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With more and more real deployments of wireless sensor network applications, their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this book, a consistency-driven data quality management framework called Orchis is proposed. It 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. Firstly, a consistency model is formally defined, 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, an adaptive lazy energy efficient data collection protocol is proposed, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, some system protocols are designed to provide system support.

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Kewei Sha is an Assistant Professor at Oklahoma City University. He got BS degree from East China University of Science and Technology, M.S. and Ph.D. degree from Wayne State University. His research interests include Distributed Systems, Participatory Sensing and Cyber-Physical systems.

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