Knowledge Based System for Reliable Perimeter Protection Using Sensor Networks
Faculty of Computing, Health and Science
School of Engineering
This paper provides a strategy for perimeter protection using sensor networks with hardware and analytical redundancy. The sensor network reliability is augmented using a knowledge-based system, which implicates the analysis of the trustworthiness of each sensor. For this, we used two stratagems: one that relies on hardware redundancy based on the Confidence Weighted Voting Algorithm and one that relies on analytical redundancy based on a neural perceptron predictor that uses past and present values obtained from neighbouring nodes. This solution can be also a way to discover the malfunctioning nodes that were subjects of an attack and it is localized at the base station level being suitable even for large-scale sensor networks.