New vulnerabilities arise in Cyber-Physical Systems (CPS) as new technologies are integrated to interact and control physical systems. In addition to software and network attacks, sensor attacks are a crucial security risk in CPS, where an attacker alters sensing information to negatively interfere with the physical system. Acting on malicious sensor information can cause serious consequences. While many research efforts have been devoted to protecting CPS from sensor attacks, several critical problems remain unresolved.

Robotic manipulation and automation systems have received a lot of attention in the past few years and have demonstrated promising performance in various applications spanning smart manufacturing, remote surgery, and home automation. These advances have been partly due to advanced perception capabilities (using vision and haptics) and new learning models and algorithms for manipulation and control. However, state-of-the-art cyber-physical systems remain limited in their sensing and perception to a direct line of sight and direct contact with the objects they need to perceive.

uring normal operations an aircraft is operated by its autopilot. When the autopilot sense a dangerous condition, near or outside of the flight envelope, the autopilot disengages itself, returning control to the pilot. Well-trained pilots typically can deal with modest out-of-envelope challenges.

This research is to explore various approaches for a single-chip detector that (1) can record semiconductor-chip-package tampering activity without the need of a battery, (2) can be placed inside semiconductor chip packages through a nozzle-less droplet ejector, and (3) can be wirelessly interrogated without need to open up the semiconductor package.

Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols.

In societal-scale cyber-physical systems (SCPS), machine learning algorithms are increasingly becoming the interface between stakeholders---from matching drivers and riders on ride-sharing platforms to the real-time scheduling of energy resources in electric vehicle (EV) charging stations. The fact that the different stakeholders in these systems have different objectives gives rise to strategic interactions which can result in inefficiencies and negative externalities across the SCPS.

This NSF CPS project aims to redesign the information structure utilized by system operators in today's electricity markets to accommodate technological advances in energy generation and consumption. The project will bring transformative change to power systems by incentivizing and facilitating the integration of non-conventional energy resources via a principled design of bidding, aggregation, and market mechanisms. Such integration will provide operators with the necessary flexibility to operate a network with high levels of renewable penetration.

Distributed cyber-physical systems (CPS), where multiple computer programs distributed across a network interact with each other and with physical processes, are challenging to design and verify. Such systems are found in industrial automation, transportation systems, energy distribution systems, and many other applications. This project is developing a ?systems theory? for such applications that provides a good analytical toolkit for understanding how a system will behave when networks misbehave.

The future of cyber-physical systems are smart technologies that can work collaboratively, cooperatively, and safely with humans. Smart technologies and humans will share autonomy, i.e., the right, obligation and ability to share control in order to meet their mutual objectives in the environment of operations. For example, surgical robots must interact with surgeons to increase their capabilities in performing high-precision surgeries, drones need to deliver packages to humans and places, and autonomous cars need to share roads with human-driven cars.

Multi-agent coordination and collaboration is a core challenge of future cyber-physical systems as they start having more complex interactions with each other or with humans in homes or cities. One of the key challenges is that agents must be able to reason about and learn the behavior of other agents in order to be able to make decisions. This is particularly challenging because state of the art approaches such as recursive belief modeling over partner policies often do not scale.