EAGER: Smart and Connected Communities: Transformative Emergency Dispatch Protocols for a Sixty Second Response It is estimated that on average, every individual in the United States or Canada will call for emergency assistance at least twice during his or her lifetime. In a 9-1-1 call, the dispatcher first collects critical information (e.g., location, call-back number, what happened), assesses the situation, and then decides whether and what to dispatch. Once first responders (police, fire, medical) are dispatched, it takes anywhere from a few minutes to several tens of minutes before the first responders arrive. How much a person can do during the pre-arrival time is very critical. It can make the difference between rapid vs. prolonged recovery, temporary vs. permanent disability, and ultimately life and death. However, the caller is often left without the benefit of life-saving instructions while waiting for help. Meanwhile, the dispatchers constantly face a 60-second dilemma; that is within 60 seconds, they need to make a complicated but important decision in a very limited period: whether to dispatch and, if so, what to dispatch. We believe that the results of this proposed emergency dispatch protocols will provide directions to shorten the total response time, reduce the volume of 9-1-1 calls, decrease the operating cost of 9-1-1 call centers, and successfully establish a 60-second response mechanism in emergency dispatching. There is a huge interest in Next Generation 9-1-1 (NG 9-1-1) in not only in the academic community, but also in the operational community of 9-1-1 service providers, network application developers, federal agents, and first responders. By closely collaborating with call takers, dispatchers, emergency physicians, standard body (NENA) and NG 9-1-1 architects, we can achieve results that will not only provide a promising approach for emergency medicine but also guide the preparation of real-world practitioners. The developed dispatch protocols can be used for telemedicine applications where the physicians are able to remote media control the sensors in mobile phones and collect information from patients. In turn, these protocols can help nursing homes in suburban areas where access to specialists is limited. Next-generation (NG) 9-1-1 is being deployed for higher bandwidth and better routing of 9-1-1 calls.in smart and connected communities With mobile voice and video over IP, and remote control functions, dispatchers can get access to different forms of information (e.g., text, images, and video). In particular, dispatchers will be able to utilize sensors such as cameras, microphones, speakerphones, accelerometers, and pressure sensors for accurately assessing a situation. PI plans to use the already developed technologies such as measuring heart rate, respiratory rate, and the depth of CPR compressions from the previous awards. In this proposal, we want to address on how multimedia information can help the dispatchers and improve pre-arrival instructions to the callers to accomplish required emergency tasks (e.g., CPR or operate a fire extinguisher). The proposed research makes several unique contributions: i) developing new services for NG 9-1-1 for supporting callers and dispatchers prior to the arrival of the first responders, one of the most critical yet neglected areas in emergency dispatching, ii) exploring several fundamental research issues (e.g., index of difficulty of tasks, communication media optimization, miscommunication quantification) related to emergency task executions facilitated by using a mobile phone, which have not been examined before and are not well understood, iii) providing a sea change in the understanding of human-machine interface in NG9-1-1 in emergency dispatching, iv) providing an effective and efficient 60-second response mechanism in emergency dispatching, and v) facilitating a revised set of dispatch protocols for accurate identification of needed medical care.

