An Online Prediction Framework for Sensor Networks

This paper presents a novel approach to online prediction in sensor networks based on temporal correlation of sensed data. The proposed approach greatly reduces the amount of data transmitted by sensor nodes and thus increasing network lifetime. Current prediction frameworks in sensor networks use an offline created model to predict the sensed value. In contrast, our approach creates and uses a prediction model in an online manner, no extra buffering or model creation delay is needed. Moreover, the amount of error caused by using our framework is bounded and often negligible.