The prevalence of battery-powered systems such as electric vehicles, smartphones, and IoT devices has made batteries crucial to everyone's daily life and business. Battery health, however, degrades over time, not only decreasing system reliability such as unexpected system shutoffs, but also causing overheating/gassing which, in turn, increases safety risks such as thermal runaway or even battery fire/explosion. To address these problems, we must monitor, prognose, and optimize battery health throughout the physical system life. However, existing battery management systems (BMSes) are usually treated as complementary system components attached/embedded to/in batteries, and are unable to make optimal health management decisions adaptively based on system dynamics or user requirements. Our approach tightly integrates the cyber (battery management software) and the physical (sensing of battery state) to enable significant improvements in battery life and performance. The research, outreach, and education activities of this project will have broader impacts on the CPS research and industry communities, bridging the gap between them, providing environment-friendly solutions, increasing the awareness of CPS, and developing skilled human resources. It will also make significant economical and environmental impacts by enabling longer battery life and improved performance. This project will develop R-AWARE, a recovery period-assisted battery health management that schedules system operation while considering both system/user requirements and battery health. R-AWARE will improve battery health via relaxation-aware battery scheduling of battery charging/discharging, and recovery-based thermal control. It will advance the science of CPS by uncovering a thorough understanding of battery recovery and exploiting it via a recovery-aware scheduler during system operation. Specifically, R-AWARE is grounded on a thorough understanding of the physical recovery effects on battery health -- a new dimension for system optimization existing BMSes fail to exploit. R-AWARE then takes a cyber-physical approach for battery health management by adapting its decisions based on various physical properties of batteries/systems/environment. Finally, R-AWARE improves battery health by exploiting the opportunities offered by user behavior/requirements without degrading user experience. A performance goal of the project is to enable a 40% slowdown in battery health degradation.

In Cyberphysical Systems (CPS) such as large scale infrastructure systems, individual users are shifting from being passive consumers of services to active participants. This shift promises societal, economic, and environmental benefits. For example, turning consumers into "prosumers" through distributed renewable energy integration can improve sustainability, and turning users into sources of data about traffic and road conditions can help alleviate congestion. However, an explosion of decision makers also leads to heterogeneity in concerns, aims, and quality of decision making. Participants will optimize their decisions on data collection, data sharing, and actuation based on their own self-interest. In an interconnected system, these decisions will affect the system state, and hence, the utilities other participants derive from the system. In extreme cases, undesirable and unintended consequences such as instability can also ensue from actions of a few decision makers. Thus, there is an urgent need to understand and modulate decision making by various participants so that they internalize the impact on system performance and utilities of the other participants that their decisions produce; the proposed research solves basic problems in this domain. The proposed research has potentially substantial social and economic impact. The general trend in CPS systems, such as infrastructure systems, is to allow users to become active participants. However, this will challenge the traditional algorithms and organization for such systems. Since most CPS systems provide basic services to the population, designs that guarantee reliable operation in spite of strategic behavior of self-interested participants are needed. The research is coupled with an education plan that includes integration of research and education, curriculum design, student advising and training, as well as outreach. Existing approaches such as cooperative control or competitive game theory in which participants are price takers are insufficient to consider strategic behavior by participants. Motivated by this observation, this proposal will advance the science of CPS by (i) abstracting this problem into one of designing contracts by which the decisions of strategic users, who may anticipate the effect of their actions and misreport any information, can be aligned to obtain desired system performance, (ii) presenting new mechanisms for this model under several assumptions inspired by large scale CPS such as smart infrastructure systems, and (iii) demonstrating the efficacy of this framework by applying it to several CPS case studies. The proposed approach couples optimization and systems theory with economics and mechanism design as well as models and constraints from specific infrastructure systems.

As evidenced by the recent cyberattacks against Ukrainian power grids, attack strategies have advanced and new malware agents will continue to emerge. The current measures to audit the critical cyber assets of the electric power infrastructure do not provide a quantitative guidance that can be used to address security protection improvement. Investing in cybersecurity protection is often limited to compliance enforcement based on reliability standards. Auditors and investors must understand the implications of hypothetical worst case scenarios due to cyberattacks and how they could affect the power grids. This project aims to establish an actuarial framework for strategizing technological improvements of countermeasures against emerging cyberattacks on wide-area power networks. By establishing an actuarial framework to evaluate and manage cyber risks, this project will promote a self-sustaining ecosystem for the energy infrastructure, which will eventually help to improve overall social welfare. The advances in cyber insurance will stimulate actuarial research in handling extreme cyber events. In addition, the research and practice related to cybersecurity and cyber insurance for the critical energy infrastructure will be promoted by educating the next generation of the workforce and disseminating the research results. The objective of this project is to develop an actuarial framework of risk management for power grid cybersecurity. It involves transformative research on using insurance as a cyber risk management instrument for contemporary power grids. The generation of comprehensive vulnerabilities and reliability-based knowledge from extracted security logs and cyber-induced reliability degradation analysis can enable the establishment of risk portfolios for electric utilities to improve their preparedness in protecting the power infrastructure against cyber threats. The major thrusts of this project are: 1) developing an approach to quantifying cyber risks in power grids and determining how mitigation schemes could affect the cascading consequences to widespread instability; 2) studying comprehensively how hypothesized cyberattack scenarios would impact the grid reliability by performing a probabilistic cyber risk assessment; and 3) using the findings from the first two thrusts to construct actuarial models. Potential cyberattack-induced losses on electric utilities will be assessed, based on which insurance policies will be designed and the associated capital market will be explored.

