Semantic-based bandwidth reduction in wide area training networks

Data filtering is a semantic-based technique that can significantly reduce the size of traffic in large-scale distributed interactive simulation (DIS) networks. If not carefully designed, filtering algorithms could lead to errors that may compromise the realism of the training exercise. In this paper, we review the problem of filtering errors and present an approach suitable for preventing filtering errors