Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
IEEE Transactions on Parallel and Distributed Systems
In-situ soil moisture sensing: measurement scheduling and estimation using compressive sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
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As the number of sensors increase in wireless sensing applications, it is important for nodes to provide meaningful summary reports of the original data to the gateway. In doing so, given the resource constraints of the sensing devices, we need a light weight, yet, effective scheme to minimize the number of reports at the sensors while preserving the accuracy of the original data. However, we show in this work that unlike outdoors environments where various sensors may show a similar phenomena (e.g., high spatial correlation), this may not be true for sensors deployed in a typical indoors environment. To resolve this issue, we introduce a data summarizing scheme for such indoor applications that combines two techniques. First, our scheme detects events in a data stream by comparing the short term mean of the recent measurements with the most recent report sent to the gateway. Second, we include an exponentially increasing/decreasing timer that triggers additional reports where the timer's interval is reconfigured dynamically with respect to the result of our event detection method. Evaluations with temperature and humidity data collected in an indoors environment indicate that our scheme significantly reduces the number of transmissions while maintaining a mean error as low as ~0.07°C and ~0.08%RH.