Information quality model and optimization for 802.15.4-based wireless sensor networks

High information quality is a paramount requirement for wireless sensor network (WSN) 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 sensor 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 present an information quality metric which characterizes information quality based on the sampling frequency of sensor nodes and the packet loss rate during network transmission. Our fundamental quality metric is based on signal-to-noise ratio and is therefore application independent. Based on our metric, 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 phase. Our experimental results indicate that our QSS is a novel and effective approach to improve information quality for WSNs.