Additive manufacturing is finding increased application in industry. Safety-critical products, such as medical prostheses and parts for aerospace and automotive industries are being printed by additive manufacturing methods, but there currently are no standard methods for verifying the integrity of the parts that are produced. Trustworthy operation of industrial additive manufacturing depends on secure embedded controllers that monitor and control the underlying physical manufacturing processes. This research will investigate a perfectly air-gapped intrusion detection solution for cyber-physical industrial additive manufacturing infrastructures in which some of the controllers may be infected by malicious code. The research will provide guidelines to: i) tie together resilience solutions in software security, control system design, and signal processing, and ii) incorporate reliable and practical cyber-physical attack detection into real-world manufacturing. Educational and technology transfer activities will address the need to improve the applicability of training methods to ensuring the safety and cyber security of physical control systems. Activities will involve The Society of Women Engineers and a large population of underrepresented and low-income minorities with diverse cultural backgrounds and improve the security of existing, real-world, additive manufacturing systems in industry. Next generation cyber-physical additive manufacturing enables advanced product designs and capabilities, but increasingly relies on highly networked industrial control systems that present opportunities for cyber attacks. The predominant approach to defending against these threats relies on host-based intrusion detectors that sit within the same target controllers, and hence are often the first target of the controller attacks. This project will research contact-less and perfectly air-gapped intrusion detection by analyzing physical side-channels to protect against cyber-physical attacks. This solution requires no runtime overhead on real-time controllers, requires minimal change to legacy systems, and reliably identifies intrusions even if the target system is completely compromised. The work will address solutions for: i) air-gapped intrusion detection on cyber-physical systems while maintaining a perfect air gap, ii) a comprehensive understanding of the types of side-channels available for analysis in different industrial systems, and iii) empirical validation of the various perfectly air-gapped intrusion detection tools, both independently and working in tandem.

Close to one million lives could be saved each year in the United States alone by organ transplantation if a sufficient number of organs were available, potentially preventing 35% of all deaths in the nation. In contrast, due to critical shortages of organs, only about 28,000 organ transplants are performed each year, with a waiting list of 120,000 people. A promising potential solution to this shortage is the high quality and production-scale 3D printing of human organs by bio-additive manufacturing (Bio-AM). However, as articulated in the 2016 NSF workshop on Additive Manufacturing for Healthcare, the current use of Bio-AM is impeded by poor organ quality, resulting in part from inadequate process monitoring and lack of integrated process control strategies. As a result, despite enormous strides, it is still not possible to scale Bio-AM to the stringent quality standards mandated for organ transplants. This research will address the compelling need to incorporate advanced process models into sensor-based process control strategies needed to prevent cell damage, decrease cell placement errors, and improve tissue functioning in Bio-AM. If successful methods for reliable, high-volume, high-quality, and safe Bio-AM can be realized, it will have profound socioeconomic benefits in terms of public health, medical safety, and drug discovery. The project will engage grade 6-12 STEM teachers through the Research Experiences for Teachers (RET) Innovation-based Manufacturing Program by providing opportunities for teachers to engage in cutting edge research in Bio-AM. The goal of the project is to reliably produce viable 3D printed biological constructs (mini-tissues). The central approach is to couple in-situ heterogeneous sensor-based monitoring and real-time closed-loop process control approaches for ensuring the reliable printing of biological constructs. The work involves the following four objectives: (1) using experimentation and modeling to understand the causal effect of process-material interactions on specific Bio-AM defects, (2) employing sensors to detect incipient defects during printing, (3) diagnosing the root causes of detected defects by analyzing sensor data using real-time decision-theoretic models, and (4) preventing propagation of defects through closed-loop process control. The investigation will contribute: (1) fundamental understanding of the causal bio-physical process interactions that govern the quality of printed biological tissue constructs through empirical investigation and sensor-based data analytics, (2) new mathematical models for predicting the layer quality by taking into consideration the complex and dynamic tissue maturation phenomena, (3) real-time and computationally efficient decision-making for accurate classification of defects from sensor data, and (4) a two-stage, real-time, closed-loop quality control approach for preventing propagation of defects by executing smart corrective actions during the printing process.

