For this project, we are building tools and verification techniques that check modern cyberphysical systems (CPSs) and Internet of Things (IoT) systems for correctness in order to decrease the likelihood of behavior that may lead to various vulnerabilities, including those related to security.   In particular, we intend to create a suite of verification tools for design-time, compile-time, and run-time checking of these systems.  Some of these tools will be software-oriented, but others will explore hardware-support for checking correct execution of deployed systems.

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.

A method for achieving lane-level localization in global navigation satellite system (GNSS)-challenged environments is presented. The proposed method uses the pseudoranges drawn from unknown ambient cellular towers as an exclusive aiding source for a vehicle-mounted light detection and ranging (lidar) sensor. The following scenario is considered. A vehicle aiding its lidar with GNSS signals enters an environment where these signals become unusable. The vehicle is equipped with a receiver capable of producing pseudoranges to unknown cellular towers in its environment.

The overall research objective of the project is to establish and demonstrate a generic motion-sensing co-design procedure that significantly reduces the complexity of mission design for swarm-ing CPS, and greatly facilitates the development of effective and efficient control and sensing strategies.

The IoT requires rethinking of traditional ways of providing power. The environment is a source of mechanical energy that could be converted into electrical energy via the direct piezoelectric effect. In order to have a reliable and sustainable energy supply for low power sensing systems in buildings, vibrational energy harvesting is being pursued. Lead based materials in a piezoelectric compliant mechanism energy harvester can provide up to 3.9 mWcm-2g2 (H.G. Yeo, 2016).

Current learning algorithms cannot be easily applied in CPS due to their need for continuous and expensive updates, with the current triggered frameworks having fundamental limitations. Such limitations lead to the following questions. How can we incorporate and fully adapt to totally unknown, dynamic, and uncertain environments? How do we co-design the action and the intermittent schemes? How can we provide quantifiable real-time performance, stability and robustness guarantees by design? And how do we solve congestion and guarantee security?

Future power grids will incorporate a large number of distributed energy resources, which motivates research on real-time online algorithms that optimally control large networked systems. This poster introduces two algorithms for the real-time control of time-varying physical systems, based on recent development in time-varying optimization. The two algorithms take advantage of real-time feedback measurements of the system, are computationally efficient, and are theoretically guaranteed to have bounded suboptimality under certain conditions.