Generating models for every aspect of a system is a labor-intensive and error-prone process. This research focuses on defining transformations from one type of system model to a different type of model, allowing developers to derive models of various system aspects easily. For example, one could transform an architectural model of a smart grid into a reliability model of that system. Any model that can be deterministically transformed must be deterministically specified; these deterministic specifications can be converted into abstract mathematical models. In this framework, transformations then become functions mapping one model to another. Formalizing modeling in this fashion allows us to apply a wealth of research on transforming mathematical structures.
Developed countries are entirely reliant on critical infrastructure, hence the need for rigorous assessment of the trustworthiness of these systems. The advanced electric power grid, also known as smart grid, is an example of a critical infrastructure that utilizes computation and communication to improve the efficacy and dependability of power generation, transmission, and distribution. Reliance of almost every other critical infrastructure on electric power and the interdependency between these infrastructure systems places smart power grids among the most critical and complex CPSs. The objective of this research is to develop and validate a comprehensive dependability model for cyber-physical critical infrastructures, focusing on smart power grids, and considering interdependencies among physical processes and the cyber network providing intelligence to the system. Such a model can help in guiding investments to eliminate or alleviate vulnerabilities in and failure mitigation for such critical infrastructures.
The dynamic, episodically connected nature of autonomous vehicles present significant challenges to resilience. The objective of this research is to create qualitative and quantitative models for the survivability of autonomous vehicles under faults and well correlated failures. This entails the investigation of autonomous vehicle vulnerabilities, which inform the prioritization of attacks based on their probability of success. The insights gained from experimentation will help fortify the platform against future attacks, through criticality analysis, with the goal of increasing survivability. Attack trees along with Bayesian Networks are used to model security, while a Discrete Time Markov Chain (DTMC) is used to model the state of the vehicle to inform survivability analysis.
Interdependencies among the cyber and physical components of a CPS significantly complicate the specification, modeling, and simulation of these systems, as it is difficult to represent both the cyber and the physical infrastructure in one integrated system. The interdependencies among the cyber and physical components and heterogeneity in the notion of "flow" invalidate and complicate traditional reliability models that assume components fail independently. We propose the use of agent-based modeling for qualitative representation of the operation of a CPS, as a precursor to quantitative modeling. Agent-based models can capture various facets of the operation of a CPS within a single agent, while reflecting the distributed nature of and interdependencies within the system through the use of multiple agents. Furthermore, such an abstraction facilitates failure mitigation through the use of software agents, which are autonomous and perceivably intelligent. The overarching objective of this project is to develop an understanding of failure propagation in CPSs, and to utilize the resulting insights to mitigate the effect of failures in cyber and/or physical components. Our focus is on critical infrastructure systems whose main function is the distribution of a tangible commodity, such as water or power. Physical components, e.g., valves, pipes, and reservoirs, of water distribution networks (WDNs), coupled with the hardware and software that support intelligent water allocation, comprise the model CPS that is used as a case study.
Advances in databases, distributed computing, computational intelligence, and especially pervasive computing, which allows non-intrusive and transparent "anytime, anywhere" access to information, provide fertile ground for radical changes in pedagogy. Recent studies of undergraduate engineering education have identified "linearity" of the dominant curricular model as fundamentally contradictory to the body of knowledge on how students learn. A networked model has been proposed, where the components form a cohesive and strongly interconnected whole, and learning in one area reinforces and supports learning in other areas. The objective of this project is to leverage technological advances, in particular in pervasive computing, to create a cyberinfrastructure that facilitates personalized learning in engineering education, while supporting a networked curricular model. We believe that such an infrastructure will accelerate the acquisition of knowledge and skills critical to professional engineering practice, while facilitating the study of how this acquisition comes about, yielding insights that may lead to significant changes in pedagogy.
