Award

Communication networks provide Command and Control with the necessary information and connections to their soldiers in the field required to execute their missions.  Ensuring these networks are available and optimized for the mission at hand is crucial to mission success.  However, the monitoring, testing, and design of these networks is a tedious, manual, and costly effort when performed but can be catastrophic if neglected.  Advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) offer an opportunity to apply these capabilities to network analysis including traffic engineering, dynamic path planning, and topology optimization.  Therefore, the Machina Cognita Technologies (MCT) and Epsilon team propose to lower the overall burden and cost of network analysis while also improving the accuracy of the analytics, optimization of the network design, and minimization of the impact of localized network outages and component failures.  To accomplish these goals, we propose to develop the AI and DL powered Testing Routines using AI for Communication Evaluation and Recommendations (TRACER) system.  The TRACER system will provide a Modular, Open Systems Approach (MOSA) to Test and Evaluation (T&E) of Command, Control, Communications, and Intelligence (C3I) systems.  The system will be a combination of the Test Automation Framework (TAF), an automated testing and scenario execution framework, and an AI/DL powered network analysis and recommendation engine.  The system will be able to provide descriptions of how the network is performing under test, forecasts of how the network will behave under various scenarios, and recommendations on how to improve the network to meet specific goals. The TRACER system will be composed of three major components and will connect to the network of interest (or System Under Test (SUT)) and provide results in an intuitive, easy-to-use interface.  The three major components are the TAF, the Analysis Engine, and the Recommendation Engine.  TAF will enable the automated testing and evaluation of the network through data injection, scenario management, simulation, and metric/data collection.  TAF will send the results of the automated testing along with the network metrics and topology to the Analysis Engine.  The Analysis Engine will utilize DL technologies to understand the performance of the network with regard to temporal fluctuations and the impact of the network topology and equipment on performance.  These results will then be passed to the Recommendation Engine that will generate specific, actionable, and understandable recommendations for modifications to the network along with expected impacts on performance.