Context-aware framework the Integration of Internet of Things and Cloud Computing for for Remote Monitoring Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Internet of Things and Cloud (Pontevedra), Computing for Remote Monitoring 2017, VigoServices Spain Services Ali Al-Shdifat a*, Christos Emmanouilidis a Costing models for capacity optimization in Industry 4.0: Trade-off a a AliCranfield Al-Shdifat Christos Emmanouilidis University,*, Cranfield, Bedfordshire, MK43 0AL, UK. between used capacity and operational efficiency a a
Abstract
Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK.
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
Abstract a With global competition and technological progress, there4800-058 have been growing demands by industry for more efficiency in University of Minho, Guimarães, Portugal b monitoring the health status of the manufacturing equipment in realChapecó, time. Remote monitoring services in the era of Industry 4.0 are Unochapecó, 89809-000 SC, Brazil With global faced competition and technological been growing industry for more efficiencydata in nonetheless some challenges such as progress, big data’sthere 4V have challenges (volume, demands velocity, by variety, veracity), scalability, monitoring the as health of the manufacturing in real time. Remote monitoring services the era of in Industry 4.0 are heterogeneity, wellstatus as relevant to integrating equipment data with domain knowledge. While all these poseinproblems conventional nonetheless facedbecome some challenges such as bigwhen data’s 4V challenges velocity,tovariety, scalability, data monitoring, they even more challenges integrating IoT and(volume, cloud computing deliver veracity), advanced services to offer heterogeneity, as well as relevant to integrating data with domain knowledge. While all these pose problems in conventional Abstract infrastructure availability and ubiquitous accessibility. Although it offers many benefits and solution enablers, substantial effort is monitoring, they become even the more challenges integrating IoT the andkey cloud computing to deliver services to offer required to manage and exploit data generatedwhen by "things". Among instruments to tackle suchadvanced complexity is the concept infrastructure availability and ubiquitous accessibility. Although it offers many benefits and solution enablers, substantial effort is Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, of context information management. This paper proposes a conceptual context-aware framework for the integration of Internet of required to manage and exploit the data generated by "things". Among the key instruments to tackle such complexity is the concept information based on a realfortime basis and, necessarily, Things and cloud computing remote monitoring services. much more efficient. In this context, capacity optimization of context information management. This paper proposes a conceptual context-aware framework for the integration of Internet of goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Things computing for by remote monitoring © 2018and Thecloud Authors. Published Elsevier B.V. services.
Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of This is an open access articleofunder the CCoptimization BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier B.V. maximization. The study capacity and costing models is an important research topic that deserves © 2018 The Authors. Published byofElsevier B.V. committee Peer-review under responsibility theCC scientific of the 7th This International Conference Through-life Engineering This is an open access article under the BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) contributions from both the practical and theoretical perspectives. paper presents andon discusses a mathematical This is an open access article under thescientific CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Services. Peer-review under responsibility of the committee of the 7th International Conference on Through-life Engineering Services. model for capacity management on different costing models (ABC and TDABC). modelEngineering has been Peer-review under responsibility of based the scientific committee of the 7th International Conference A ongeneric Through-life developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s Services. Keywords: Internet of Things, Context Management, Cloud Computing, Remote Monitoring Services. value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Keywords: Internet of Things, Context Management, Cloud Computing, Remote Monitoring Services. optimization might hide operational inefficiency. © 2017 The Authors. Published by Elsevier B.V. 1. Introduction Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 1. Introduction 2017.
Recent developments in the fourth industrial revolution have led to a renewed interest in the Internet of Things (IoT) for remote monitoring services in order to meetIdle theCapacity; very high demands on availability and efficiency of industrial Keywords: Models; ABC; TDABC; Capacity Management; RecentCost developments in the fourth industrial revolution haveOperational led to a Efficiency renewed interest in the Internet of Things systems [1]. The IoT has evolved widely in five stages as shown in Fig. 1. Prior to the emergence of IoT, networking (IoT) for remote monitoring services in order to meet the very high demands on availability and efficiency of industrial focused on connecting few computers systems together, moving later to scale this up by creating the World Wide systems [1]. The IoT has evolved widely in five stages as shown in Fig. 1. Prior to the emergence of IoT, networking Web. Later, mobile devices and people were connected to the internet via mobile and social networks. Eventually, the 1. Introduction focused on connecting few computers systems together, moving later to scale this up by creating the World Wide IoT extended the internet connectivity to internetwork a wide range of physical entities [2]. The IoT enables to bring Web. Later, mobile devices and people were connected to the internet via mobile and social networks. Eventually, the cost of the idleinternet capacityconnectivity is a fundamental information companies and theirentities management of extreme importance IoTThe extended to internetwork a for wide range of physical [2]. The IoT enables to bring 2351-9789 2018 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured This is an open accesstons articleofunder the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789 ©under 2018responsibility The Authors. of Published by Elsevier B.V.of the 7th International Conference on Through-life Engineering Services. Peer-review the761; scientific committee * Paulo Tel.: article +351 253 510 +351 253license 604 741 This is anAfonso. open access under the CCfax: BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mail address: [email protected] Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services.
