Message Prioritization in Contested And Dynamic Tactical Networks Using Regression Methods and Mission Context

Abstract

Military communications at the tactical edge consists of unreliable, disrupted, and limited bandwidth networks, which can lead to the delay and loss of critical information. These networks are increasingly being used for the transmission of digital command and control (C2) information, requiring timely and accurate transmission, and play a vital role in the outcome of military operations. Machine Learning (ML) techniques have the potential to improve operational outcomes by autonomously prioritizing the delivery of the most important information through these networks, using observations of the current mission and network state. This paper covers the experimental process and the operational metric used for comparison between the ML and a non-ML approach that sorts messages in a fixed order. We present two regression-based supervised-learning methods that were shown to be more effective in both medium and high congested networks than the non-ML approach.