Large-scale networked systems (such as the power grid, the internet, multi-robot systems, and smart cities) consist of a large number of interconnected components. To allow the entire system to function efficiently, these components must communicate with each other and use the exchanged information in order to estimate the state of the entire system and take optimal actions. However, such large-scale networked systems are also increasingly under threat from sophisticated cyber-attacks that can compromise some of the components and cause them to behave erratically or inject malicious information into the network. Existing algorithms for distributed coordination in large-scale networks are highly vulnerable to such attacks. This project will address this critical problem by creating new algorithms to enable components in large-scale networks to cooperatively take optimal actions and estimate the state of the system despite attacks on a large number of the components. The algorithms will provide provable security and performance guarantees, and identify characteristics of networks and algorithms that are vulnerable to attacks. The project will identify new ways to design networks that provide a desired level of resilience to attacks. The algorithms that arise from the research will enable the design of more secure networks and critical infrastructure that remain functional under attacks, with substantial benefits to society. In addition to the technical and scientific contributions, the project will also train students in the design of secure networked systems, and will engage the local community in central Indiana in learning about networks via interactive exhibits and workshops at the local museum. This proposal presents an integrated research and education program focused on establishing the foundations of distributed optimization, learning, and estimation algorithms that are resilient to attacks. The research agenda is focused along three thrusts: (i) designing resilient algorithms for distributed optimization of static objective functions, (ii) designing resilient learning algorithms for settings where optimization objectives change over time, and (iii) designing resilient distributed state estimators for large scale dynamical systems. The three research thrusts each lead to new theoretical contributions. First, the proposed research will establish new metrics for measuring resilience in distributed optimization algorithms, and will build upon commonly studied optimization approaches (which are highly vulnerable to adversaries in their existing forms) to derive resilient distributed optimization algorithms. Second, it will establish new fundamental lower bounds on the regret that can be achieved with distributed online learning algorithms under adversarial behavior, and characterize achievable regret bounds via the design of new learning algorithms. Third, the proposed research will investigate the interplay between the dynamics of underlying physical systems and the communication network topology between distributed observers in order to design resilient distributed state estimation schemes. The proposed research will lead to a greater understanding of the fundamental factors that affect the resilience of distributed optimization, learning, and estimation dynamics, and establish systematic procedures to design large-scale networked systems that are capable of operating in a near-optimal manner under attacks. Given the lack of existing work on this topic, the research will lay the groundwork for substantial further explorations of resilient algorithms for distributed decision-making and coordination in large-scale networks.

The proposed effort will help develop systems in which humans and autonomy are responsible for collective information acquisition, perception, cognition and decision-making. Such collective operation is a necessity as much as it is an augmenting technology. In assistive robotics, for example, the autonomy exists to support functionality that the human users cannot perform. On the other hand, in cases in which a human can adequately operate a platform (e.g., semi-autonomous unmanned vehicles), she effectively augments the robot's abilities. Establishing provable trust is one of the most pressing bottlenecks in deploying autonomous systems at scale. Embedding a human as a user, information source or decision aid into the operation of autonomous systems amplifies the difficulty. While humans offer cognitive capabilities that complement machine implementable functionalities, the impact of this synergy is contingent on the system's ability to infer the intent, preferences and limitations of the human and the imperfections imposed by the interfaces between the human and the autonomous system. We expect the proposed theory, methods and tools to cut across the spectrum of cyberphysical systems that are to work with and in the vicinity of humans. Such systems include, to name a few, human-robot interactions, a range of assistive medical devices, semi-autonomous driving or safety augmentation systems in modern automobiles and control rooms of large-scale plants. The proposed effort targets a major gap in theory and tools for the design of human-embedded autonomous systems. Its objective is to develop languages, algorithms and demonstrations for the formal specification and automated synthesis of shared control protocols. Our technical approach is based on bridging formal methods, controls, learning and human behavioral modeling. It is based on three main research thrusts. (i) Specifications and modeling for shared control: What does it mean to be provably correct in human-embedded autonomous systems, and how can we represent correctness in formal specifications? (ii) Automated synthesis of shared control protocols: How can we mathematically abstract shared control, and automatically synthesize shared control protocols from formal specifications? (iii) Shared control through human-autonomy interfaces: How can we account for the limitations in expressivity, precision and bandwidth of human-autonomy interfaces, and co-design controllers and interfaces? The mathematically-based specifications and automated synthesis algorithms will diffuse the process of building trust throughout the design, have the potential to mitigate the need for purely empirical testing, and diagnose failure modes in advance of costly and restricted user studies. This systematic and early integration will help develop autonomous systems in which the operator and autonomy protocols are equally essential components of the same system and reduce the so-called ``automation surprises." While we expect the theoretical and algorithmic outcomes of the proposed effort to be application- and hardware-agnostic, we concretize our research plan in a specific hardware platform. It is composed of an existing quadrotor testbed with 3D motion capture; human monitoring and decoding functionality through neural, visual, audial and biopotential signals; and human-autonomy interfaces with virtual reality embeddings.