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services. 10.1016/j.promfg.2018.10.155
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the physical world to the digital world, where physical things share information, coordinate decisions and upgrade their functions from being traditional to becoming intelligent [3]. IoT technologies are increasingly employed in a wide range of application domains. One promising application area for IoT is that of remote monitoring services. It allows to connect a monitoring system with an end user or another system and establishes a remote overview of the observed system’s state in order to prevent machinery performance degradation, reduce maintenance costs, improve machine availability, as well as to enhance process quality [4].
Fig. 1. The Internet of Things Evolution The deeper penetration of IoT in industry creates significant opportunities but also introduces challenges for monitoring service. 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. According to Cisco’s report [5], the number of Internet-connected devices is expected to reach 500 billion by 2030. These connected devices are expected to generate a large amount of data and communicate over a network, which helps drive more informed decisions and actions. Therefore, using data of significant scale for monitoring can be challenging for several reasons, for instance, processing time, data type, power, data size and storage. The sheer complexity of such activities creates a need to narrow down the scope of processing and ground it, if possible, on a sound domain. This is exactly where context information management can contribute to. It plays a central role in determining what data needs to be collected and how to process it. It also identifies what information and services are required to be presented to the consumer. In this regard, many technologies can be a significant part of that, especially by making the connected devices work together. Cloud computing is particularly relevant, enabling the delivery of hosted services for instance storage, servers, analytics, software development platforms over the internet, and processing of massive amount of data produced in IoT. This paper offers a new concept, empowered by three enablers namely, context lifecycle management, IoT, and cloud computing for remote monitoring services. Moreover, it provides a conceptual framework that would serve industrial environments as a stepping stone for more efficiency in monitoring the health status of production machinery or physical engineering assets in real time. The rest of this paper is organised as follows: Section 2 provides background and basic concepts for these enablers. Section 3 deals with a context-aware IoT and cloud computing framework for remote monitoring services. The analysis results in identifying some key challenges to be addressed by further research in the field, as summarised in conclusion. 2. Background and basic concepts 2.1. Internet of Things Nowadays, the transformation of society demands towards technological and services is a strong stimulus for improvements in industrial processes. This is further fueled by the accelerating shift to service-based business models,
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wherein service level agreements must be ascertained, supported by adequate monitoring systems. IoT brings together many functionalities, such as identification, sensing, communication, computation, services, and semantic information management, as shown in Fig. [3]. • Identification deals to defining and matching services as well as assigning a clear identity for each thing over the network [6]. • Sensing is for extracting data/information from physical entities [7]. • Communication technologies enable the interconnectivity of things and sharing of data between things or between things and the cloud [8]. • Computation: There are many computational parts of the IoT are utilized to provide IoT functionalities. Among these parts are processing units, software platforms, cloud platforms, and software applications represent the “brain” and the computational ability of the IoT. • Services: IoT services can be categorized under multiple classes. These classes are collaborative-aware services, identity-related services, ubiquitous services, and information aggregation services. • Semantic information processing offers solutions for managing the complex and often heterogeneous nature of data and knowledge from different entities, subsystems, or users.
Fig. 2. The Internet of Things Functionalities adapted from [3] The following conclusions can be drawn from this section is that with the deeper penetration of IoT technologies in monitoring tasks, the need for context information management increasingly manifests itself as a requirement for industrial applications. Context gathering, modelling, reasoning and dissemination are needed for the efficient handling of vast amounts of data, produced by numerous devices, and their efficient integration in enterprise systems. The following part of this paper moves on to describe in greater detail of opportunities to cloud computing which provide the resources needed to store and analyze the vast amount of data generated in IoT for remote monitoring services. 2.2. Cloud Computing Cloud computing is a significant shift from the conventional way companies think about IT resources that can be utilised for remote monitoring services. It brings together many benefits such as reliability, global scale, performance, speed, and cost. The term cloud computing is used here to refer to a model for allowing users to access all their applications and services from anywhere, anytime, on the Internet, because the information is stored on the server servers provided for cloud computing services and not on the user's devices. There are several types of services models
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depending on the kind of resources delivered via them as illustrates in Fig. 3. Services, platforms, and infrastructure can be quickly provided and deployed with less administrative effort or service provider interaction [9].
