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Efficient Continuous Skyline Query Processing in Wireless Sensor Networks

Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WS...

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Autores principales: Xiao, Yingyuan, Jiao, Xu, Wang, Hongya, Hsu, Ching-Hsien, Liu, Li, Zheng, Wenguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651498/
https://www.ncbi.nlm.nih.gov/pubmed/31262069
http://dx.doi.org/10.3390/s19132902
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author Xiao, Yingyuan
Jiao, Xu
Wang, Hongya
Hsu, Ching-Hsien
Liu, Li
Zheng, Wenguang
author_facet Xiao, Yingyuan
Jiao, Xu
Wang, Hongya
Hsu, Ching-Hsien
Liu, Li
Zheng, Wenguang
author_sort Xiao, Yingyuan
collection PubMed
description Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of the popular queries for multi-criteria decision making, has received considerable attention due to its numerous applications. In this paper, we study how to process a continuous skyline query over a sensor data stream in WSNs. We present an energy-efficient continuous skyline query method called EECS. EECS can avoid the transmission of invalid sensor data and prolong the lifetime of WSNs. Extensive experiments are conducted, and the experimental results demonstrate the effectiveness of the proposed method.
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spelling pubmed-66514982019-08-08 Efficient Continuous Skyline Query Processing in Wireless Sensor Networks Xiao, Yingyuan Jiao, Xu Wang, Hongya Hsu, Ching-Hsien Liu, Li Zheng, Wenguang Sensors (Basel) Article Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of the popular queries for multi-criteria decision making, has received considerable attention due to its numerous applications. In this paper, we study how to process a continuous skyline query over a sensor data stream in WSNs. We present an energy-efficient continuous skyline query method called EECS. EECS can avoid the transmission of invalid sensor data and prolong the lifetime of WSNs. Extensive experiments are conducted, and the experimental results demonstrate the effectiveness of the proposed method. MDPI 2019-06-30 /pmc/articles/PMC6651498/ /pubmed/31262069 http://dx.doi.org/10.3390/s19132902 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xiao, Yingyuan
Jiao, Xu
Wang, Hongya
Hsu, Ching-Hsien
Liu, Li
Zheng, Wenguang
Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
title Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
title_full Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
title_fullStr Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
title_full_unstemmed Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
title_short Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
title_sort efficient continuous skyline query processing in wireless sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651498/
https://www.ncbi.nlm.nih.gov/pubmed/31262069
http://dx.doi.org/10.3390/s19132902
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