Sensor energy efficiency is the top critical concern that hinders long-term monitoring in energy-constrained Internet-of-things (IoT) applications. Conventional compressive sensing techniques fail to achieve satisfactory performance in IoT and especially wearable applications due to the lack of prior knowledge about signal models and the overlook of individual variability. The research goal of this CAREER plan is to develop a data-driven and hardware-friendly IoT framework to fundamentally address the unmet energy efficiency need of IoT and especially wearable applications. This will be accomplished by a systematic approach that seamlessly integrates compressive sensing and data analytics in compressed domains using deep learning methods. The proposed research will provide a transformative IoT framework that significantly reduces the data size for transmission from sensors to cloud while improving the overall quality of information delivery and bringing signal intelligence closer to users. The research outcomes will directly impact a variety of IoT applications, such as long-term environmental sensing for monitoring the airborne quality, radiation, water quality, hazardous chemicals, and many other environment indicators, by allowing compressive sensors to be deployed in energy-constrained environments to perform precise information acquisition over a significantly increased time span impossible with existing technologies. The proposed framework will also advance wearable technologies to enable important progress in transforming the existing healthcare model from episodic examination for disease diagnosis and treatment to continuous monitoring for disease prediction and prevention. This will make our healthcare systems more effective and economic and improve the overall quality of living for billions of individuals. The PI will take advantage of his affiliation with the I/UCRC Center for Embedded Systems at ASU to engage industry sponsors to accelerate technology adoption and transfer to benefit the society at large. The PI also plans to undertake an ambitious education program to actively engage and impact a diverse population of K-12, undergraduate, and graduate students to take away the PI?s research and create more values for the community in the long term. The specific research objectives are to 1) formulate problems and develop efficient solvers to construct binary near-isometry embedding matrices to enable effective data compression on sensors through compressive sampling; 2) train deep neuron networks to decode information directly from the compressive samples for on-chip data analytics; 3) prototype the proposed framework in wearable hardware and evaluate the system performance over a variety of physiological signals. The research outcomes will allow future IoT devices to precisely sense and transfer the information of interest specified by users in an energy-efficient manner rather than recording imprecise data in raw forms as in existing approaches. The findings from this research will advance the theory development of data-driven compressive sensing by filling the current knowledge gap on how to design near-isometry embedding matrices with binary constraints that are essential for cost-effective hardware mapping. It will also uncover the intrinsic connections between compressive sensing and deep learning by establishing a viable data analytics solution for decoding high-level information directly from compressive samples. On the integration of research and education, the PI will enhance the current curriculum to better prepare students for careers in both industry and academic. The PI will take advantage of the FURI program at ASU to engage undergraduate students in research to foster their interest and motivation to pursue graduate degrees. ASU has one of the largest Hispanic and Native American student populations in the nation. The PI will make strong personal efforts to encourage the recruitment, retention, and advancement of the underrepresented groups. The PI will also collaborate with the Fulton Engineering Education Outreach office to initiate an exciting high school teacher training program, which aims to increase the level of literacy and interest in STEM fields of a large body of high school students through advanced coursework development.

