Date of Award
Master of Science
Electrical and Computer Engineering
High information quality is a paramount requirement for wireless sensor network monitoring applications. However, it is challenging to achieve a cost effective information quality solution due to unpredictable environment noise and events, unreliable wireless channel and network bandwidth, and resource and energy constraints. Specifically, the dynamic and unreliable nature of WSNs make it difficult to pre-determine optimum sensor rates and predict packet loss. To address this problem, we use information quality metrics presented by [26, 11] which characterize information quality based on the sampling frequency of sensor nodes and the packet loss rate during network transmission. These fundamental quality metrics are based on signal-to-noise ratio and are therefore application independent. Based on these metrics, a quality-aware scheduling system (QSS) is developed, which exploits cross-layer control of sensor nodes to effectively schedule data sensing and forwarding. Particularly, we develop and evaluate several QSS scheduling mechanisms: passive, reactive and perceptive. These mechanisms can adapt to environment noise, bandwidth variation and wireless channel collisions by dynamically controlling sensor rates and sensor phase. Our software and hardware experimental results indicate that our QSS is a novel and effective approach to improve information quality for WSNs.
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