Light-weight Online Predictive Data Aggregation for Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) have found many practical applications in recent years. Apart from both the vast new opportunities and challenges raised by the availability of large amounts of sensory data, energy conservation remains a challenging research topic that demands intelligent solutions. Various data aggregation techniques have been proposed in the literature, but the optimal tradeoff between algorithm complexity and prediction ability remains elusive. In this paper we concentrate on employing a few light-weight time series estimation algorithms for online predictive sensing. A number of performance metrics are proposed and employed to examine the effectiveness of the scheme using real-world datasets.