Fig. 3. Types of cloud services and top benefits Cloud computing affords a suitable means for accessing real-time maintenance data anywhere in the world accessible via simple devices for example smartphones, tablets or PC. Cloud computing architecture can be divided into three categories: Firstly, IaaS which provides infrastructure services and capabilities to deploy and run software in general, for instance, physical computers, virtual machines, networks, storage devices or a combination of these devices. Secondly, PaaS plays a vital role in providing capabilities for application development and deployment in Cloud. Finally, SaaS provides the necessary applications and infrastructure services from the service provider such as Application Program Interface (API). Nowadays, there are several IoT cloud platforms available that provide several operational benefits to industries especially the IoT. Among these platforms are Google Cloud Platform, IBM Watson, Microsoft Azure Cloud, and Amazon Web Services. These platforms provide many benefits such as an option to deploy IoT applications in order to evaluate, monitor, and analyse performance in cloud manufacturing systems. Moreover, they can be used to publish data to a back-end message broker and also to receive control messages from other devices or IoT applications. 2.3. Internet of things and Cloud Computing for Remote monitoring services Condition monitoring services are needed in a wide range of fields. Condition monitoring refers to the process of acquiring and processing of information and data that express the state of a machine over time, in order to provide meaningful real-time information to help reduce risk and lower failure [10]. On the other hand, A remote monitoring program which usually consists of four steps can remotely access asset condition data and enable effective, efficient predictive maintenance[11]. Data acquisition is the process of gathering valuable data from things and converting data to the digital data. Data Processing is the process of producing meaningful information by converting digital data into real quantities of working machine conditions. • Decision-making not only focuses on the nature of the machine failure but further to detect and identify a machine fault. When failure happens, the suitable actions could be considered automatically to control the operation status of machines. • Remote communication employed to transmit information, for example, machine’s operation status and alarm conditions, over the network. Aazam et al.[12] mention that because of the amount of data generated by the IoT, it has become increasingly essential to combine it with another technology such as cloud computing, to cover the storage capacity needed and demand of virtual resources utilisation. Cloud computing, one of the latest developments that can be used in remote • •
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monitoring services, allows data shared from one location, to be accessible to the end user devices over a network and the Internet [13]. Challenges such as monitoring the proper functioning of the equipment, costly breakdown and repairs and ensuring that equipment is operating at maximum efficiency and uptime all could be easier to overcome by the integration of IoT and cloud computing. Moreover, they could reduce interruptions, improve uptime, and remotely identify faults more quickly and decrease time to repair, by providing real-time data on the performance of assets which enhances their value to the business. Although many researchers presented several solutions that focus on remote monitoring in the Cloud, these solutions are used in specific scenarios but are difficult to apply in other situations. Furthermore, IoT – driven and cloud-supported monitoring services are nonetheless faced with some of the typical challenges of big data. To this end, it is necessary to introduce context awareness into this research to present contextual information about the involved objects and to determine what information and services are required to be offered to the consumers, systems, or software. Moreover, the context has a significant role in enabling provision of adequate services to the users based on their surrounding environments. In addition, the context can help IoT services to adapt with the dynamic environment changes and making the right decision. 2.4. Context Awareness There is a growing body of literature that recognises the important role of context-awareness in deploying IoT solutions in complex industrial environments. Previous studies mostly defined context to give relevant information and services to the consumer, where relevance depends on the consumer's task [14]. IoT has expanded the range of applications with substantial needs for context management, and this was reflected in the focus of relevant surveys. Nonetheless, while substantial research efforts have been devoted to context lifecycle management in web-based, mobile, and ubiquitous computing, including IoT-enabled computing, little attention has been given to translate these advances to tangible progress in remote monitoring services. Among the early surveys, Chen and Kotz [15] analysed context-aware systems regarding the types of context they use, and the best way on how to leverage contextual information, suggesting that context awareness will play an essential factor for new applications in computing everywhere. Perttunen et al. [16] have studied the popular context reasoning and representation in the area of pervasive and context-aware computing. They found that the field of context-aware computing still lacks a major breakthrough – although using expressive ontologies is suggested in some papers, the evidence does not yet show that these systems would meet all requirements. In contrast, Bettini et al. [17] also included a comprehensive survey of context reasoning and modelling by focusing on techniques rather than projects. These techniques can increase their maintainability and evolvability and decrease the complexity of contextaware applications. Another study was conducted in the same year by Saeed and Waheed [18] focusing on the architectures of context-aware based on many parameters including adaptability, interoperability, fault tolerance, architectural style and discoverability. In 2018, Sezer et al. [19] focusing on the role of a context-aware computing system in IoT. They have argued that context awareness is becoming a necessary part of the IoT solutions not just to understand changes in the environment but also to afford an opportunity to act and respond accordingly. However, while considerable research efforts have been devoted to managing the context lifecycle in different research areas, little awareness has been given to translate these advances to tangible progress in remote monitoring services. Therefore, further research is required to develop context-aware approaches and architectures to deliver more efficient IoT-enabled monitoring services. In order to do so, an outline of how the lifecycle of context information can be managed is first introduced as shown in Fig. 4. Context lifecycle refers to how data is gathered, modelled, processed, and how knowledge is deduced from the obtained data. The context lifecycle management generally consists of four steps as shown in Fig. 4.
