A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks.

Many approaches have been proposed in the literature to reduce energy consumption in Wireless Sensor Networks (WSNs). Influenced by the fact that radio communication and sensing are considered to be the most energy consuming activities in such networks. Most of these approaches focused on either reducing the number of collected data using adaptive sampling techniques or on reducing the number of data transmitted over the network using prediction models. In this article, we propose a novel prediction-based data reduction method. Furthermore, we combine it with an adaptive sampling rate technique, allowing us to significantly decrease energy consumption and extend the whole network lifetime. To validate our work, we tested our approach on real sensor data collected at our offices. The final results were promising and confirmed our theoretical claims.