Air Quality Monitoring System and Benchmarking

Xiufeng Liu; Per Sieverts Nielsen

doi:10.1007/978-3-319-64283-3_34

Air Quality Monitoring System and Benchmarking

CITIES - Centre for IT-Intelligent Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.

Original language	English
Title of host publication	International Conference on Big Data Analytics and Knowledge Discovery
Publisher	Springer
Publication date	2017
Pages	459-470
DOIs	https://doi.org/10.1007/978-3-319-64283-3_34
Publication status	Published - 2017
Event	19th International Conference on Big Data Analytics and Knowledge Discovery - Lyon, France Duration: 28 Aug 2017 → 31 Aug 2017 Conference number: 19

Conference

Conference	19th International Conference on Big Data Analytics and Knowledge Discovery
Number	19
Country/Territory	France
City	Lyon
Period	28/08/2017 → 31/08/2017

Series	Lecture Notes in Computer Science
Volume	10440
ISSN	0302-9743

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

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10.1007/978-3-319-64283-3_34

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CTT: Carbon Track and Trace
Liu, X. (Project Participant) & Nielsen, P. S. (PI)
10/09/2016 → 30/09/2018
Project: Research

Cite this

@inproceedings{d1d89ea2f95641ae9df2e982352cca25,

title = "Air Quality Monitoring System and Benchmarking",

abstract = "Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.",

author = "Xiufeng Liu and Nielsen, \{Per Sieverts\}",

year = "2017",

doi = "10.1007/978-3-319-64283-3\_34",

language = "English",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "459--470",

booktitle = "International Conference on Big Data Analytics and Knowledge Discovery",

note = "19th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2017 ; Conference date: 28-08-2017 Through 31-08-2017",

}

TY - GEN

T1 - Air Quality Monitoring System and Benchmarking

AU - Liu, Xiufeng

AU - Nielsen, Per Sieverts

N1 - Conference code: 19

PY - 2017

Y1 - 2017

N2 - Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.

AB - Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.

U2 - 10.1007/978-3-319-64283-3_34

DO - 10.1007/978-3-319-64283-3_34

M3 - Article in proceedings

T3 - Lecture Notes in Computer Science

SP - 459

EP - 470

BT - International Conference on Big Data Analytics and Knowledge Discovery

PB - Springer

T2 - 19th International Conference on Big Data Analytics and Knowledge Discovery

Y2 - 28 August 2017 through 31 August 2017

ER -

Air Quality Monitoring System and Benchmarking

Abstract

Conference

UN SDGs

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CTT: Carbon Track and Trace

Cite this