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Fig. 4. A context lifecycle management Context lifecycle refers to how data is gathered, modelled, processed, and how knowledge is deduced from the obtained data [19]. The context lifecycle management generally consists of four steps, namely context acquisition, modelling, reasoning, and dissemination [20]. However, more detailed handling and analysis of what these steps actually involve when considering monitoring services are largely missing in the relevant literature. Figure 5 offers an illustration of the different stages of context information management, placing them against the monitoring services functionality. 3. A context-aware IoT and cloud computing framework for remote monitoring services Although IoT represents the amalgamation of many more technologies and offers numerous benefits, it requires a tremendous amount of effort to preserve and takes advantage of the big amount of information that will be generated via objects. In this regard, cloud computing is expected to represent an enormous role in offering the resources to store, communicate, monitor, and analysis of the vast amount of data produced in IoT. Furthermore, based on the literature review, there is evidence that there is a need for an obviously defined view of the methods and technologies involved in remote monitoring services. Therefore, it is clear that the combination of the IoT and cloud computing, as well as context awareness for remote monitoring services, is vital and important. In that regard, Fig. 5 illustrates a context-aware framework for the amalgamation of IoT and cloud computing for remote monitoring services. From a context lifecycle management perspective, the first stage is acquiring and bringing together data from physical objects in different ways, based on sensor types, responsibility, acquisition process, frequency, and source. Then, the acquired data must be represented and modelled in a meaningful form. After that, it is essential to obtain high-level context information from the low-level through the processing of modelled data. The last step is distributing context information to the interested parties [20]. From an IoT point of view, IoT has expanded the range of applications with substantial needs for context management, significant effort is required to manage and exploit the data collected" through the abstraction of the things" in order to decrease the amount of data moving via the network to the next stage and improve data quality for remote monitoring services. From a cloud computing perspective, the process of acquisition, modelling, reasoning, and data dissemination gathered by IoT devices and sensors in industrial environments can take advantage from modern cloud computing service model (IaaS, PaaS, and SaaS) towards full implementation of the Industry 4.0 concept. From a remote monitoring point of view, the evaluation and analysis of colossal datasets produced through these sensors might enable quick and accurate decision making that helps to prevent machinery performance degradation, reduce maintenance costs, improve machine availability, as well as to enhance process quality. Moreover, the context has a significant role to enable provision and orchestration of adequate services to the users based on their surrounding environments.
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Fig. 5. A context-aware IoT and cloud computing framework for remote monitoring services 4. Conclusion and further research This paper aimed to provide a framework that would serve developing solutions for industrial environments as a stepping stone for more efficiency in monitoring the health status of manufacturing equipment. This paper introduced a new concept, empowered by three enablers namely, context lifecycle management, IoT, and cloud computing for remote monitoring services, while highlighting the necessity for their integration. One of the more significant findings to emerge from this study is that the combination of these enablers for remote monitoring services, is vital and essential to tackle the lack of an obviously defined view for the synthesis in literature. Furthermore, this framework is expected to help companies to obtain the highest profits from a minimum investment in equipment. This could be achieved by improving equipment reliability through the effective prediction of equipment failures. Consequently, further research should be carried out to provide architectures that would serve industrial environments to deliver more efficient IoTenabled monitoring services.
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References [1] Uhlmann, E., Laghmouchi, A., Hohwieler, E. and Geisert, C. (2015) ‘Condition Monitoring in the Cloud’, The Fourth International Conference on Through-Life Engineering Services, Procedia CIRP, 38, pp. 53–57. [2] Perera, C., Liu, C. H., Jayawardena, S. and Chen, M. (2015) ‘A Survey on Internet of Things from Industrial Market Perspective’, IEEE Access, 2, pp. 1660–1679. [3] Al-Fuqaha, Ala. Guizani, Mohsen. Mohammadi, Mehdi. Aledhari, Mohammed. Ayyash, M. (2015) ‘A survey on Internet of Things’, IEEE Communication Surveys & Tutorials, 17, No 4. [4] Marr, B. (2017) Internet of Things And Predictive Maintenance Transform The Service Industry., Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2017/05/05/internet-of-things-and-predictive-maintenance-transform-the-serviceindustry/#2c5c39e5eaf4 (Accessed: 28 April 2018). [5] Cisco (2016) Internet of Things: Connected Means Informed. Available at: https://www.cisco.com/c/dam/en/us/products/collateral/se/internetof-things/at-a-glance-c45-731471.pdf. (Accessed: 14 August 2018). [6] Koshizuka, N. and Sakamura, K. (2010) ‘Standards & Emerging Technologies Ubiquitous ID’, Context, 9(4), pp. 98–101. [7] Xu, L. Da, He, W. and Li, S. (2014) ‘Internet of things in industries: A survey’, IEEE Transactions on Industrial Informatics, 10(4), pp. 2233– 2243. [8] Breivold, H. P. and