Every year, 3.5 million people in the US experience homelessness, with 1 in 30 children becoming homeless. Despite numerous government-sponsored programs and efforts by nonprofit organizations, many homeless people live in abject conditions. This research re-envisions smart city technologies to best serve those in need of access to basic resources including food, shelter and medical services. The proposed infrastructure will connect the currently disjoint efforts of public services, NGOs and private citizens, and use population-modeling and planning algorithms to match the varying and unpredictable supply with those who need it. In pursuit of the overarching goal of collecting and delivering services to maximize social welfare, this research will make advances in the science of population modeling, the analysis and design of human-centered planning algorithms, and technological challenges including secure and privacy-aware sensing modalities and mobile technologies. As part of a human-centered design approach, interviews and observations will be conducted to understand user needs, and design a system that multiple stakeholders can use to report their needs and extra supply. This collected data will be used by non-profit organizations to strategically distribute resources. The real-world stakeholders such as food banks, food pantries, shelters, street medicine teams, and food rescue organizations will be closely involved in the design and evaluation process. This research is high-risk and high-reward, and appropriate for EAGER. Failure means that the resulting planning algorithms will make unfair decisions and prioritize a few organizations or donors, or will make fair, but inefficient allocation decisions, which will endanger social justice and community well-being. Success will improve both efficiency of resource distribution and the quality of life of underserved populations in the United States. The completion of the project will produce 1) algorithms for optimal resource allocation that are both efficient and aware of human-in-the-loop concerns, and which can be used for other functions including disaster-response, and 2) communication infrastructure for non-profit organizations, volunteers, and populations in need, to coordinate other service activities. The project has potential for great societal impact: it will make charitable donations convenient and inexpensive for those with supply power, increasing the volume of donations and thereby reducing wastage. The outcome will be an improved realization of the philanthropic potential of the increasingly sharing nature of the American economy.

This project addresses timing attacks in cyber-physical systems, where attackers attempt to compromise the system functionality by changing the timing of computation and communication operations. Timing attacks could be particularly destructive for cyber-physical systems because the correctness of system functionality is affected not only by the data values of operations but also significantly by at what time operations are conducted. The discoveries and methodologies developed in this project will provide fundamental advances in addressing timing attacks, and lead to the design and implementation of more secure cyber-physical systems in a number of key sectors, including automotive and transportation systems, industrial automation, and robotics. In addition to disseminate the research results through publications and workshops, the PIs will collaborate with industry partners on transitioning the research findings into practice. The PIs will also integrate the research into the curriculum at UCR and leverage it for K-12 education through the use of Lego Mindstorm platforms. The project will build a framework for identifying, analyzing and protecting cyber-physical systems against timing attacks. Building the framework consists of three closely-related research thrusts: 1) Investigate potential timing-based attack surface, and further analyze what types and patterns of timing variations the attacks may cause and how attackers may try to hide the traces of such attacks. 2) Based on the identified attack surface and strategies, analyze how timing changes caused by these attacks may affect the overall system properties, in particular safety, stability and performance. 3) Develop control-based and cyber-security defense strategies against timing attacks. This includes run-time security detectors and mitigation/adaptation strategies across control layer and embedded system layer, as well as design-time mechanisms to provide systems that are resilient to timing attacks. This project will focus on vehicle networks and multi-agent robotic systems as main application domains.

Rapid and customized part realization in all industrial sectors imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, part attributes can very rarely be directly measured and/or controlled in the production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters. This widely used strategy is based on the assumption that the proper control of the primary process responses will implicitly yield the desired part attributes. The current work aims to replace this implicit assumption by a model-based explicit evaluation of the part's attributes that uses newly established process models, available measurements of process responses and historical data from a data base that is continuously updated. In effect, this approach implies a direct instead of an implicit control of the part's desired attributes and, as such, also moves a step closer to rapid part certification. The research will establish the scientific and technological foundation for a manufacturing platform in a distributed network that seamlessly and efficiently integrates physical processes and numerical simulations in a fast predictive framework. The platform is envisioned as a multi-loop simulation and control environment consisting of four control loops running at different time scales. Two of the control loops, similar in structure to conventional controllers, act at the hardware-level and are devoted to the physical control of the relevant process variables while the other two are devoted to the software-level model-based evaluation of the desired part attributes. The latter two instruct the hardware-level controllers on required changes in their behavior that are necessary to reach the desired part attributes. To enable the integration, a voxel-based geometric model powered by an underlying data structure capable of dynamically generating analysis information, storing experimental information, and encoding the final part attributes obtained from the simulation and measured results will be established. This geometrical representation is well-suited to the use of general purpose graphics processing units (GPGPU) for fast computation of the process models that determine the physical process responses and attributes in arbitrary regions of a part. The researched framework will be validated using the state-of-the art open-architecture Directed Energy Deposition machine at Northwestern equipped with networked real-time sensing and control.

