Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring

Critical infrastructure monitoring is one of the most important applications of a smart city. The objective is to monitor the integrity of the structures (e.g., buildings, bridges) and detect and pinpoint the locations of possible events (e.g., damages, cracks). Regarding today’s complex structures,...

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Main Authors:	Wang, Tian, Bhuiyan, Md Zakirul Alam, Wang, Guojun, Rahman, Md. Arafatur, Wu, Jie, Cao, Jiannong
Format:	Article
Language:	English
Published:	IEEE Communications Society 2018
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/23750/ http://umpir.ump.edu.my/id/eprint/23750/ http://umpir.ump.edu.my/id/eprint/23750/ http://umpir.ump.edu.my/id/eprint/23750/8/Big%20Data%20Reduction%20for%20a%20Smart%20City.pdf

id	ump-23750
recordtype	eprints
spelling	ump-237502019-01-22T03:19:20Z http://umpir.ump.edu.my/id/eprint/23750/ Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring Wang, Tian Bhuiyan, Md Zakirul Alam Wang, Guojun Rahman, Md. Arafatur Wu, Jie Cao, Jiannong Q Science (General) Critical infrastructure monitoring is one of the most important applications of a smart city. The objective is to monitor the integrity of the structures (e.g., buildings, bridges) and detect and pinpoint the locations of possible events (e.g., damages, cracks). Regarding today’s complex structures, collecting data using wireless sensor data over extensive vertical lengths creates enormous challenges. With a direct BS deployment, a big amount of data will accumulate to be relayed to the BS. As a result, traditional models and schemes developed for health monitoring are largely challenged by low-cost, quality-guaranteed, and real-time event monitoring. In this article, we propose BigReduce, a cloud based health monitoring application with an IoT framework that could cover most of the key infrastructures of a smart city under an umbrella and provide event monitoring. To reduce the burden of big data processing at the BS and enhance the quality of event detection, we integrate real-time data processing and intelligent decision making capabilities with BigReduce. Particularly, we provide two innovative schemes for health event monitoring so that an IoT sensor can use them locally; one is a big data reduction scheme, and the other is a decision making scheme. We believe that BigReduce will result in a remarkable performance in terms of data reduction, energy cost reduction, and the quality of monitoring. IEEE Communications Society 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23750/8/Big%20Data%20Reduction%20for%20a%20Smart%20City.pdf Wang, Tian and Bhuiyan, Md Zakirul Alam and Wang, Guojun and Rahman, Md. Arafatur and Wu, Jie and Cao, Jiannong (2018) Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring. IEEE Communications Magazine, 56 (3). pp. 128-133. ISSN 0163-6804 https://doi.org/10.1109/MCOM.2018.1700303 https://doi.org/10.1109/MCOM.2018.1700303
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Wang, Tian Bhuiyan, Md Zakirul Alam Wang, Guojun Rahman, Md. Arafatur Wu, Jie Cao, Jiannong Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring
description	Critical infrastructure monitoring is one of the most important applications of a smart city. The objective is to monitor the integrity of the structures (e.g., buildings, bridges) and detect and pinpoint the locations of possible events (e.g., damages, cracks). Regarding today’s complex structures, collecting data using wireless sensor data over extensive vertical lengths creates enormous challenges. With a direct BS deployment, a big amount of data will accumulate to be relayed to the BS. As a result, traditional models and schemes developed for health monitoring are largely challenged by low-cost, quality-guaranteed, and real-time event monitoring. In this article, we propose BigReduce, a cloud based health monitoring application with an IoT framework that could cover most of the key infrastructures of a smart city under an umbrella and provide event monitoring. To reduce the burden of big data processing at the BS and enhance the quality of event detection, we integrate real-time data processing and intelligent decision making capabilities with BigReduce. Particularly, we provide two innovative schemes for health event monitoring so that an IoT sensor can use them locally; one is a big data reduction scheme, and the other is a decision making scheme. We believe that BigReduce will result in a remarkable performance in terms of data reduction, energy cost reduction, and the quality of monitoring.
format	Article
author	Wang, Tian Bhuiyan, Md Zakirul Alam Wang, Guojun Rahman, Md. Arafatur Wu, Jie Cao, Jiannong
author_facet	Wang, Tian Bhuiyan, Md Zakirul Alam Wang, Guojun Rahman, Md. Arafatur Wu, Jie Cao, Jiannong
author_sort	Wang, Tian
title	Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring
title_short	Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring
title_full	Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring
title_fullStr	Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring
title_full_unstemmed	Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring
title_sort	big data reduction for a smart city’s critical infrastructural health monitoring
publisher	IEEE Communications Society
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23750/ http://umpir.ump.edu.my/id/eprint/23750/ http://umpir.ump.edu.my/id/eprint/23750/ http://umpir.ump.edu.my/id/eprint/23750/8/Big%20Data%20Reduction%20for%20a%20Smart%20City.pdf
first_indexed	2023-09-18T22:35:42Z
last_indexed	2023-09-18T22:35:42Z
_version_	1777416580714463232

Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring

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