Tribal communities represent the final frontiers of Internet access in the U.S., with fast (broadband) Internet access available to fewer than 10% of Native Americans on tribal reservation lands. The lack of broadband access is caused by a collection of challenges, including remote terrain, inadequate funding, and complex telecommunication policies. Yet Native Americans need reliable avenues for participation and contribution to Internet content to strengthen their communities. The work investigates technologies that will increase Internet availability on reservation lands using capacity that has been allocated to TV stations but is unused, so called ?white spaces?. Through the trial of our solutions within Southern California tribal communities, the work has the potential to reach over 2,700 homes and 60 community anchor institutions. While the immediate goal of the project is to benefit Native Americans throughout the U.S., the work has broad applicability beyond this context. Other indigenous groups, as well as low-income rural communities in the U.S. and elsewhere, face similar problems. Course material will be integrated from this project demonstrating the positive humanitarian impact of computer science in order to increase the appeal of computer science to female and minority groups. The goal of the research is to develop technologies to support Native American community building through broadening Internet accessibility and supporting social media content production and dissemination across the reservation. The work outlined in this proposal will make critical inroads to addressing the lack of Internet access on tribal reservations, to increase the number of Native American reservation residents who are able to engage with, create, and disseminate Internet and on-line social network content. The goal will be achieve through three related elements: (1) Development of an architecture suitable for a multi-network setting comprised of cellular, wireless ISP and whitespace networks, where coverage, cost and network speed varies. (2) Partnering with the community to pilot a next generation whitespace network on reservation lands, demonstrating geographic reach beyond current pilots and supporting quantitative measurements of changes in network use before and after deployment. (3) Using the initial lessons learned from the pilot to develop approaches to network management that span the socio-technical including, as appropriate, spectrum management, access management, caching policies and scheduled and on-demand content.

Tribal communities represent the final frontiers of Internet access in the U.S., with fast (broadband) Internet access available to fewer than 10% of Native Americans on tribal reservation lands. The lack of broadband access is caused by a collection of challenges, including remote terrain, inadequate funding, and complex telecommunication policies. Yet Native Americans need reliable avenues for participation and contribution to Internet content to strengthen their communities. The work investigates technologies that will increase Internet availability on reservation lands using capacity that has been allocated to TV stations but is unused, so called ?white spaces?. Through the trial of our solutions within Southern California tribal communities, the work has the potential to reach over 2,700 homes and 60 community anchor institutions. While the immediate goal of the project is to benefit Native Americans throughout the U.S., the work has broad applicability beyond this context. Other indigenous groups, as well as low-income rural communities in the U.S. and elsewhere, face similar problems. Course material will be integrated from this project demonstrating the positive humanitarian impact of computer science in order to increase the appeal of computer science to female and minority groups. The goal of the research is to develop technologies to support Native American community building through broadening Internet accessibility and supporting social media content production and dissemination across the reservation. The work outlined in this proposal will make critical inroads to addressing the lack of Internet access on tribal reservations, to increase the number of Native American reservation residents who are able to engage with, create, and disseminate Internet and on-line social network content. The goal will be achieve through three related elements: (1) Development of an architecture suitable for a multi-network setting comprised of cellular, wireless ISP and whitespace networks, where coverage, cost and network speed varies. (2) Partnering with the community to pilot a next generation whitespace network on reservation lands, demonstrating geographic reach beyond current pilots and supporting quantitative measurements of changes in network use before and after deployment. (3) Using the initial lessons learned from the pilot to develop approaches to network management that span the socio-technical including, as appropriate, spectrum management, access management, caching policies and scheduled and on-demand content.

This project will create a living lab for mHealth Analytical and Behavioral Research using Internet of Things (mHABIT) to build a generalizable infrastructure for new analytical models and a Behavioral Experimentation Platform (BEP) to understand drivers of human health and wellness behavior and lifestyle changes through mobile and sensor technologies. Using an interdisciplinary approach, this project will enhance the understanding of human behavior and interactions with smart technologies in communities. The investigators will leverage test beds with domestic and international partners to advance knowledge towards developing new analytical and experimental methods drawn from econometrics, machine learning, behavioral economics and randomized field experiments. This project will contribute to a scalable prototype technology platform and lead to new solutions for improving user health and wellness, and healthcare efficiency. Overall, this project will integrate advanced Internet-of-Things infrastructures with an instrumented version of the physical world to improve quality of life, health and wellbeing, and sustainability of communities. The methods and infrastructures developed from this project can be easily deployed by healthcare providers to support data collection, analytics, solution and evaluation. The insights from this project will suggest policy implications towards the design of smart community through sustained usage of emerging technology. Moreover, broader impact includes dissemination of research to the public, underrepresented groups, and widespread deployment of the technology. The infrastructure developed from this project will collect and analyze large-scale and fine-grained user GPS trajectory data and RFID tracking data, linked with the EHR data, to examine what factors drive users' engagement with mHealth, their interactions with doctors inside and outside the clinical setting, and what changes they make in their personal lifestyle to improve their health outcomes. To evaluate the learning of user health behavior and decision making, the investigators plan to implement a pilot deployment of the BEP in the mHABIT living lab, by partnering with healthcare providers in the US and overseas, to design and implement novel mobile-enabled interventions and evaluate the effectiveness of mHealth technology from a causal perspective.