Ocean Big Data (OBD) is an emerging area of research that benefits ocean environmental monitoring, offshore exploration, disaster prevention, and military surveillance. It is now affordable for oil and gas companies, fishing industry, militaries, and marine researchers to deploy physical undersea sensor systems to obtain strategic advantages. However, these sensing activities are scattered, isolated, and often follow the traditional "deploy, wait, retrieve, and post-process" routine. Since transmitting information underwater remains difficult and unreliable, these sensors lack a cyber interconnection, which severely limits ocean cyber-physical systems. This project aims to providing a viable cyber interconnection scheme that enables distributed, efficient, ubiquitous, and secure (DEUS) data delivery from underwater sensors to the surface station. The proposed cyber interconnection scheme features cheap underwater sensor nodes with energy harvesting capability, a fleet of autonomous underwater vehicles (AUVs) for information ferrying, advanced magnetic-induction (MI) antenna design using ferrite material, distributed algorithms for efficient data collection via AUVs, and secure data delivery protocols. The success of this project will help push the frontier of Internet of Things in Oceans (IoTO) and OBD, both of which will find numerous underwater applications in offshore oil spill response, fisheries management, storm preparedness, etc., which impact the economy and well-being of not only coastal regions but also inland states. The project will also provide special interdisciplinary training opportunities for both graduate and undergraduate students, particularly women and minority students, through both research work and related courses on underwater wireless communication, network security, and AUV designs. The DEUS project provides a viable cyber interconnection scheme that enables distributed, efficient, ubiquitous, and secure data delivery in underwater environment via four synergistic thrusts: (1) integration of underwater wireless sensor and communication systems, which will enhance the current MI and light communication means of underwater sensors, integrate acoustic transmission systems for long-range communications between anchor nodes and AUVs, and design energy harvesting and replenishment solutions to prolong the lifetime of underwater sensors (30+ years); (2) distributed and ubiquitous data delivery via multiple AUVs, which aims to collect the distributed data and deliver them ubiquitously throughout the underwater network by employing ferrite material and triaxial induction antennas and mounting them outside of the AUV body for MI enhancement, and developing algorithms of multiple AUVs' path-planning, trajectory optimization, etc. under dynamic network conditions; (3) efficiency and security in data delivery, which designs network algorithms to improve the efficiency and security of data delivery. Instead of collecting data from every sensor via acoustic communications, the AUVs choose some sensors to collect data with the high data rate transmission mode in near field (e.g., light), and allowing the sensor far away from the AUVs to send its data either directly to AUVs via acoustic wave or to its nearby chosen sensors via MI/light communications. A secure data delivery scheme will also be developed to not only secure the data delivery against typical malicious attacks and guarantee the integrity of collected data, but also allow the data aggregation of one business entity without knowing others' private business information; (4) experimental validation and testing, which will verify the proposed data delivery schemes, and quantitatively present the performance gains through simulations, experiments and field test, based on existing facilities.

The aim of this collaborative project is to increase the efficiency of utility scale solar arrays using sensors, machine learning and signal processing methods to detect faults and optimize power. New cyber-computing strategies, that rely on sensor data and imaging methods to predict solar panel shading, are used to improve efficiency. A programmable 18kW testbed that consists of 104 panels equipped with sensors, actuators and cameras is used to validate all theoretical results and test new approaches for using solar analytics to optimize power generation. Machine learning and dynamic image modeling algorithms are used to control each individual panel and change connection topologies to optimize power for different cloud, load, and fault conditions. Outcomes of the CPS project include advances in: a) cloud movement modeling and shading prediction using computer vision algorithms, b) PV fault detection and optimization methods that will switch array topologies dynamically while limiting PV inverter transients, d) experimental (testbed) validation of all array monitoring methods, and e) secure wireless sensor and data fusion. Theoretical and experimental research which enables real-time analytics and remote connection topology control may influence PV array standards and smart grid initiatives. The project tasks also include: education activities, outreach at high schools, and engagement with several organizations including minority and HBCU institutions to enhance diversity.

