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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Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 68; no. 9; pp. 1207 - 1221
Main Authors: Sha, Kewei, Shi, Weisong
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 01.09.2008
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2008.06.004