Sandstrom, K. (2015) ‘Internet of Things for Industrial Automation-Challenges and Technical Solutions’, Proceedings 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE Inte, pp. 532–539. [9] Mell, P. and Grance, T. (2011) The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology, Nist Special Publication, pp. 7. [10] ISO (2012) Condition monitoring and diagnostics of machines. Available at: https://www.iso.org/obp/ui/#iso:std:iso:13372:ed-2:v1:en (Accessed: 29 April 2018). [11] Wang, W. and Kanneg, D. (2009) ‘An integrated classifier for gear system monitoring’, Mechanical Systems and Signal Processing, 23(4), pp. 1298–1312. [12] Aazam, M., Khan, I., Alsaffar, A. A. and Huh, E. N. (2014) ‘Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved’, Proceedings of 2014 11th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2014, pp. 414–419. [13] Oracle (2016) ‘Remote Monitoring and Maintenance : Mission-Critical Operations at the Competitive Edge’. Available at: http://www.oracle.com/us/solutions/internetofthings/iot-remote-monitoring-brief-2881653.pdf. [14] Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M. and Steggles, P. (1999) ‘Towards a better understanding of context and contextawareness’, in Proc. 1st international symposium on Handheld and Ubiquitous Computing, ser. HUC ’99. London, UK: Springer-Verlag, p. 304– 307. [15] Chen, G. and Kotz, D. (2000) ‘A Survey of Context-Aware Mobile Computing Research’, Dartmouth Computer Science Technical Report, 3755, pp. 1–16. [16] Perttunen, M., Riekki, J. and Lassila, O. (2009) ‘Context Representation and Reasoning in Pervasive Computing’, International Journal of Multimedia and Ubiquitous Engineering, 4(4), pp. 1–28. [17] Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A. and Riboni, D. (2010) ‘A survey of context modelling and reasoning techniques’, Pervasive and Mobile Computing. Elsevier B.V., 6(2), pp. 161–180. [18] Saeed, A. and Waheed, T. (2010) ‘An extensive survey of context-aware middleware architectures’, 2010 IEEE International Conference on Electro/Information Technology, EIT2010. [19] Sezer, O. B., Dogdu, E. and Ozbayoglu, A. M. (2018) ‘Context Aware Computing, Learning and Big Data in Internet of Things: A Survey’, IEEE Internet of Things Journal, 5(1), pp. 1–1. [20] Perera, C., Zaslavsky, A., Christen, P. and Georgakopoulos, D. (2014) ‘Context Aware Computing for The Internet of Things’, IEEE Communications Surveys & Tutorials, 16(1), pp. 414–454.
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Procedia Manufacturing 16 (2018) 31–38 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
7th International Conference on Through-life Engineering Services 7th International Conference on Through-life Engineering Services
Development of a Context-aware framework for the Integration of Development of a Context-aware framework the Integration of Internet of Things and Cloud Computing for for Remote Monitoring Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Internet of Things and Cloud (Pontevedra), Computing for Remote Monitoring 2017, VigoServices Spain Services Ali Al-Shdifat a*, Christos Emmanouilidis a Costing models for capacity optimization in Industry 4.0: Trade-off a a AliCranfield Al-Shdifat Christos Emmanouilidis University,*, Cranfield, Bedfordshire, MK43 0AL, UK. between used capacity and operational efficiency a a
Abstract
Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK.
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
Abstract a With global competition and technological progress, there4800-058 have been growing demands by industry for more efficiency in University of Minho, Guimarães, Portugal b monitoring the health status of the manufacturing equipment in realChapecó, time. Remote monitoring services in the era of Industry 4.0 are Unochapecó, 89809-000 SC, Brazil With global faced competition and technological been growing industry for more efficiencydata in nonetheless some challenges such as progress, big data’sthere 4V have challenges (volume, demands velocity, by variety, veracity), scalability, monitoring the as health of the manufacturing in real time. Remote monitoring services the era of in Industry 4.0 are heterogeneity, wellstatus as relevant to integrating equipment data with domain knowledge. While all these poseinproblems conventional nonetheless facedbecome some challenges such as bigwhen data’s 4V challenges velocity,tovariety, scalability, data monitoring, they even more challenges integrating IoT and(volume, cloud computing deliver veracity), advanced services to offer heterogeneity, as well as relevant to integrating data with domain knowledge. While all these pose problems in conventional Abstract infrastructure availability and ubiquitous accessibility. Although it offers many benefits and solution enablers, substantial effort is monitoring, they become even the more challenges integrating IoT the andkey cloud computing to deliver services to offer required to manage and exploit data generatedwhen by "things". Among instruments to tackle suchadvanced complexity is the concept infrastructure availability and ubiquitous accessibility. Although it offers many benefits and solution enablers, substantial effort is Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, of context information management. This paper proposes a conceptual context-aware framework for the integration of Internet of required to manage and exploit the data generated by "things". Among the key instruments to tackle such complexity is the concept information based on a realfortime basis and, necessarily, Things and cloud computing remote monitoring services. much more efficient. In this context, capacity optimization of context information management. This paper proposes a conceptual context-aware framework for the integration of Internet of goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Things computing for by remote monitoring © 2018and Thecloud Authors. Published Elsevier B.V. services.
Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of This is an open access articleofunder the CCoptimization BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier B.V. maximization. The study capacity and costing models is an important research topic that deserves © 2018 The Authors. Published byofElsevier B.V. committee Peer-review under responsibility theCC scientific of the 7th This International Conference Through-life Engineering This is an open access article under the BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) contributions from both the practical and theoretical perspectives. paper presents andon discusses a mathematical This is an open access article under thescientific CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Services. Peer-review under responsibility of the committee of the 7th International Conference on Through-life Engineering Services. model for capacity management on different costing models (ABC and TDABC). modelEngineering has been Peer-review under responsibility of based the scientific committee of the 7th International Conference A ongeneric Through-life developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s Services. Keywords: Internet of Things, Context Management, Cloud Computing, Remote Monitoring Services. value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Keywords: Internet of Things, Context Management, Cloud Computing, Remote Monitoring Services. optimization might hide operational inefficiency. © 2017 The Authors. Published by Elsevier B.V. 1. Introduction Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 1. Introduction 2017.
Recent developments in the fourth industrial revolution have led to a renewed interest in the Internet of Things (IoT) for remote monitoring services in order to meetIdle theCapacity; very high demands on availability and efficiency of industrial Keywords: Models; ABC; TDABC; Capacity Management; RecentCost developments in the fourth industrial revolution haveOperational led to a Efficiency renewed interest in the Internet of Things systems [1]. The IoT has evolved widely in five stages as shown in Fig. 1. Prior to the emergence of IoT, networking (IoT) for remote monitoring services in order to meet the very high demands on availability and efficiency of industrial focused on connecting few computers systems together, moving later to scale this up by creating the World Wide systems [1]. The IoT has evolved widely in five stages as shown in Fig. 1. Prior to the emergence of IoT, networking Web. Later, mobile devices and people were connected to the internet via mobile and social networks. Eventually, the 1. Introduction focused on connecting few computers systems together, moving later to scale this up by creating the World Wide IoT extended the internet connectivity to internetwork a wide range of physical entities [2]. The IoT enables to bring Web. Later, mobile devices and people were connected to the internet via mobile and social networks. Eventually, the cost of the idleinternet capacityconnectivity is a fundamental information companies and theirentities management of extreme importance IoTThe extended to internetwork a for wide range of physical [2]. The IoT enables to bring 2351-9789 2018 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured This is an open accesstons articleofunder the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789 ©under 2018responsibility The Authors. of Published by Elsevier B.V.of the 7th International Conference on Through-life Engineering Services. Peer-review the761; scientific committee * Paulo Tel.: article +351 253 510 +351 253license 604 741 This is anAfonso. open access under the CCfax: BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mail address: [email protected] Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services.
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services. 10.1016/j.promfg.2018.10.155
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the physical world to the digital world, where physical things share information, coordinate decisions and upgrade their functions from being traditional to becoming intelligent [3]. IoT technologies are increasingly employed in a wide range of application domains. One promising application area for IoT is that of remote monitoring services. It allows to connect a monitoring system with an end user or another system and establishes a remote overview of the observed system’s state in order to prevent machinery performance degradation, reduce maintenance costs, improve machine availability, as well as to enhance process quality [4].
Fig. 1. The Internet of Things Evolution The deeper penetration of IoT in industry creates significant opportunities but also introduces challenges for monitoring service. 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. According to Cisco’s report [5], the number of Internet-connected devices is expected to reach 500 billion by 2030. These connected devices are expected to generate a large amount of data and communicate over a network, which helps drive more informed decisions and actions. Therefore, using data of significant scale for monitoring can be challenging for several reasons, for instance, processing time, data type, power, data size and storage. The sheer complexity of such activities creates a need to narrow down the scope of processing and ground it, if possible, on a sound domain. This is exactly where context information management can contribute to. It plays a central role in determining what data needs to be collected and how to process it. It also identifies what information and services are required to be presented to the consumer. In this regard, many technologies can be a significant part of that, especially by making the connected devices work together. Cloud computing is particularly relevant, enabling the delivery of hosted services for instance storage, servers, analytics, software development platforms over the internet, and processing of massive amount of data produced in IoT. This paper offers a new concept, empowered by three enablers namely, context lifecycle management, IoT, and cloud computing for remote monitoring services. Moreover, it provides a conceptual framework that would serve industrial environments as a stepping stone for more efficiency in monitoring the health status of production machinery or physical engineering assets in real time. The rest of this paper is organised as follows: Section 2 provides background and basic concepts for these enablers. Section 3 deals with a context-aware IoT and cloud computing framework for remote monitoring services. The analysis results in identifying some key challenges to be addressed by further research in the field, as summarised in conclusion. 2. Background and basic concepts 2.1. Internet of Things Nowadays, the transformation of society demands towards technological and services is a strong stimulus for improvements in industrial processes. This is further fueled by the accelerating shift to service-based business models,
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wherein service level agreements must be ascertained, supported by adequate monitoring systems. IoT brings together many functionalities, such as identification, sensing, communication, computation, services, and semantic information management, as shown in Fig. [3]. • Identification deals to defining and matching services as well as assigning a clear identity for each thing over the network [6]. • Sensing is for extracting data/information from physical entities [7]. • Communication technologies enable the interconnectivity of things and sharing of data between things or between things and the cloud [8]. • Computation: There are many computational parts of the IoT are utilized to provide IoT functionalities. Among these parts are processing units, software platforms, cloud platforms, and software applications represent the “brain” and the computational ability of the IoT. • Services: IoT services can be categorized under multiple classes. These classes are collaborative-aware services, identity-related services, ubiquitous services, and information aggregation services. • Semantic information processing offers solutions for managing the complex and often heterogeneous nature of data and knowledge from different entities, subsystems, or users.
