Abstract: The surge of cyber-physical systems around us has led to the emergence of novel use-cases like smart surveillance and smart cars. These use-cases either involve high amounts of machine-to-machine communication or are highly interactive, making low-latency response to events critically important. Traditional cloud computing paradigm is unsuitable for such applications due to high network latency to datacenters, leading to the emergence of the edge computing paradigm.

This project is developing tools for traffic management and control using formal methods. By applying techniques such as model-checking and correct-by-construction synthesis, we ensure that traffic flow satisfies high-level objectives expressed using temporal logics that guarantee desirable behavior such as avoiding congestion, maintaining high throughput, ensuring fairness of ramp metering strategies, and reacting to incidents or unexpected conditions.

Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. New urban arterial designs encourage modal shifts which gives further impetus to devise novel traffic control strategies to more quickly respond to changing conditions and salient events, while balancing safety and efficiency for all users.

Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. However, cost-effective deployments of such capabilities have remained elusive. Such deployments will require software infrastructure that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost, as well as tools and methods for validating that required processing rates can be maintained.

The ultimate goal of the project is to help the electric grid become more reliable even when a large amount of electricity is generated from green, but intermittent - sources such as solar and wind. To deal with this intermittency, inexpensive sources of energy storage are required. Instead of investing in batteries, this project seeks to obtain cheap storage by manipulating power demand in consumer loads through intelligent decision-making algorithms.

The project is developing novel architectures for control and diagnosis of complex cyber-­‐physical systems subject to stringent performance requirements in terms of safety, resilience, and adaptivity. These ever-­‐increasing demands necessitate the use of  formal  model-­‐based  approaches  to  synthesize  provably-­‐correct  feedback  controllers.

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.