Orchis: consistency-driven data quality management in sensing systems

As new fabrication and integration technologies reduce the cost and size of wireless sensors, the observation and control of our physical world will expand dramatically using the temporally and spatially dense monitoring afforded by wireless sensing systems. Their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. However, most previous efforts focus on devising techniques to save the sensor node energy and thus extend the lifetime of the whole sensor network. With more and more deployments of real sensor systems, in which the main function is to collect interesting data and to share with peers, data quality has been becoming a more important issue in the design of sensor systems. In this dissertation, we envision that the quality of data should reflect the timeliness and accuracy of collected data that are presented to interested recipients who make the final decision based on these data. Thus, we undertake a novel approach that detects deceptive data through considering the consistency requirements of data, and study the relationship between the quality of data and the multi-hop communication and energy-efficient design of networked sensor systems. In this dissertation, we tackle the data quality management problem by proposing a general framework, called Orchis, which mainly consists of six components, including an analysis to the characteristics of the sensing data from an environmental application, a set of data consistency models customized to wireless sensing systems, a set of APIs to management the quality of collected data, an adaptive protocol for data sampling, a framework to detect and filter deceptive data, and a formal model for the lifetime of the wireless sensing system to evaluate the energy efficiency performance of the protocols. The experiment performance from both simulation and prototype shows that the Orchis framework is promising in terms of both energy efficiency and data consistency.

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