Hardware architecture and a software framework, where the combination allows software to run.
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In this poster, we expose some of our recent results on authenatication of the Internet of things.
Our results include new machine learning approaches for authenaticating IoT devices based on their environment,
as well as novel ideas to distinguish malicious attacks from normal environmental changes. The poster also
presents some of our related works on IoT jamming and stochastic moving target defense in IoT-like environments.
Our extensive outreach activities within the scope of the GCTC challenge are also presented.
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Direct digital manufacturing (DDM) is the practice of creating a physical part directly from a computer-aided design (CAD) model with minimal process planning. DDM is typically applied in additive manufacturing (AM) processes, which offer rapid implementation and the ability to produce complex geometry. However, the treatment of subtractive manufacturing (SM) in the context of DDM is still lacking. SM is a decades- old process that is responsible for the production of many parts in the world today. SM and AM have distinct advantages and disadvantages.
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This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process.
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The vision of this work is to unite experts in granular mechanics, optimal control, and learning theory in order to define a methodology for advancing cyber-physical systems (CPS) involving a tight coupling of the physical with the cyber through dynamic interactions that must be learned online. The proposed work will advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain.
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SCALE (Safe Community Awareness and Alerting) is a Global Cities Challenge Project with multiple deployments, the primary one being in in Montgomery County, Maryland. SCALE2 is a community government/academic/industry partnership effort that aims to deploy, actuate and evaluate techniques to support multiple heterogeneous IoT technologies in real world communities. Through SCALE, significant public benefits can be enabled in the areas of emergency preparedness/response at the individual and community scale.
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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.
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Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges. In particular, and of special importance to the automotive industry, is the transition from conventional powertrains to (plug-in) hybrid and electric vehicles, all of which are subject to environmental and operational variations.
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Millions of mobile applications (apps) are being developed in domains such as energy, health, security, and entertainment. The US FDA expects that there will be 500 million smart phone users downloading healthcare related apps by the end of 2015. Many of these apps will perform interventions to control human physiological parameters such as blood pressure and heart rate. The intervention aspects of the apps can cause dependency problems, e.g., multiple interventions of multiple apps can increase or decrease each other's effects, some of which can be harmful to the user.
