Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

An efficient model-based method for coordinated control of urban traffic networks

Traffic control is an effective and also efficient approach to reduce traffic jams. To alleviate the traffic congestion in an urban traffic network, a traffic control strategy that can coordinate the whole traffic network from a global point of view, is required. In this paper, an advanced control strategy, i.e. Model Predictive Control (MPC), is applied to control and coordinate urban traffic networks. However, the on-line computational complexity becomes a big challenge when the scale of the traffic network gets larger. To overcome this problem, the MPC control strategy is reformulated and solved efficiently on-line by a Mixed-Integer Linear Programming (MILP) solver. An MPC controller based on MILP is established and studied for the urban traffic network in different traffic scenarios. The simulation results show that the MILP-based MPC controller is a promising approach to reduce the on-line computational complexity of MPC controllers for urban traffic networks