As technology becomes increasingly available to highway travelers, an interesting variety of applications has emerged. Communication capability among vehicles, drivers, and other services has opened new doors for research that could improve the safety and efficiency of highway driving. Based on the observation that drivers’ travel habits are frequently repeated and can be accurately modeled, our work proposes that mobile devices collaborate to predict future traffic conditions based on travel habits. Our approach is unique in that mobile devices are collaborating to predict traffic conditions based on current and historical information, as opposed to only determining current traffic conditions or relying on a central server for predictions.
The Electromagnetic Interference (EMI) Detection group aims to develop a software based methodology to detect the presence of EMI and study the effects it has on software. Modern electronics are becoming more susceptible to EMI due to their rising clock speeds and smaller overall size which makes it ever so important for critical systems to understand how EMI affects them. Having the ability to understand when and how a device is being affected by EMI will allow critical systems to further harden against the effects of EMI and provide more reliable services. Furthermore, a software based analysis method allows for a better understanding of hardware that is infeasible to equip with laboratory sensors such as large-scale systems or consumer hardware. Our technique uses a lightweight watchdog to log changes in key registers and uses these registers values to construct states. When the device is active these states are recorded and used to give insight via categorical time series statistics and classification algorithms. Below you can find information about the device we use and specific detection methods.
This project encompasses the real-time monitoring, communications, and alerting capabilities of the Smart Brick, a low-cost, autonomous wireless device for structural health monitoring. It is a fully self-contained monitoring platform, requiring no additional on-site equipment to collect data and generate alerts. The Smart Brick has several sensors for monitoring temperature, water level, tilt, vibration, and acoustic emissions and can optionally be used with a digital camera, displacement sensors, load cells, strain gauges, or any number of other analog or digital sensors. It uses the widely-available GSM/GPRS network to send monitoring data and alerts to a number of users via FTP, SMS, and e-mail. Its rugged construction, completely wireless design, and extensive power-saving measures allow for a field life of 3-4 years powered only by alkaline batteries. This project is being currently developed into Standoff detection of IED emplacement using non-imaging wireless sensor networks. This project involves monitoring the environment for phenomena indicative of IED emplacement, using seismic, acoustic, magnetic, infrared, and RF sensors, among others. Expandable I/O and plug-and-play software support will allow for additional sensors to be integrated as needed. The advantage of these modalities over techniques based on imaging is their autonomy and significantly lower cost. Signal processing and data fusion will be employed for corroboration of data from multiple sensors, resulting in higher probability of detection. Communication capability allows for corroboration with offsite databases, and facilitates autonomous transmission of alerts. Hardware and software of the base system has been designed with the goal of ultra-low power consumption, which in conjunction with the harvesting of solar power, remote configuration and update, and physical robustness, allows long extended field life of the system.
This project is aimed at implementing data fusion algorithms for detection in real-time systems. Raw data from chemo metric spectroscopy will be processed and analyzed and results will be fed to data fusion algorithms at various levels for final detection.
The purpose of this project is to minimize the lighting energy consumption in the University of Missouri - Rolla. The aim of the project is to devise and install a real-time monitoring system which will keep track of the amounts of energy being expended in our university buildings. A suitable network architecture is designed to interface the lighting energy monitoring system with the Building Automation system as well as the campus Ethernet Backbone network. This type of architecture provides a convenient method to the facility managers to regularly monitor the power related information. The project facilitates a proper channel to estimate the correct amount of light energy savings that we will be having as part of the various lighting retrofits being done in the university buildings.
The objective of this project is the development of an embedded system for real-time tracking, recording and reporting of the location of mine detection dogs. The project is being carried out in collaboration with the US Geological Survey and the Engineer Canine Detachment of the Counter Explosive Hazard Center at Fort Leonard Wood. The research tasks include survey of related object tracking techniques, algorithm development for tracking the movements of the dog and its handler, and the design and development of an appropriate wireless network for communication of this data.
The objective of the project is to design and develop the next generation of research instrumentation for in situ hydrological monitoring of watersheds. More specifically, a system will be developed that autonomously measures various attributes of the watershed soil, including chemical composition, moisture, temperature, and resistivity. The measurements will be taken at several depths, and will be communicated to a processing server over the GSM cellular network.