Fig. 2. The Internet of Things Functionalities adapted from [3] The following conclusions can be drawn from this section is that with the deeper penetration of IoT technologies in monitoring tasks, the need for context information management increasingly manifests itself as a requirement for industrial applications. Context gathering, modelling, reasoning and dissemination are needed for the efficient handling of vast amounts of data, produced by numerous devices, and their efficient integration in enterprise systems. The following part of this paper moves on to describe in greater detail of opportunities to cloud computing which provide the resources needed to store and analyze the vast amount of data generated in IoT for remote monitoring services. 2.2. Cloud Computing Cloud computing is a significant shift from the conventional way companies think about IT resources that can be utilised for remote monitoring services. It brings together many benefits such as reliability, global scale, performance, speed, and cost. The term cloud computing is used here to refer to a model for allowing users to access all their applications and services from anywhere, anytime, on the Internet, because the information is stored on the server servers provided for cloud computing services and not on the user's devices. There are several types of services models
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depending on the kind of resources delivered via them as illustrates in Fig. 3. Services, platforms, and infrastructure can be quickly provided and deployed with less administrative effort or service provider interaction [9].
Fig. 3. Types of cloud services and top benefits Cloud computing affords a suitable means for accessing real-time maintenance data anywhere in the world accessible via simple devices for example smartphones, tablets or PC. Cloud computing architecture can be divided into three categories: Firstly, IaaS which provides infrastructure services and capabilities to deploy and run software in general, for instance, physical computers, virtual machines, networks, storage devices or a combination of these devices. Secondly, PaaS plays a vital role in providing capabilities for application development and deployment in Cloud. Finally, SaaS provides the necessary applications and infrastructure services from the service provider such as Application Program Interface (API). Nowadays, there are several IoT cloud platforms available that provide several operational benefits to industries especially the IoT. Among these platforms are Google Cloud Platform, IBM Watson, Microsoft Azure Cloud, and Amazon Web Services. These platforms provide many benefits such as an option to deploy IoT applications in order to evaluate, monitor, and analyse performance in cloud manufacturing systems. Moreover, they can be used to publish data to a back-end message broker and also to receive control messages from other devices or IoT applications. 2.3. Internet of things and Cloud Computing for Remote monitoring services Condition monitoring services are needed in a wide range of fields. Condition monitoring refers to the process of acquiring and processing of information and data that express the state of a machine over time, in order to provide meaningful real-time information to help reduce risk and lower failure [10]. On the other hand, A remote monitoring program which usually consists of four steps can remotely access asset condition data and enable effective, efficient predictive maintenance[11]. Data acquisition is the process of gathering valuable data from things and converting data to the digital data. Data Processing is the process of producing meaningful information by converting digital data into real quantities of working machine conditions. • Decision-making not only focuses on the nature of the machine failure but further to detect and identify a machine fault. When failure happens, the suitable actions could be considered automatically to control the operation status of machines. • Remote communication employed to transmit information, for example, machine’s operation status and alarm conditions, over the network. Aazam et al.[12] mention that because of the amount of data generated by the IoT, it has become increasingly essential to combine it with another technology such as cloud computing, to cover the storage capacity needed and demand of virtual resources utilisation. Cloud computing, one of the latest developments that can be used in remote • •
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monitoring services, allows data shared from one location, to be accessible to the end user devices over a network and the Internet [13]. Challenges such as monitoring the proper functioning of the equipment, costly breakdown and repairs and ensuring that equipment is operating at maximum efficiency and uptime all could be easier to overcome by the integration of IoT and cloud computing. Moreover, they could reduce interruptions, improve uptime, and remotely identify faults more quickly and decrease time to repair, by providing real-time data on the performance of assets which enhances their value to the business. Although many researchers presented several solutions that focus on remote monitoring in the Cloud, these solutions are used in specific scenarios but are difficult to apply in other situations. Furthermore, IoT – driven and cloud-supported monitoring services are nonetheless faced with some of the typical challenges of big data. To this end, it is necessary to introduce context awareness into this research to present contextual information about the involved objects and to determine what information and services are required to be offered to the consumers, systems, or software. Moreover, the context has a significant role in enabling provision of adequate services to the users based on their surrounding environments. In addition, the context can help IoT services to adapt with the dynamic environment changes and making the right decision. 