ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems

In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data conne...
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Autor*in:	Zhishu Shen [verfasserIn] Tiehua Zhang [verfasserIn] Jiong Jin [verfasserIn] Kenji Yokota [verfasserIn] Atsushi Tagami [verfasserIn] Teruo Higashino [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 40402-40415
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:40402-40415

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2906645

Katalog-ID:	DOAJ057685401

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allfields	10.1109/ACCESS.2019.2906645 doi (DE-627)DOAJ057685401 (DE-599)DOAJ87f151aaa38f45a5be2a2a0d66d1e027 DE-627 ger DE-627 rakwb eng TK1-9971 Zhishu Shen verfasserin aut ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment. Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing Electrical engineering. Electronics. Nuclear engineering Tiehua Zhang verfasserin aut Jiong Jin verfasserin aut Kenji Yokota verfasserin aut Atsushi Tagami verfasserin aut Teruo Higashino verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 40402-40415 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:40402-40415 https://doi.org/10.1109/ACCESS.2019.2906645 kostenfrei https://doaj.org/article/87f151aaa38f45a5be2a2a0d66d1e027 kostenfrei https://ieeexplore.ieee.org/document/8672574/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 7 2019 40402-40415
spelling	10.1109/ACCESS.2019.2906645 doi (DE-627)DOAJ057685401 (DE-599)DOAJ87f151aaa38f45a5be2a2a0d66d1e027 DE-627 ger DE-627 rakwb eng TK1-9971 Zhishu Shen verfasserin aut ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment. Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing Electrical engineering. Electronics. Nuclear engineering Tiehua Zhang verfasserin aut Jiong Jin verfasserin aut Kenji Yokota verfasserin aut Atsushi Tagami verfasserin aut Teruo Higashino verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 40402-40415 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:40402-40415 https://doi.org/10.1109/ACCESS.2019.2906645 kostenfrei https://doaj.org/article/87f151aaa38f45a5be2a2a0d66d1e027 kostenfrei https://ieeexplore.ieee.org/document/8672574/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 7 2019 40402-40415
allfields_unstemmed	10.1109/ACCESS.2019.2906645 doi (DE-627)DOAJ057685401 (DE-599)DOAJ87f151aaa38f45a5be2a2a0d66d1e027 DE-627 ger DE-627 rakwb eng TK1-9971 Zhishu Shen verfasserin aut ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment. Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing Electrical engineering. Electronics. Nuclear engineering Tiehua Zhang verfasserin aut Jiong Jin verfasserin aut Kenji Yokota verfasserin aut Atsushi Tagami verfasserin aut Teruo Higashino verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 40402-40415 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:40402-40415 https://doi.org/10.1109/ACCESS.2019.2906645 kostenfrei https://doaj.org/article/87f151aaa38f45a5be2a2a0d66d1e027 kostenfrei https://ieeexplore.ieee.org/document/8672574/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 7 2019 40402-40415
allfieldsGer	10.1109/ACCESS.2019.2906645 doi (DE-627)DOAJ057685401 (DE-599)DOAJ87f151aaa38f45a5be2a2a0d66d1e027 DE-627 ger DE-627 rakwb eng TK1-9971 Zhishu Shen verfasserin aut ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment. Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing Electrical engineering. Electronics. Nuclear engineering Tiehua Zhang verfasserin aut Jiong Jin verfasserin aut Kenji Yokota verfasserin aut Atsushi Tagami verfasserin aut Teruo Higashino verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 40402-40415 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:40402-40415 https://doi.org/10.1109/ACCESS.2019.2906645 kostenfrei https://doaj.org/article/87f151aaa38f45a5be2a2a0d66d1e027 kostenfrei https://ieeexplore.ieee.org/document/8672574/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 7 2019 40402-40415
allfieldsSound	10.1109/ACCESS.2019.2906645 doi (DE-627)DOAJ057685401 (DE-599)DOAJ87f151aaa38f45a5be2a2a0d66d1e027 DE-627 ger DE-627 rakwb eng TK1-9971 Zhishu Shen verfasserin aut ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment. Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing Electrical engineering. Electronics. Nuclear engineering Tiehua Zhang verfasserin aut Jiong Jin verfasserin aut Kenji Yokota verfasserin aut Atsushi Tagami verfasserin aut Teruo Higashino verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 40402-40415 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:40402-40415 https://doi.org/10.1109/ACCESS.2019.2906645 kostenfrei https://doaj.org/article/87f151aaa38f45a5be2a2a0d66d1e027 kostenfrei https://ieeexplore.ieee.org/document/8672574/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 7 2019 40402-40415
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authorswithroles_txt_mv	Zhishu Shen @@aut@@ Tiehua Zhang @@aut@@ Jiong Jin @@aut@@ Kenji Yokota @@aut@@ Atsushi Tagami @@aut@@ Teruo Higashino @@aut@@
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callnumber-first	T - Technology
author	Zhishu Shen
spellingShingle	Zhishu Shen misc TK1-9971 misc Building energy management system misc fog computing misc information-centric networking misc Internet of Things misc machine learning misc sensor data processing misc Electrical engineering. Electronics. Nuclear engineering ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems
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topic_title	TK1-9971 ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems Building energy management system fog computing information-centric networking Internet of Things machine learning sensor data processing
topic	misc TK1-9971 misc Building energy management system misc fog computing misc information-centric networking misc Internet of Things misc machine learning misc sensor data processing misc Electrical engineering. Electronics. Nuclear engineering
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title	ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems
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title_sort	iccf: an information-centric collaborative fog platform for building energy management systems
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title_auth	ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems
abstract	In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment.
abstractGer	In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment.
abstract_unstemmed	In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment.
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title_short	ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems
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