Artificial Intelligence And Synchronization In Wireless Sensor Networks

The basic concept behind a Wireless Sensor Network is to deploy a large number of sensor nodes able to acquire and process data. Most of WSNs applications require sensor nodes to maintain local clocks both to determine the events order and to provide temporal information to measured data. Thus, providing a powerful synchronization system is one of the most important goals to be pursued if an efficient utilization of sensor networks has to be addressed. In order to achieve this goal, applications generally require a synchronization precision close to Milli seconds. This paper proposes a novel synchronization system based on Kohonens Self Organizing Maps (SOMs), able to provide some Artificial Intelligence features to sensor nodes. A SOM is a particular neural network that learns to classify data without any supervision. In each sensor node, a SOM is implemented to evaluate the sensor node time, using a very little amount of storage and computing resources. In a scenario where thousands of sensor nodes are placed, this system evaluates the time of each sensor in a distributed manner, assuming a very little percentage of nodes knowing their actual time, thus ensuring an effective clock synchronization among all the sensors.