The Sensors in a Shoebox project focuses on empowering urban citizens with the tools and methods necessary to observe and analyze the physical, social, and natural systems that affect their communities for improved community-based decision making. The project creates an affordable and ruggedized sensor kit that consists of solar-powered wireless sensors with Internet connectivity that can be distributed to communities to sense environmental parameters, vibrations, motion, among other parameters. Data is transmitted from community-deployed sensor kits to the cloud where sensor data is stored and managed. The community directly accesses their data from a web portal offering a suite of user-friendly analytical tools that citizens could use to extract community-relevant information from raw sensor data. Some envisioned community uses of the Sensors in a Shoebox platform include but not limited to: measuring neighborhood air quality, tracking the usage of public spaces, and observing residents' mobility choices (walking, biking, and motorized transport). This project will provide a scientific and technological foundation to the extension of cyber-physical systems to explicitly include humans. So called cyber-physical-social systems, these human-in-the-loop systems have the potential to transform a variety of commercial application including those in transportation, building energy management, among others. The project engages the communities of Detroit, a city beginning to go through transformation after decades of dramatic population declines. Specifically, the project recruits middle- and high school students from Detroit public charter schools to serve at the front lines of the system design and deployment. In doing so, the team will closely study and rigorously assess the experiences of urban youth using the system. In particular, advancement of STEM knowledge and youth's notions of being connected citizens will be qualitatively and quantitatively assessed.

The goal of this project is to investigate a low-cost and energy-efficient hardware and software system to close the loop between processing of sensor data, semantically high-level detection and trajectory generation in real-time. To safely integrate Unmanned Aerial Vehicles into national airspace, there is an urgent need to develop onboard sense-and-avoid capability. While deep neural networks (DNNs) have significantly improved the accuracy of object detection and decision making, they have prohibitively high complexity to be implemented on small UAVs. Moreover, existing UAV flight control approaches ignore the nonlinearities of UAVs and do not provide trajectory assurance. The research thrusts of this project are: (i) FPGA implementation of DNNs: both fully connected and convolutional layers of deep (convolutional) neural networks will be trained using (block-)circulant matrix and implemented using custom designed universal Fast Fourier Transform kernels on FPGA. This research thrust will enable efficient implementation of DNNs, reducing memory and computation complexity from O(N2) to O(N) and O(NlogN), respectively; (ii) autonomous detection and perception for onboard sense-and-avoid: existing regional detection neural networks will be extended to work with images taken from different angles, and multi-modal sensor inputs; (iii) real-time waypoint and trajectory generation - an integrated trajectory generation and feedback control scheme for steering under-actuated vehicles through desired waypoints in 3D space will be developed. For efficient implementation and hardware reuse, both detection and control problems will be formulated and solved using DNNs with (block-)circulant weight matrix. Deep reinforcement learning models will be investigated for waypoint generation and to assign artificial potential around the obstacles to guarantee a safe distance. The fundamental research results will enable onboard computing, real-time detection and control, which are cornerstones of autonomous and next-generation UAVs.

This proposal is for research on the Mobile Automated Rovers Fly-By (MARS-FLY) for Bridge Network Resiliency. Bridges are often in remote locations and the cost of installing electricity and a data acquisition system in hundreds of thousands of bridges is prohibitive. The MARS-FLY project will develop a cyber-physical system (CPS) designed to monitor the health of highway bridges, control the loads imposed on bridges by heavy trucks, and provide visual inspectors with quantitative information for data-driven bridge health assessment requiring no electricity and a minimum of data acquisition electronics on site. For fly-by monitoring, GPS-controlled auto-piloted drones will periodically carry data acquisition electronics to the bridge and download the data from the sensors at a close range. Larger Imaging drones carrying infrared (IR) cameras will be used to detect detail damages like concrete delamination. The research objectives will be accomplished first, by wireless recharging of remote sensor motes by drone to enhance the sensor operational lifetime whereas wireless recharging of drone battery will extend the operational efficiency, payload, and drone range. The novel multi-coil wireless powering approach will provide an investigation of an engineered material i.e. metamaterial with the resonant link to enhance the power level and link distance, otherwise unachievable. Next, by a major scientific breakthrough in the utilization of small quantities of low quality sensor data and IR images to determine damage information at all levels: detection of a change in behavior, location, and magnitude; streamlining of reliability analysis to incorporate the new information of damage into the bridge's reliability index based on combined numerical and probabilistic approaches such as Ensemble Empirical Mode Decomposition with the Hilbert Transform; and finally detection of nonlinearities in the signals in a Bayesian Updating framework. Moreover, an instrumented drive-by vehicle will complement damage detection on the bridge. A Bayesian updating framework will be used to update the probability distribution for bridge condition, given the measurements. Image processing of the infrared images to distinguish between the environmental effects and the true bridge deterioration (e.g. delamination in concrete) will be used to develop a better method of site-specific and environment-specific calibration.

Automation is being increasingly introduced into every man-made system. The thrust to achieve trustworthy autonomous systems, which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for meeting these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system, uncertainties can arise from the operating environment, adversarial attacks, and from within the system. While a lot of work has been done on ensuring safety of systems under standard sensing errors, much less attention has been given on securing it and its sensors from attacks. As such, autonomous cyber-physical systems (CPS), which rely heavily on sensing units for decision making, remain vulnerable to such attacks. Given the fact that the age of autonomous CPS is upon us and their influence is gradually increasing, it becomes an urgent task to develop effective solutions to ensure the security and trustworthiness of autonomous CPS under adversarial attacks. The researchers of this project provide a comprehensive real-time, resource-aware solution for detection and recovery of autonomous CPS from physical and cyber-attacks. This project also includes effort to educate and prepare the community for the potential cyber and physical threats on autonomous CPS. With the observation that a thorough security certification of autonomous CPS will provide formal evaluation of autonomous CPS, the researchers in this project intend to develop methods to facilitate manufacturers for certifying security solutions. Toward this goal, the researchers will first develop new theories to understand the impact of physical and cyber-attack on system level properties such as controllability, stability, and safety. They will then develop algorithms for detection and recovery of CPS from physical attacks on active sensors. The proposed recovery method will ensure the integrity of sensor measurements when the system is under attack. Furthermore, a new analysis framework will be constructed that uses platform-based design methodology to represent the CPS and verifies it against design metric constraints such as security, timing, resource, and performance. The key contributions of this project towards autonomous CPS security certification include 1) a comprehensive study of relationship between attacks and system-level properties; 2) algorithms and their optimization for detection and automatic recovery of autonomous CPS from attacks; and 3) systematically quantifying impact of security on design metrics.