How do decision makers in cities use information? How can they use information effectively? What are cross cutting definitions of problems that define the information that is useful? How does the fragmented decision-making via multiple policymaking paths in cities shape how information is used? This project will study organizations within local governments to analyze variation in how cities define problems and use data, disaggregating policy decision-making. Co-PIs will study jurisdictions that vary in administrative capacity, using as a case a policy issue that key informants in each city define as significant. Cities are in an increasingly information rich environment, and increasing investments in data analytics will press city officials to use information. We know little about how the practice of data analytics works outside of large cities. Intellectually, the project will contribute to theorizing problem formulation, priorities in decision-making and use of information. The project's broader impacts will include assisting cities in developing a process for formulating questions about the information that will be most useful for them, and disseminating findings across jurisdictions. The project will convene meetings that help local governments to improve the flow of information and to improve the appropriateness of measures. Finally, the project will train graduate students in multiple methods. Many of the students in the available programs are non-traditional students who are members of racialized minorities, contributing to broadening participation in the social sciences. Cases have been selected for variation in administrative capacity and size. Relying on institutional ethnography, key informant interviews, and analysis of documents, the project will trace understandings of the usefulness of data and specifically the translation from data to action. The framework is community-based research, where informants also get feedback on processes and understandings from other participants and jurisdictions. Therefore, as the project brings different organizations together to develop and use data, it will also convene participants to share the different understandings and to collaboratively define problems and possible solutions, and to discuss problem definitions, data analytics, and potential solutions with community members. That approach will mean that city officials, and cities' citizens and denizens, as designers, users and constituents will benefit from the process as the project progresses. The project will surface a soft systems model of information and decision-making flows. Finally, the co-PIs will use the information gathered in previous phases to develop an agent-based model to assist decision makers.

This project will create a living lab for mHealth Analytical and Behavioral Research using Internet of Things (mHABIT) to build a generalizable infrastructure for new analytical models and a Behavioral Experimentation Platform (BEP) to understand drivers of human health and wellness behavior and lifestyle changes through mobile and sensor technologies. Using an interdisciplinary approach, this project will enhance the understanding of human behavior and interactions with smart technologies in communities. The investigators will leverage test beds with domestic and international partners to advance knowledge towards developing new analytical and experimental methods drawn from econometrics, machine learning, behavioral economics and randomized field experiments. This project will contribute to a scalable prototype technology platform and lead to new solutions for improving user health and wellness, and healthcare efficiency. Overall, this project will integrate advanced Internet-of-Things infrastructures with an instrumented version of the physical world to improve quality of life, health and wellbeing, and sustainability of communities. The methods and infrastructures developed from this project can be easily deployed by healthcare providers to support data collection, analytics, solution and evaluation. The insights from this project will suggest policy implications towards the design of smart community through sustained usage of emerging technology. Moreover, broader impact includes dissemination of research to the public, underrepresented groups, and widespread deployment of the technology. The infrastructure developed from this project will collect and analyze large-scale and fine-grained user GPS trajectory data and RFID tracking data, linked with the EHR data, to examine what factors drive users' engagement with mHealth, their interactions with doctors inside and outside the clinical setting, and what changes they make in their personal lifestyle to improve their health outcomes. To evaluate the learning of user health behavior and decision making, the investigators plan to implement a pilot deployment of the BEP in the mHABIT living lab, by partnering with healthcare providers in the US and overseas, to design and implement novel mobile-enabled interventions and evaluate the effectiveness of mHealth technology from a causal perspective.

