Model-Driven Data Acquisition for Temperature Sensor Readings in Wireless Sensor Networks

The increasing interest and utilization of Wireless Sensor Networks has increased the requirements of energy saving for battery powered sensor nodes. Even in modern sensor nodes, communication causes the largest part of energy consumption and therefore ways to reduce the amount of data sending are widely concerned. One solution to reduce data transmission is a model-driven data acquisition technique called Derivative-Based Prediction (DBP). Instead of transmitting every measured sample, a sensor node uses algorithms to compute approximated models to represent the measured data. In this work, we developed an algorithm to monitor temperature samples in different environmental scenarios. We also evaluated the algorithm with regard to its efficiency and classified the recorded temperature patterns to enhance the precision. In our tests, the algorithm successfully suppressed up to 99% of data transmissions while the average error of prediction has been kept below 0.1 degrees C.