The Center for Education and Research in Information Assurance and Security (CERIAS)
The Center for Education and Research inInformation Assurance and Security (CERIAS)
Principal Investigator: Shimon Nof
Conflicts and errors are unavoidable disruptions in complex networks such as energy grids, supply chains, and collaborative decision support systems. We aim to design and implement real-time, AI-based algorithms for effective and efficient automated conflict and error (CE) prevention, detection, and recovery (PDR) for the resilience and security of such complex systems. The relationships of different CE play an essential role: Local CE can propagate to large-scale system damage according to CE dependencies if not handled correctly and in time. On the other hand, the structural information of the CE network can improve PDR operations. Constraint-based models are designed based on the complex network theory to define CE dependencies and provide prescriptive abstractions for real-world systems. A centralized algorithm taking advantage of network structure, a decentralized algorithm enabling parallelism with distributed PDR agents, and hybrid algorithms are designed to prevent, detect, and recover from CEs. The established and new algorithms use relationships between CE constraints to improve efficiency. Analytical studies and simulation experiments on various systems have been conducted to validate the latest algorithms and compare their performance to that of traditional algorithms. Results show that for effective PDR, new algorithms shall be used according to several performance measures: Response time, coverage ability, preventability, and damage minimization. The machine learning and alignment between algorithms and network characteristics, i.e., centralized algorithms for centralized networks and decentralized algorithms for decentralized networks, improve PDR. During the PDR operations, the collaboration between PDR agents also needs to be efficiently coordinated and optimized to minimize the potentially cascading effects of CE.
Other PIs: Hao Zhong Xin W. Chen Rodrigo Reyes Levalle Win P.V. Nguyen
Other Faculty: Xin W. Chen Rodrigo Reyes Levalle Win P.V. Nguyen
Students: Vivek Sangani
Landry, S. J., Chen, X. W., & Nof, S. Y. (2013). A decision support methodology for dynamic taxiway and runway conflict prevention. Decision Support Systems, 55(1), 165-174.
Zhong, H., Nof, S. Y., Filip, F. G. (2014) Dynamic Lines of Collaboration in CPS Disruption Response, 19th IFAC World Congress, August 24-29, 2014, Cape Town, South Africa.
Xin W. Chen, Steven J. Landry, Shimon Y. Nof: A framework ofenroute air traffic conflict detection and resolution through complex network analysis. Computers in Industry 62(8-9): 787-794 (2011)
Xin W. Chen, Shimon Y. Nof: Conflict and error prevention and detection in complex networks. Automatica 48(5): 770-778 (2012)
Chen, X. W. and Nof, S. Y. (2012), Agent-based error prevention algorithms. Expert Syst. Appl. 39, 1 (2012), 280-287.
Hao Zhong, Shimon Y. Nof, Dynamic Lines of Collaboration: Disruption Handling & Control, 2020, Springer ACES Series, Vol. 6
Rodrigo Reyes Levalle, Resilience by Teaming in Supply Chains and Networks, 2018, Springer ACES Series, Vol. 5
Reyes Levalle, R., and Nof, S.Y. “Resilience by Teaming in Supply Network Formation and Re-Configuration.” Int. J. Production Economics 160, 2015, 80–93.
Reyes Levalle, R. and Nof, S.Y., “Resilience in supply networks: Definition, dimensions, and levels,” Annual Reviews in Control, 43, 2017, 224-236.
Nguyen, W.P.V., and Nof, S.Y. Resilience Informatics for Cyber-augmented Manufacturing Networks (CMN): Centrality, Flow, and Disruption,” Studies in Informatics and Control, 27(4), 2018, 377-384.
Nguyen, W.P.V., and Nof, S.Y. Collaborative response to disruption propagation (CRDP) in cyber-physical systems and complex networks, Decision Support Systems, 117, 2019, 1-13.
Nguyen, W.P.V., and Nof, S.Y. “Strategic lines of collaboration in response to disruption propagation (CRDP) through cyber-physical systems.” Int. J. Production Economics, 230, p. 107865, 2020.
Keywords: AI-based collaborative control, Complex systems, Detection algorithms, Error detection, Network topologies, Prevention