Aging and degradation of transportation infrastructure pose significant safety concerns, especially in light of increased use of these structures. The economic downturn further exacerbates such concerns, especially for critical structures such as bridges, where replacement is infeasible and maintenance and repair are expensive. The US Federal Highway Administration has classified over 25% of the bridges in the United States as either structurally deficient or functionally obsolete, underscoring the importance of structural health monitoring (SHM) to ensure public safety. We give an overview of emerging wireless sensor networks (WSN) for autonomous SHM systems, their application, the power use and sources needed to support autonomy, and the type of communication that allows remote monitoring.

A smart grid is a cyber-physical system designed to achieve sustainability by facilitating the use of renewable energy sources, without compromising the reliability of power distribution and transmission. The research presented in this paper seeks to facilitate analysis of the reliability achieved by smart grids. To this end, we present an analytical reliability model that captures the effect of impairments originating from both physical and cyber components, as well as the effect of cyber-physical interdependencies among these components. The model can be instantiated with failure data from field use or simulation. We categorize and quantify dependencies in the smart grid and analyze the impact on reliability of introducing additional interdependencies. Finally, we present a case study that investigates the effect of physical and cyber improvements on overall reliability of the smart grid, and demonstrate that flawed cyber infrastructure can result in reliability lower than that of a conventional power grid with less advanced control.

The paradigm shift to commercial off-the-shelf components appears inevitable, necessitating drastic changes to current software development and business practices. Quality and risk concerns currently limit the application of COTS based system design to noncritical applications. New approaches to quality and risk management will be needed to handle the growth of CBSs. Our metrics based approach and software engineering metrics can aid developers and managers in analyzing the return on investment in quality improvement initiatives for CBSs. These metrics also facilitate the modeling of cost and quality, although we need more complex models to capture the intricate relationships between cost and quality metrics in a CBS.

The development of accurate models for cyber-physical systems (CPSs) is hampered by the complexity of these systems, fundamental differences in the operation of cyber and physical components, and significant interdependencies among these components. Agent-based modeling shows promise in overcoming these challenges, due to the flexibility of software agents as autonomous and intelligent decision-making components. Semantic agent systems are even more capable, as the structure they provide facilitates the extraction of meaningful content from the data provided to the software agents. In this paper, we present a multi-agent model for a CPS, where the semantic capabilities are underpinned by sensor networks that provide information about the physical operation to the cyber infrastructure. This model is used to represent the static structure and dynamic behavior of an intelligent water distribution network as a CPS case study.

In cyber-physical systems (CPSs), embedded computing systems and communication capability are used to streamline and fortify the operation of a physical system. Intelligent critical infrastructure systems are among the most important CPSs and also prime examples of pervasive computing systems, as they exploit computing to provide "anytime, anywhere" transparent services. The existing body of knowledge includes techniques for assessment, modeling, and simulation of physical and cyber infrastructures, respectively, but such isolated analysis is incapable of fully capturing the interdependencies that occur when they intertwine to create a CPS. Fundamental differences exist between the attributes of cyber and physical components, significantly complicating representation of their behavior with a single comprehensive model or simulation tool. This paper articulates the challenges present in integrated simulation of a CPS, where the goal is to accurately reflect the operation and interaction of the cyber and physical networks that comprise the system. A solution is presented for the CPS domain of intelligent water distribution networks, using EPANET and Matlab to represent the physical water distribution network and the decision support algorithms used to control the allocation of water, respectively.

The overarching objective of the proposed doctoral researches to build a qualitative and quantitative understanding of dependability in cyber physical systems (CPS).The existing body of knowledge includes frameworks and techniques for assessment, modeling, and simulation of the physical and cyber infrastructures, respectively, but such isolated analysis is incapable of fully capturing the interdependencies between these infrastructures. Understanding these interdependencies is a critical precursor to accurate representation and modeling of the CPS as a whole, especially with respect to dependability.The physical water distribution infrastructure, coupled with the hardware and software that support intelligent water allocation, comprise the model CPS that will be used as a case study for the proposed research. A preliminary literature review has been carried out on dependability modeling for CPS, with very sparse results. Allocation algorithms for water distribution have also been investigated,with game theory appearing to hold the most promise.An agent-based approach is suggested for linking the cyber and physical layers, where the agents retrieve information from sensors monitoring the physical components and provide this information to the cyber components. Fault injection will be used to investigate the propagation of failures between the cyber and physical layers. Markovian models will be used to capture the manifestation of cyber and/or physical faults as failures in water allocation, or containment of contaminants.