Round Robin Cycle for Predictions in Wireless Sensor Networks

Use of prediction models in sensor networks proves to be efficient with respect to energy savings, as it allows sensors whose readings are predicted to remain in their idle mode, thereby consuming orders of magnitude less energy that in the active mode. In the context of continuous monitoring, where a set of sensors is typically required to regularly send their readings to a central server, an interesting approach consists in splitting the set of sensors in two subsets, such that readings of one subset are used to predict readings of the second subset. In this paper, we propose to identify several sensor subsets for predictions, that are used in turn in a round robin fashion. Identification of different sensor subsets allows to detect erroneous models or sensor failure, and to better distribute energy consumption. Efficiency of the proposed procedure is demonstrated on a set of experiments using real world sensor data.

Introduction

Many applications of wireless sensor networks require a large set of sensors (potentially hundreds or thousands) to regularly report their readings to a central server to undertake off-line data analysis. These applications, for instance environmental or structural monitoring, HVAC systems, or object tracking, also require the sensors to be running for as long a period of time as possible. Limited energy ressources available on a sensor module (a.k.a. mote) drive the need for efficient scheduling of sensing and networking activities, so as to maximize the application lifetime.