Acoustic sensing-based preventive maintenance approach focuses on mapping auditory information, captured from mechanical systems in buildings, to their health status and probability of impending failures. An important application of this methodology is reducing energy waste in commercial heating, ventilating, and air-conditioning (HVAC) systems, which accounts for over 42% of the total U.S. commercial building energy usage. The outcome of this project is a robust acoustic sensing technology that has a high accuracy in predicting actual failures of HVAC systems. This research will be integrated with new user interfaces that will allow building managers to virtually navigate the equipment and appliances in large buildings (or collections of buildings), and to quickly identify potential failures. This EArly-Concept Grants for Exploratory Research (EAGER) project addresses the following technology gaps as it translates from research discovery toward commercial applications: (a) ensuring privacy, and (b) minimizing false positives in predicting equipment failure. This project develops acoustic signal acquisition and processing techniques that preserve the privacy of everyone and everything that is susceptible to privacy violations due to continuous acoustic monitoring. The proposed collaborative research enables buildings to be retrofitted with a low-cost, acoustic sensing solution to monitor its HVAC systems to predict their impending failures. A major goal of this project is to reduce false positives when making these predictions that are primarily caused by inadequate modeling of sounds from a faulty component, inadequate modeling of different types of faults, and errors in sound source recognition. Furthermore, this project creates a foundation for the next generation of intelligent systems that autonomously monitor equipment and predict failure. The project engages University of Florida and University of North Carolina at Chapel Hill to augment research capability in conducting visualization-based dynamic assessment of HVAC systems, and building low-cost, embedded device-based centralized HVAC monitoring systems. With a cloud-connected network of embedded audio monitoring devices deployed in the University of Florida campus buildings for running acoustic processing and classification tasks, this novel and transformative technology is aimed at identifying and solving challenges in large-scale, commercial-grade deployment of such systems in real world scenarios. This project will engage an industrial partner to develop privacy-preserving algorithms, build test environments, and guide commercialization aspects of this technology.

Advances in technology mean that computer-controlled physical devices that currently still require human operators, such as automobiles, trains, airplanes, and medical treatment systems, could operate entirely autonomously and make rational decisions on their own. Autonomous cars and drones are a concrete and highly publicized face of this dream. Before this dream can be realized we must address the need for safety - the guaranteed absence of undesirable behaviors emerging from autonomy. Highly publicized technology accidents such as rocket launch failures, uncontrolled exposure to radiation during treatment, aircraft automation failures and unintended automotive accelerations serve as warnings of what can happen if safety is not adequately addressed in the design of such cyber-physical systems. One approach for safety analysis is the use of software tools that apply formal logic to prove the absence of undesired behavior in the control software of a system. In prior work, this approach this been proven to work for simple controller software that is generated automatically by tools from abstract models like Simulink diagrams. However, autonomous decision making requires more complex software that is able to solve optimization problems in real time. Formal verification of control software that includes such optimization algorithms remains an unmet challenge. The project SORTIES (Semantics of Optimization for Real Time Intelligent Embedded Systems) draws upon expertise in optimization theory, control theory, and computer science to address this challenge. Beginning with the convergence properties of convex optimization algorithms, SORTIES examines how these properties can be automatically expressed as inductive invariants for the software implementation of the algorithms, and then incorporates these properties inside the source code itself as formal annotations which convey the underlying reasoning to the software engineer and to existing computer-aided verification tools. The SORTIES goal is an open-source-semantics-carrying autocoder, which takes an optimization algorithm and its convergence properties as input, and produces annotated, verifiable code as output. The demonstration of the tool on several examples, such as a Mars lander, an aircraft avionics system, and a jet engine controller, shows that the evidence of quality produced by annotations is fully compatible with its application to truly functional products. Project research is integrated with education through training of "tri-lingual" professionals, who are equally conversant in system operation, program analysis, and the theory of control and optimization.

