Balanced Coordinated Algorithms for Damage Mitigation and Resource Allocation in Network Systems
Abstract
Growing complexity of current engineered cyber-physical networks makes them vulnerable to reconfigure their architectures for tolerating any unexpected outcomes during WMD attacks. The challenge lies in the co-design of cyber and physical components in a networked environment where the relationships between software and hardware components are always intertwined. Traditionally optimizing such a cyber-physical network system relies on the assumption that the coupling between cyber and physical components is weak enough so that the separation design principle can be used without losing too much optimality of performance. However, since highly complex interconnections are more and more ubiquitous in today’s cyber-physical network systems due to the increasing number of sophisticated devices embedded in the existing system, this design principle is facing a great challenge and may not be applicable for the cyber-physical network system design to produce certifiably dependable systems to counter WMD stressors. The goal of this proposed research is to explore this design issue for handling intertwined, complex connections between cyber and physical components in cyber-physical network systems. In particular, we need to develop some fundamental, insightful techniques on model analysis, control design, and computational verification for cyber-physical network systems. The design framework should be robust and resilient to both expected and unexpected WMD scenarios, and should be modular and scalable to highly parallel, non-deterministic cyber-physical architectures. To this end, we propose to develop new cost-effective, hybrid control techniques for stabilizing cyber-physical network systems under extreme events such as WMD attacks. Special attention will be given to analyze and robustly stabilize nonlinear cyber-physical systems. Moreover, we propose an optimal data flow strategy concerning the capacity constraints of the wireless links in the data networks, the price for capacity of links (in dollars per unit flow), and the data flow accuracy requirements in the links from the physical layers. Specifically, the data flow in a link should be designed to be within the capacity and be as small as possible to reduce transmit power and the load of the network, and data flow should also provide enough and accurate information in the physical layer to retain the performance of physical layer plants. On top of the physical layer, topological structure plays a significant role when considering convergence rates for resource allocation problems. Here we seek to provide some insights on the interaction between topological structure and resource allocation algorithms, and hence, better enhance the dissemination and self-healing ability for the network under WMD attacks. Moreover, to numerically address the design problems for the network, another aim of this research is to develop novel, robust swarm intelligence optimization algorithms to solve the topology based resource allocation problems. With the rapidly growing requirement for the large-scale network, accuracy and efficiency of an optimization algorithm are the two main considerations when choosing an optimization algorithm. As the third aim of this research, we will discuss several distributed parallel algorithms by using the recent parallel, brain-like supercomputing technologies to shorten the operation time for the optimization algorithm when addressing large-scale network problems. The proposed research will lead to the development of such fast-response, intelligent, autonomous systems that will make optimal use of limited resources and will operate robustly in extreme WMD environments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 26, 2016
- Source ID
- HDTRA11510070
Entities
People
- Qing Hui
Organizations
- Defense Threat Reduction Agency
- University of Nebraska–Lincoln