Applications from industrial process automation to environmental monitoring can be achieved using wireless sensor networks. A team of researchers from the Alpen-Adria-Universität Klagenfurt have developed a time synchronization method and have conducted experimental performance testing. The technique developed learns the actions of the sensor clocks, making it especially efficient with regard to energy and computational resources.
For years, researchers have been involved in enhancing sensor networks. An important design goal is to maintain the cost of individual sensors (such as thermometers and cameras) as low as possible to enable large networks with numerous linked sensors. This entails a drawback: economical sensors have minimal energy and computing abilities. Therefore, methods designed to exploit the limited resources to the maximum are of vital importance.
Time synchronization plays a primary role in this scenario. Tight synchronization can reduce the energy consumption of the nodes by minimizing their radio activity time. This extends their duration considerably. Researchers at the Institute for Networked and Embedded Systems at the Alpen-Adria-Universität Klagenfurt have come up with a new synchronization method to solve this issue.
Particular importance was laid on ensuring that the technique is not too greedy in its utilization of resources, which would annul the benefits of the synchronization.
“Imagine that a group of friends have arranged a meeting. Usually you agree on a time and place. It is often the case that not all of them arrive on time, so the coordinator of the meeting calls the latecomers. This involves effort,” explains Jorge Schmidt, Postdoctoral researcher in Professor Bettstetter’s team. If this illustration is transferred to the sensor networks that he and his team are analyzing, this attempt means a loss of energy and computing power for the individual sensors.
Collaborating with doctoral student Wasif Masood, Schmidt and Bettstetter have at present developed a method that decreases the extra effort of synchronization between the oscillators of the individual sensors.
Schmidt clarifies this in more detail using an example:
With a group of friends, we already know who is usually late. Therefore, the coordinator of such a meeting could tell the individual friends different times in order to intercept the delay. This is exactly what the newly developed technique does: Using time series analysis it learns the behavior of the sensor clocks and can anticipate or correct future deferrals before asynchronicities can even begin to develop. While the idea of learning behaviors to predict future corrections is not new, we have shown that the behavior models extracted from our time series analysis work very well with commonly employed wireless sensor devices.
Jorge Schmidt, Postdoctoral Researcher, Alpen-Adria-Universität Klagenfurt
The synchronization method was analyzed in the lab as well as outdoors under different temperature conditions using sensor devices available in the market.