Heavy vehicles, such as trucks and buses, are part of the US critical infrastructure and carry out a significant portion of commercial and private business operations. Little effort has been invested in cyber security for these assets. If an adversary gains access to the vehicle's Controller Area Network (CAN), attacks can be launched that can affect critical vehicle electronic components. Traditionally, physical access to a heavy vehicle was required to access the CAN. However, wireless devices are also installed on heavy vehicles, which open trucks and busses to remote wireless cyber attacks. This project explores cyber security vulnerabilities related to wireless devices that communicate on the CAN. For identified threats, researchers determine the proper mitigation strategies, including where and how they are best deployed. To demonstrate potential exploits and subsequent trust in proposed mitigation strategies, this project designs and implements a scalable, high-fidelity test bed using actual heavy vehicle electronic control units, such as engine and brake controllers. The test bed includes built-in mechanisms for remote access and secure information delivery to allow for collaboration among researchers at different sites. The results of the research, including the potential to extend the test bed with other components, can impact cyber security analysis for other industries that use CAN, such as building automation, medical devices, and manufacturing. The SAE J1939 communication network in heavy vehicles is based on CAN and has open documentation for packet definition and transmission. This openness may be exploited for creating spoofed J1939 messages. Heavy vehicle owners utilize third-party systems, such as remote telematics, that introduce new J1939 enabled modules, which can potentially be subverted by an adversary. This project uses these systems to gain remote access and attack another CAN connected electronic control unit. Packet sniffing is performed as the telematics system connects wirelessly to the CAN to determine if fake packets can be inserted. Research includes examining different designs, configurations, and deployments of intrusion detection systems to best thwart such remote attacks using the developed test bed. One challenge is to develop algorithms that can act in real-time with deployed test bed hardware. Research includes developing scientific strategies to measure the temporal response of the cyber actions in the test bed and the reaction time of any intrusion detection system, so that bounds can be determined based on the ability to conduct a remote cyber operation on a J1939 network.

Computer systems are increasingly coming to be relied upon to augment or replace human operators in controlling mechanical devices in contexts such as transportation systems, chemical plants, and medical devices, where safety and correctness are critical. A central problem is how to verify that such partially automated or fully autonomous cyber-physical systems (CPS) are worthy of our trust. One promising approach involves synthesis of the computer implementation codes from formal specifications, by software tools. This project contributes to this "correct-by-construction" approach, by developing scalable, automated methods for the synthesis of control protocols with provable correctness guarantees, based on insights from models of human behavior. It targets: (i) the gap between the capabilities of today's hardly autonomous, unmanned systems and the levels of capability at which they can make an impact on our use of monetary, labor, and time resources; and (ii) the lack of computational, automated, scalable tools suitable for the specification, synthesis and verification of such autonomous systems. The research is based on study of modular reinforcement learning-based models of human behavior derived through experiments designed to elicit information on how humans control complex interactive systems in dynamic environments, including automobile driving. Architectural insights and stochastic models from this study are incorporated with a specification language based on linear temporal logic, to guide the synthesis of adaptive autonomous controllers. Motion planning and other dynamic decision-making are by algorithms based on computational engines that represent the underlying physics, with provision for run-time adaptation to account for changing operational and environmental conditions. Tools implementing this methodology are validated through experimentation in a virtual testing facility in the context of autonomous driving in urban environments and multi-vehicle autonomous navigation of micro-air vehicles in dynamic environments. Education and outreach activities include involvement of undergraduate and graduate students in the research, integration of the research into courses, demonstrations for K-12 students, and recruitment of research participants from under-represented demographic groups. Data, code, and teaching materials developed by the project are disseminated publicly on the Web.