Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach

The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and techn...
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Autor*in:	An Ngoc Lam [verfasserIn] Oystein Haugen [verfasserIn] Jerker Delsing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Arrowhead framework autonomic computing industrial IoT self-adaptation semantic interoperability semantic web

Übergeordnetes Werk:	In: IEEE Open Journal of the Industrial Electronics Society - IEEE, 2020, 3(2022), Seite 128-145
Übergeordnetes Werk:	volume:3 ; year:2022 ; pages:128-145

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/OJIES.2022.3149093

Katalog-ID:	DOAJ004496299

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allfieldsSound	10.1109/OJIES.2022.3149093 doi (DE-627)DOAJ004496299 (DE-599)DOAJeb06bbb6a1504c308e7da5f2f4db5382 DE-627 ger DE-627 rakwb eng TK7800-8360 T55.4-60.8 An Ngoc Lam verfasserin aut Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible. Arrowhead framework autonomic computing industrial IoT self-adaptation semantic interoperability semantic web Electronics Industrial engineering. Management engineering Oystein Haugen verfasserin aut Jerker Delsing verfasserin aut In IEEE Open Journal of the Industrial Electronics Society IEEE, 2020 3(2022), Seite 128-145 (DE-627)1690051620 (DE-600)3008466-0 26441284 nnns volume:3 year:2022 pages:128-145 https://doi.org/10.1109/OJIES.2022.3149093 kostenfrei https://doaj.org/article/eb06bbb6a1504c308e7da5f2f4db5382 kostenfrei https://ieeexplore.ieee.org/document/9706288/ kostenfrei https://doaj.org/toc/2644-1284 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2022 128-145
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callnumber-first	T - Technology
author	An Ngoc Lam
spellingShingle	An Ngoc Lam misc TK7800-8360 misc T55.4-60.8 misc Arrowhead framework misc autonomic computing misc industrial IoT misc self-adaptation misc semantic interoperability misc semantic web misc Electronics misc Industrial engineering. Management engineering Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach
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topic_title	TK7800-8360 T55.4-60.8 Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach Arrowhead framework autonomic computing industrial IoT self-adaptation semantic interoperability semantic web
topic	misc TK7800-8360 misc T55.4-60.8 misc Arrowhead framework misc autonomic computing misc industrial IoT misc self-adaptation misc semantic interoperability misc semantic web misc Electronics misc Industrial engineering. Management engineering
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title	Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach
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title_full	Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach
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title_sort	dynamical orchestration and configuration services in industrial iot systems: an autonomic approach
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title_auth	Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach
abstract	The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.
abstractGer	The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.
abstract_unstemmed	The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.
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title_short	Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach
url	https://doi.org/10.1109/OJIES.2022.3149093 https://doaj.org/article/eb06bbb6a1504c308e7da5f2f4db5382 https://ieeexplore.ieee.org/document/9706288/ https://doaj.org/toc/2644-1284
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