Signal change detection in sensor networks with artificial neural network structure

The paper describes a design and implementation of a novel intelligent sensor network protocol enhancing reliability and security by detecting a change in sensor signals. The change could be caused by the sensor or communication unit malfunctioning or by malicious altering of a measurement result. The protocol utilizes a neural network function prediction methodology to predict sensor outputs in order to determine if the sensor outputs have changed. The parameter choice and the relationship between the threshold values and false alarm and missing attack rates are studied. The protocol is implemented and tested in real life environments with sensor networks built from Crossbow MICA motes. The test results are analyzed and recommendations on applications are provided