FDFA: A fog computing assisted distributed analytics and detecting system for family activities

Gu, F; Niu, J; Jin, X; Yu, S

FDFA: A fog computing assisted distributed analytics and detecting system for family activities

Gu, F Niu, J Jin, X Yu, S

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Publisher:: Springer Verlag
Publication Type:: Journal Article
Citation:: Peer-to-Peer Networking and Applications, 2020, 13, (1), pp. 38-52
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Gu, F
dc.contributor.author	Niu, J
dc.contributor.author	Jin, X
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-01-07T00:18:08Z
dc.date.available	2021-01-07T00:18:08Z
dc.date.issued	2020
dc.identifier.citation	Peer-to-Peer Networking and Applications, 2020, 13, (1), pp. 38-52
dc.identifier.issn	1936-6450
dc.identifier.issn	1936-6450
dc.identifier.uri	http://hdl.handle.net/10453/145138
dc.description.abstract	© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Researches have shown that taking parting in family activities could establish good relationships with family members. Fine-grained family activities detection is proven effective for increasing self-awareness and motivating people to modify their life styles for improved well being. Mobile health provides the possibility to solve this problem. However, with the increase of such applications, the requirements for computation, communication, and storage capability are becoming higher and higher. Fog computing, a new computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, communication, storage, and so on. In this paper, we propose FDFA, the first fog computing assisted distributed analytics and detecting system for family activities using smartphones and smart watches. Specifically, FDFA firstly uses the built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, and so forth. Then, a fog computing assisted resolution framework is proposed to efficiently detect family activities in an unobtrusive manner based on sensed data. Finally, considering the characteristics of different people, FDFA sets a personal plan for family members in doing some exercise and making continuous progress in the process of communicating. We have fully implemented FDFA on the Android platform and the extensive experimental results demonstrate that FDFA is easy to use, accurate, and appropriate for family activities with the accuracy of 79.1% and the user satisfaction degree of 82.4%. Moreover, the system can achieve more than 90% bandwidth efficiency and offer low-latency real time response with fog computing.
dc.language	en
dc.publisher	Springer Verlag
dc.relation.ispartof	Peer-to-Peer Networking and Applications
dc.relation.isbasedon	10.1007/s12083-018-0714-5
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	FDFA: A fog computing assisted distributed analytics and detecting system for family activities
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-01-07T00:18:03Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	1

Abstract:

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Researches have shown that taking parting in family activities could establish good relationships with family members. Fine-grained family activities detection is proven effective for increasing self-awareness and motivating people to modify their life styles for improved well being. Mobile health provides the possibility to solve this problem. However, with the increase of such applications, the requirements for computation, communication, and storage capability are becoming higher and higher. Fog computing, a new computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, communication, storage, and so on. In this paper, we propose FDFA, the first fog computing assisted distributed analytics and detecting system for family activities using smartphones and smart watches. Specifically, FDFA firstly uses the built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, and so forth. Then, a fog computing assisted resolution framework is proposed to efficiently detect family activities in an unobtrusive manner based on sensed data. Finally, considering the characteristics of different people, FDFA sets a personal plan for family members in doing some exercise and making continuous progress in the process of communicating. We have fully implemented FDFA on the Android platform and the extensive experimental results demonstrate that FDFA is easy to use, accurate, and appropriate for family activities with the accuracy of 79.1% and the user satisfaction degree of 82.4%. Moreover, the system can achieve more than 90% bandwidth efficiency and offer low-latency real time response with fog computing.

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http://hdl.handle.net/10453/145138