An Adaptive Strategy for Quality-Based Data Reduction in Wireless Sensor Networks

Wireless sensor networks allow fine-grained observations of real-world phenomena. However, providing constant measurement updates incurs high communication costs for each individual node, resulting in increased energy depletion in the network. Data reduction strategies aim at reducing the amount of data sent by each node, for example by predicting the measured values both at the source and the sink node, thus only requiring nodes to send the readings that deviate from the prediction. While effectively reducing power consumption, such techniques so far needed to rely on a-priori knowledge to correctly model the expected values. Our approach instead employs an algorithm that requires no prior modeling, allowing nodes to work independently and without using global model parameters. Using the LeastMean-Square (LMS) adaptive algorithm on a publicly available, real-world (office environment) temperature data set, we have been able to achieve up to 92% communication reduction while maintaining a minimal accuracy of 0.5 degree Celsius.