The smart grid is a large-scale, societal-level hybrid cyber-physical system with tight coupling between cyber and physical components.  Ensuring availability and reliability of power requires maintaining stability of the power grid even as increasing demand and uncertain renewable power sources push the power system close to its operation limit.  In addition, the cyber-enabled grid has multiple entry points, leaving it highly susceptible to cyber attacks by malicious adversaries.

Computational simulation is one of the most im- portant ways of reproducing the dynamic responses of a Cyber Physical System using a model of the system. The simulation discovers areas of differential system performance and allows linking such performance back to system characteristics. In the medical domain, patient simulators are used to train physicians in patient management. One critical question is how to verify these systems under realistic human (physician) input.

Modern cyber-physical systems are monitored and controlled by multi-core platforms, and thermal management of multi-core chips is critical as overheated cores thereon will suffer from exponentially decaying lifetime and unacceptable performance degradation. To meet the timing and system lifetime reliability requirements under dynamic workloads and operating environment, we need a real-time thermal management (RTM) scheme that predicts run-time temperature and actuates effective thermal control without compromising task deadlines.

Cyber-Physical Systems (CPS) that contain self-modifying smart components can improve and self-repair, but sometimes at the cost of impeding model-based Verification and Validation (V&V).  In this work, we focus on maintaining short and long range V&V capability in a system containing self-adaptive smart components. In this work, we focus on smart component based in-flight control adaptation of damaged Flapping-Wing Micro Air Vehicles (FW-MAV).

 Despite their importance within the energy sector, buildings have not kept pace with technological improvements and particularly the introduction of intelligent features. A primary obstacle in enabling intelligent buildings is their highly distributed and diffuse nature. To address this challenge, a modular approach will be investigated for building design, construction, and operation that would completely transform the building industry.

The goal of this project is to create a scalable and robust cyber-physical system (CPS) framework for the observation and control of the functional interdependencies between bridge structures (stationary physical systems) and trucks (mobile physical agents). A CPS framework (Figure 1) is being developed to monitor and control trucks within a single highway corridor to manage the imposed loads and the consumption of structural life by trucks on highway infrastructure including bridges. The framework begins with the installation of wireless sensing

The current lack of toolchain for high confidence testing, validation and verification of advanced, connected and automated/autonomous vehicles can impede and even entirely prevent the introduction of such vehicles into mass production. To address this challenge, this projects develops theory, methods, and tools for generating and optimizing test trajectories and data inputs that can maximize opportunities to uncover faults in both physical and cyber domain in future automotive vehicles.

Episodic brain disorders such as epilepsy have a considerable impact on a patient’s productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. We will create a second generation brain-implantable sensing and stimulating device (BISSD) based on CPS principles and practice. The BISSD will be composed of modules placed intracranially to continuously monitor brain state and vulnerability to seizure and intervene with electrical stimulation to block the development of seizure.

Abstract: Robustness Guided Testing and Verification for Cyber-Physical Systems PI: Georgios Fainekos School of Computing, Informatics and Decision System Engineering, Arizona State University. E-mail: fainekos@asu.edu Website: http://www.public.asu.edu/~gfaineko

The powertrain control problem is one of regulating the air-to-fuel ratio in an automotive engine. A series of models of such controllers, with increasing levels of sophistication and fidelity to real-world designs, have been recently proposed by Toyota researchers as challenge problems for today’s verification technologies.