Online model-driven data acquisition for wireless sensor networks

Abstract

Model-driven data acquisition is one of the strategies utilized to save sensor node energy in Wireless Sensor Networks (WSNs), which suppresses data transmission by running one synchronized prediction model at both the sensor and sink node, and only when the predicted value deviates far from the real value should the sensor node transmit the sensed data to the sink node. In this paper, we propose a novel online model-driven data acquisition method which runs two prediction models on the sensor node simultaneously. Specifically, one model is updated online using stochastic gradient descent (SGD) learning algorithm once a new sensor data is available and the other is used to predict sensor value and updated with the former one when it is time for model re-training. The collaborative working of these two models, together with the SGD learning algorithm, solve two main problems of existing methods: data transmission during off-line model re-training and high resource requirement for model update. Extensive experiments are performed to verify the benefits of our method over two existing methods based on more than 20000 data records from three data sets. The experiment results demonstrate that up to 96% of data transmission are reduced by our method while remaining user defined data accuracy, which outperforms the compared methods in terms of energy usage and data accuracy.