2.4. Context Awareness There is a growing body of literature that recognises the important role of context-awareness in deploying IoT solutions in complex industrial environments. Previous studies mostly defined context to give relevant information and services to the consumer, where relevance depends on the consumer's task [14]. IoT has expanded the range of applications with substantial needs for context management, and this was reflected in the focus of relevant surveys. Nonetheless, while substantial research efforts have been devoted to context lifecycle management in web-based, mobile, and ubiquitous computing, including IoT-enabled computing, little attention has been given to translate these advances to tangible progress in remote monitoring services. Among the early surveys, Chen and Kotz [15] analysed context-aware systems regarding the types of context they use, and the best way on how to leverage contextual information, suggesting that context awareness will play an essential factor for new applications in computing everywhere. Perttunen et al. [16] have studied the popular context reasoning and representation in the area of pervasive and context-aware computing. They found that the field of context-aware computing still lacks a major breakthrough – although using expressive ontologies is suggested in some papers, the evidence does not yet show that these systems would meet all requirements. In contrast, Bettini et al. [17] also included a comprehensive survey of context reasoning and modelling by focusing on techniques rather than projects. These techniques can increase their maintainability and evolvability and decrease the complexity of contextaware applications. Another study was conducted in the same year by Saeed and Waheed [18] focusing on the architectures of context-aware based on many parameters including adaptability, interoperability, fault tolerance, architectural style and discoverability. In 2018, Sezer et al. [19] focusing on the role of a context-aware computing system in IoT. They have argued that context awareness is becoming a necessary part of the IoT solutions not just to understand changes in the environment but also to afford an opportunity to act and respond accordingly. However, while considerable research efforts have been devoted to managing the context lifecycle in different research areas, little awareness has been given to translate these advances to tangible progress in remote monitoring services. Therefore, further research is required to develop context-aware approaches and architectures to deliver more efficient IoT-enabled monitoring services. In order to do so, an outline of how the lifecycle of context information can be managed is first introduced as shown in Fig. 4. Context lifecycle refers to how data is gathered, modelled, processed, and how knowledge is deduced from the obtained data. The context lifecycle management generally consists of four steps as shown in Fig. 4.
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Fig. 4. A context lifecycle management Context lifecycle refers to how data is gathered, modelled, processed, and how knowledge is deduced from the obtained data [19]. The context lifecycle management generally consists of four steps, namely context acquisition, modelling, reasoning, and dissemination [20]. However, more detailed handling and analysis of what these steps actually involve when considering monitoring services are largely missing in the relevant literature. Figure 5 offers an illustration of the different stages of context information management, placing them against the monitoring services functionality. 3. A context-aware IoT and cloud computing framework for remote monitoring services Although IoT represents the amalgamation of many more technologies and offers numerous benefits, it requires a tremendous amount of effort to preserve and takes advantage of the big amount of information that will be generated via objects. In this regard, cloud computing is expected to represent an enormous role in offering the resources to store, communicate, monitor, and analysis of the vast amount of data produced in IoT. Furthermore, based on the literature review, there is evidence that there is a need for an obviously defined view of the methods and technologies involved in remote monitoring services. Therefore, it is clear that the combination of the IoT and cloud computing, as well as context awareness for remote monitoring services, is vital and important. In that regard, Fig. 5 illustrates a context-aware framework for the amalgamation of IoT and cloud computing for remote monitoring services. From a context lifecycle management perspective, the first stage is acquiring and bringing together data from physical objects in different ways, based on sensor types, responsibility, acquisition process, frequency, and source. Then, the acquired data must be represented and modelled in a meaningful form. After that, it is essential to obtain high-level context information from the low-level through the processing of modelled data. The last step is distributing context information to the interested parties [20]. From an IoT point of view, IoT has expanded the range of applications with substantial needs for context management, significant effort is required to manage and exploit the data collected" through the abstraction of the things" in order to decrease the amount of data moving via the network to the next stage and improve data quality for remote monitoring services. From a cloud computing perspective, the process of acquisition, modelling, reasoning, and data dissemination gathered by IoT devices and sensors in industrial environments can take advantage from modern cloud computing service model (IaaS, PaaS, and SaaS) towards full implementation of the Industry 4.0 concept. From a remote monitoring point of view, the evaluation and analysis of colossal datasets produced through these sensors might enable quick and accurate decision making that helps to prevent machinery performance degradation, reduce maintenance costs, improve machine availability, as well as to enhance process quality. Moreover, the context has a significant role to enable provision and orchestration of adequate services to the users based on their surrounding environments.
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Fig. 5. A context-aware IoT and cloud computing framework for remote monitoring services 4. Conclusion and further research This paper aimed to provide a framework that would serve developing solutions for industrial environments as a stepping stone for more efficiency in monitoring the health status of manufacturing equipment. This paper introduced a new concept, empowered by three enablers namely, context lifecycle management, IoT, and cloud computing for remote monitoring services, while highlighting the necessity for their integration. One of the more significant findings to emerge from this study is that the combination of these enablers for remote monitoring services, is vital and essential to tackle the lack of an obviously defined view for the synthesis in literature. Furthermore, this framework is expected to help companies to obtain the highest profits from a minimum investment in equipment. This could be achieved by improving equipment reliability through the effective prediction of equipment failures. Consequently, further research should be carried out to provide architectures that would serve industrial environments to deliver more efficient IoTenabled monitoring services.
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