In Cyberphysical Systems (CPS) such as large scale infrastructure systems, individual users are shifting from being passive consumers of services to active participants. This shift promises societal, economic, and environmental benefits. For example, turning consumers into "prosumers" through distributed renewable energy integration can improve sustainability, and turning users into sources of data about traffic and road conditions can help alleviate congestion. However, an explosion of decision makers also leads to heterogeneity in concerns, aims, and quality of decision making. Participants will optimize their decisions on data collection, data sharing, and actuation based on their own self-interest. In an interconnected system, these decisions will affect the system state, and hence, the utilities other participants derive from the system. In extreme cases, undesirable and unintended consequences such as instability can also ensue from actions of a few decision makers. Thus, there is an urgent need to understand and modulate decision making by various participants so that they internalize the impact on system performance and utilities of the other participants that their decisions produce; the proposed research solves basic problems in this domain. The proposed research has potentially substantial social and economic impact. The general trend in CPS systems, such as infrastructure systems, is to allow users to become active participants. However, this will challenge the traditional algorithms and organization for such systems. Since most CPS systems provide basic services to the population, designs that guarantee reliable operation in spite of strategic behavior of self-interested participants are needed. The research is coupled with an education plan that includes integration of research and education, curriculum design, student advising and training, as well as outreach. Existing approaches such as cooperative control or competitive game theory in which participants are price takers are insufficient to consider strategic behavior by participants. Motivated by this observation, this proposal will advance the science of CPS by (i) abstracting this problem into one of designing contracts by which the decisions of strategic users, who may anticipate the effect of their actions and misreport any information, can be aligned to obtain desired system performance, (ii) presenting new mechanisms for this model under several assumptions inspired by large scale CPS such as smart infrastructure systems, and (iii) demonstrating the efficacy of this framework by applying it to several CPS case studies. The proposed approach couples optimization and systems theory with economics and mechanism design as well as models and constraints from specific infrastructure systems.

Additive manufacturing is finding increased application in industry. Safety-critical products, such as medical prostheses and parts for aerospace and automotive industries are being printed by additive manufacturing methods, but there currently are no standard methods for verifying the integrity of the parts that are produced. Trustworthy operation of industrial additive manufacturing depends on secure embedded controllers that monitor and control the underlying physical manufacturing processes. This research will investigate a perfectly air-gapped intrusion detection solution for cyber-physical industrial additive manufacturing infrastructures in which some of the controllers may be infected by malicious code. The research will provide guidelines to: i) tie together resilience solutions in software security, control system design, and signal processing, and ii) incorporate reliable and practical cyber-physical attack detection into real-world manufacturing. Educational and technology transfer activities will address the need to improve the applicability of training methods to ensuring the safety and cyber security of physical control systems. Activities will involve The Society of Women Engineers and a large population of underrepresented and low-income minorities with diverse cultural backgrounds and improve the security of existing, real-world, additive manufacturing systems in industry. Next generation cyber-physical additive manufacturing enables advanced product designs and capabilities, but increasingly relies on highly networked industrial control systems that present opportunities for cyber attacks. The predominant approach to defending against these threats relies on host-based intrusion detectors that sit within the same target controllers, and hence are often the first target of the controller attacks. This project will research contact-less and perfectly air-gapped intrusion detection by analyzing physical side-channels to protect against cyber-physical attacks. This solution requires no runtime overhead on real-time controllers, requires minimal change to legacy systems, and reliably identifies intrusions even if the target system is completely compromised. The work will address solutions for: i) air-gapped intrusion detection on cyber-physical systems while maintaining a perfect air gap, ii) a comprehensive understanding of the types of side-channels available for analysis in different industrial systems, and iii) empirical validation of the various perfectly air-gapped intrusion detection tools, both independently and working in tandem.

