Robust Assured Paradigm for Information Distribution in Airborne Networks (RAPID-AN)

Mayflower Communication Company, Inc. (Mayflower) proposes a multilayer information distribution technology, the Robust Assured Paradigm for Information Distribution in Airborne Networks (RAPID-AN), to meet the Air Force objective, namely, to develop intelligent, efficient, and robust data distribution approaches for one-to-many and many-to-many data exchanges over airborne networks. The proposed Phase II effort focuses on the development, implementation and demonstration of the RAPID-AN technology. RAPID-AN is built with an intelligent framework that can distinguish different traffic types and provide different levels of QoS based on the existing policy context, and ensure their end-to-end delivery. RAPID-AN also contains network coding mechanism to provide high reliability and high throughput for one-to-many and many-to-many communications. The network coding mechanism has been designed to counter the effects of high dynamics and high-speed mobility. The RAPID-AN technology, which has proven feasible in the Phase I study for the Air Force application, has enormous potential for military and commercial applications. Mayflower will develop RAPID-AN prototype software in Phase II, and demonstrate its capabilities and performance on a heterogeneous radio test bed. Mayflower will, moreover, work with Airborne Network radio prime contractors to ensure the commercialization of RAPID-AN. BENEFITS: RAPID-AN would provide robust and efficient data distribution for the Air Force Airborne Network and therefore enable Single Integrated Air Picture (SIAP). RAPID-AN would interoperate with data distribution services in the Global Information Grid (GIG) to enable new net-centric warfare capabilities. RAPID-AN would enable civilian applications such as dynamic control of road traffic lights based on an integrated traffic picture produced by gathering and fusing traffic sensor data. RAPID-AN would also be an effective technology for first responders to gather and track data on large-scale disasters, and thereby enable accurate assessment and response to large-scale disasters.