Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks

Abstract

Modelling based on probabilistic inference can be used to estimate the quality of information delivered by a military sensor network. Different modelling tools have complementary characteristics that can be leveraged to create an accurate model open to intuitive and efficient querying. In particular, stochastic process models can be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The quality of information may be assessed by using the model to compute the probability that reports made by the network to its users are correct. In contrast, dynamic Bayesian network models, which have been used in a variety of military applications, are a more suitable vehicle for understanding the overall network performance and making inferences about the quality of information. Queries can be made simply by instantiating some variables and computing the probability distributions over others. We show that it is possible to combine both modelling tools by constructing a Bayesian network over the state variables of the process algebra model. The sparsity of the resulting Bayesian network allows fast propagation of probabilities, and hence interactive querying for the quality of information.