Reconfiguration of Component Level Control Network Automation Systems

"This project will develop and implement in embedded hardware the capability needed to configure pre hit, a component level automation infrastructure.This approach optimizes survivability using models of damage or casualty scenarios. This capability willbe demonstrated in a laboratory hardware in the loop simulation. This project leverages the results of several other ongoing research efforts all focused around survivable component level systems based on the ANSI 709.1 protocol.This work further extendsthe dependable topology with network fragment healing concepts.BENEFITS: This project brings together three important enabling technologies; model based reasoning, network fragment healing, and high speed component level embedded distributed processing, toproduce the unprecedented capability, that is, to predict and configure "pre-hit" a component level automation infrastructure to minimize vulnerability and maximize survivability from real time threat and damage information. This approach provides thepotential for decreased latencies in healing and better fight through capability for highly automated (low manned) systems. Moreover, the demonstrated cost effectiveness of the dependable topology with NFH makes it affordable and therefore a primecandidate for future Naval automation ship infrastructure. This project will produce, the first hardware in the loop embedded system capable of pre-hit reconfiguration of a component level network. This will provide the crucial scalable infra-structureneeded for advanced survivable component level networks.The principle military application will be the component level automation infra-structure on board a Naval platform.There are three major immediate commercial opportunities that will be enabled by this project: 1) Load balanced router for optimized component level networks. 2) Combined router/IP gateway with sophisticated routing algorithms. 3) Resilient survivableautomation system for critical services using model based reasoning for state estimation and reconfiguration."