Consistency-driven data quality management of networked sensor systems

With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management fr...

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Vydané v:	Journal of parallel and distributed computing Ročník 68; číslo 9; s. 1207 - 1221
Hlavní autori:	Sha, Kewei, Shi, Weisong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier Inc 01.09.2008 Elsevier
Predmet:	Adaptation Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Consistency models Data quality Energy efficiency Exact sciences and technology Information systems. Data bases Memory organisation. Data processing Software Wireless sensor networks Wireless sensor networks Data quality Energy efficiency Consistency models Adaptation Streaming Measurement sensor Interconnected power system Data quality;Consistency models;Wireless sensor networks;Energy efficiency;Adaptation Unfolding Application program interfaces Network management Distributed system Modeling Adaptive method Energy savings Data integrity Storage management Sampling rate Database Wireless network Data models Sampling Sensor array Quality management
ISSN:	0743-7315, 1096-0848
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Abstract	With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications.
AbstractList	With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications. With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications.
Author	Sha, Kewei Shi, Weisong
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Keywords	Wireless sensor networks Data quality Energy efficiency Consistency models Adaptation Streaming Measurement sensor Interconnected power system Data quality;Consistency models;Wireless sensor networks;Energy efficiency;Adaptation Unfolding Application program interfaces Network management Distributed system Modeling Adaptive method Energy savings Data integrity Storage management Sampling rate Database Wireless network Data models Sampling Sensor array Quality management
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Snippet	With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor...
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SubjectTerms	Adaptation Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Consistency models Data quality Energy efficiency Exact sciences and technology Information systems. Data bases Memory organisation. Data processing Software Wireless sensor networks
Title	Consistency-driven data quality management of networked sensor systems
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