The proposed research focuses on innovation in the area of detection and separation of anomaly in sensory data used for real time monitoring and control of cyber-physical systems (CPS). In this work, bulk power grid is considered as an example CPS, which is a critical infrastructure of our nation. The problem of spurious or maliciously injected sensor data originating from cyber-attacks is very important, because it can seriously jeopardize the monitoring and stabilization controls of power grids. This can lead to system-wide blackouts and cost our economy billions of dollars. This project aims to solve this problem by leveraging the system's expansive dynamic behavior to distinguish disturbances from data anomalies. To that end, the aim of this research is to bridge the gap between the developments in the area of singular value perturbation theory and Principal Component Analysis (PCA) -- traditionally focused on the 'signals' side of the CPS, with the intrinsic properties from the 'systems' side of the CPS. Fusion of sensory data with the dynamic properties of the physical system will help in gaining fundamental insight into the coupling between the cyber and the physical layer and use this knowledge to detect and separate spurious signals or malicious data manipulations originating from cyber-attacks. A successful conclusion of this project will quantify the effect of Phasor Measurement Unit signature anomalies on Principal Components in the higher versus lower dimensional subspaces. In addition, emerging concepts of Robust PCA will be explored to separate the anomalous signatures and correct the data for real-time application. In today's world of 'Industrial Internet' where sensory data is being utilized for system health monitoring and controls, this work has the potential to make significant strides in understanding data quality and anomaly. Therefore, a successful completion of this project can help protect critical infrastructures from cyber-attacks and facilitate improved system diagnosis, lower downtime, better service, and higher resiliency. The proposed research can potentially benefit a wide range of CPS including process control, oil and gas, energy, and probably other systems involving robots or even future transportation systems employing autonomous vehicles. This project will integrate its research outcome with power systems course modules to broaden the undergraduate experience on the newer developments in energy CPS. In addition, this project will integrate the proposed research into iTech: Summer Technology Camp for Teens, which is a free week-long interactive day camp, designed to introduce high school students (9th -12th grades) to information technology. All materials created and developed in this project including software data, primary research data and metadata, and teaching material will be publicly available on an open source basis at the following site: https://sites.psu.edu/nilanjan/. For faster dissemination of the research results, published papers will be made available in PDF format in this site.

In a cyber-physical system, the operation of the physical plant is typically maintained by closed-loop control, which is intended to keep the plant process variables in a desired range. A major part of any control system is its instrumentation, i.e., sensors and actuators. Due to information exchange between the controller and the instrumentation, the control system performance may be compromised by attacks on its sensors and actuators. Indeed, the sensors may project erroneous information to the controller and the actuators may receive undesirable commands, possibly leading to a catastrophe. In this research, the control system is referred to as resilient, if it identifies attacks on the sensors and actuators involved in the feedback loops and mitigates undesirable effects. The goal of the proposed research is to develop a theory for analysis and design of resilient control systems with respect to attacks on the instrumentation and to demonstrate its efficacy using the High Performance Building Testbed at the United Technologies Research Center. The broader impact of the project is in its effect on cyber-security of critical infrastructure systems, such as power, telecommunications, transportation, high performance buildings, gas, oil, and water. In this project, we consider control systems, in which the sensors and actuators may be under various types of instrumentation attacks through transfer function modification and/or external deception signal injection. This project include developing the following techniques: A method for system vulnerability evaluation with respect to various instrumentation attacks; a method for optimal controller design to minimize performance degradation under attacks, while meeting desired performance specifications under non-attacked condition; a method for actuator/sensor health assessment in control systems using the synchronous detection approach; a method for design of resilient feedback controllers, driven by the actuator and sensor health to detect, identify, and mitigate instrumentation attacks; a method for knowledge fusion for process variable estimation based on multiple sensors and control signal calculation based on the knowledge fusion results. The project will significantly enhance the field of CPS from the point of view of resiliency.

A cyberphysical system (CPS) in biology requires sensor input that represents, as closely as possible, cell activity. Much work is expended on the development of wearable sensors that detect the expression of cell activity filtered through many processes. Recent work discloses that gene transcription can be thought of as a signal, with periodic oscillations over time. The well-known 24 hour light-dark cycle has protean effects however shorter and longer cycles not only exist but have important roles to play in health and disease. Detection of these signals and their perturbation is likely to be of great use in a robust health focused CPS. The exact nature of these signals and the mathematical structure underlying them will form the basis of this proposal. The societal impacts go beyond the new sensors to include the development of open source methods allowing the dissemination of new mathematical models and insights. into measurement of cellular processes. This proposal addresses the critical problem of generating cell-level physiologic data as a substrate for an effective CPS in health. Applying new, unbiased signal processing techniques, the team has recently identified new periodicity in RNA over time. This signal provides a robust insight into cell function and its changes. The team will address the ability of the new techniques in specific situations to uncover signals to be used as inputs for a human health CPS sensor. This signal processing technique will be used to identify oscillations in genes associated with defined chronic metabolic diseases of humans such as diabetes, inflammation, and cancer). These candidate genes will be used to construct a precision signature for input into a CPS sensor. The concepts and data will be used to construct mathematical equations describing the longitudinal DNA transcripts previously identified. Taken together, these two activities will provide an integrated mathematical picture of periodic gene transcription that then sets the stage for novel sensor design that will provide prediction and control in a human-based CPS. The project will develop a new platform for understanding the cell that will be made widely available via a Web-based open source platform.