A clustering approach for sampling data streams in sensor networks

This article presents a clustering approach for rapidly and efficiently computing the best sampling rate which minimizes the Sum of Square Error for each particular sensor in a network.

Abstract

The growing usage of embedded devices and sensors in our daily lives has been profoundly reshaping the way we interact with our environment and our peers. As more and more sensors will pervade our future cities, increasingly efficient infrastructures to collect, process and store massive amounts of data streams from a wide variety of sources will be required. Despite the different application-specific features and hardware platforms, sensor network applications share a common goal: periodically sample and store data collected from different sensors in a common persistent memory. In this article, we present a clustering approach for rapidly and efficiently computing the best sampling rate which minimizes the Sum of Square Error for each particular sensor in a network. In order to evaluate the efficiency of the proposed approach, we carried out experiments on real electric power consumption data streams provided by EDF (Électricité de France).