This project develops a theory of accountability that encompasses both control and computing systems. A unified theory of accountability in Cyber-Physical Systems (CPS) can be built on a foundation of causal information flow analysis, a well-established set of methods for computer security. Information flow properties model how inputs of a system affect its outputs. Causal information flow notions provide a useful foundation for accountability since they support traceability, that is, not just detection of violations but also responsibility-assignment, which then can be used to adopt corrective measures. The intellectual merit of the proposed project lies in developing a unified foundation for CPS security based on theories of accountability, which build on causal information flow analysis. Broader impacts include the design of resilient and secure CPS, a key to sustaining reliable operation of a wide range of critical infrastructure applications for societal benefit, such as transportation and smart-grid systems. The project is organized into three major thrusts. With regards to attack detection the project develops information flow analyses to support passive and active detection against realistic adversaries. First, a general framework of information flows to recover existing results related to detection in a unified manner is proposed. Second, information flows are used as an analysis tool, with the goal of producing new results on active detection and stealthy attack characterization. The proposed project will employ this framework to formally study a set of general questions about detection. With respect to responsibility assignment and identification, the project seeks to develop theory and methodologies for identifying nodes responsible for an attack by a novel combination of methods from cryptography and control theory. In particular, it will leverage tools from traitor tracing in cryptography to efficiently search for subsets of nodes that contain malicious or faulty entities. It will employ methods from control theory to evaluate if given subsets of nodes are misbehaving or normal and it will investigate control and design policies to directly attribute information flows to distinct entities. The final task seeks to develop corrective measures via resilient system design and control. This task seeks to develop algorithms to support resilient offline (resource allocation) and online (architectures and algorithms that enable resilient online control) design of CPS and analysis tools to evaluate the security of CPS under composition.

This research investigates a cyber-physical framework for scalable, long-term monitoring and maintenance of civil infrastructures. With growth of the world economy and its population, there has been an ever increasing dependency on larger and more complex networks of civil infrastructure as evident in the billions of dollars spent by the federal, state and local governments to either upgrade or repair transportation systems or utilities. Despite these large expenditures, the nation continues to suffer staggering consequences from infrastructural decay. Therefore, paramount to the concept of a smart city of the future is the concept of smart civil infrastructure that can self-monitor itself to predict any impending failures and in the cases of extreme events (e.g. earthquakes) identify portions that would require immediate repair, and prioritize areas for emergency response. A goal of this research project is to make significant progress towards this grand vision by investigating a framework of infrastructural Internet-of-Things (i-IoT) using a network of self-powered, embedded health monitoring sensors. The collaborative and interdisciplinary nature of this research would provide opportunities for unique outreach programs involving undergraduate and graduate students in technical areas, e.g., sensors, IoTs and structural health monitoring. The project would also provide avenues for disseminating the results of this research to stakeholders in the state governments and for translating the results of the research into field deployable prototypes. This research addresses different elements of the proposed i-IoT framework by bringing together expertise from three universities in the area of self-powered sensors, energy scavenging processors, structural health monitoring and earthquake engineering. At the fundamental level, the project involves investigating self-powered sensors that will require zero maintenance and can continuously operate over the useful lifespan of the structure without experiencing any downtime. The challenge in this regard is that sensors need to occupy a small enough volume such that an array of these devices could be easily embedded and can provide accurate spatial resolution in structural imaging. This research is also investigates techniques that would enable real time wireless collection of data from an array of self-powered sensors embedded inside a structure, without taking the structure out-of-service. The methods to be explored involve combining the physics of energy scavenging, transduction, rectification and logic computation to improve the system's energy-efficiency and reduce the system latency. At the algorithmic level the project explores novel structural failure prediction and structural forensic algorithms based on historical data collected from self-powered sensors embedded at different spatial locations. This includes kernel algorithms that can exploit the data to quickly identify the most vulnerable part of a structure after a man-made or a natural crisis (for example an earthquake). Finally, the technology translation plan for this research is to validate the proposed i-IoT framework in real-world deployment, which includes buildings, multi-span bridges and highways.