In this poster, we expose some of our recent results on authenatication of the Internet of things.  Our results include new machine learning approaches for authenaticating IoT devices based on their environment, as well as novel ideas to distinguish malicious attacks from normal environmental changes. The poster also presents some of our related works on IoT jamming and stochastic moving target defense in IoT-like environments. Our extensive outreach activities within the scope of the GCTC challenge are also presented.

This project designs algorithms for the integration of plug-in hybrid electric vehicles (PEVs) into the power grid. Specifically, the project will formulate and solve optimization problems critical to various entities in the PEV ecosystem -- PEV owners, commercial charging station owners, aggregators, and distribution companies -- at the distribution / retail level.

Over the past two decades, cities across the country have experienced a tremendous growth in cycling. As cities expand and improve their bicycle networks, local governments and bicycle associations are looking into ways of making cycling in urban areas safer. However, one of the main obstacles in decreasing the number of bicycle crashes is the lack of information regarding cycling safety at the street level. Historically, Bicycle Level of Service (BLOS) models have been used to measure street safety.

This project aims to investigate energy and delay sensitive sensing, communication, decision-making and control in the context of cyber physical systems. We particularly focus on systems where sensors with varying accuracy observe heterogeneous source signals that must be processed and communicated and used for inference and decision-making purposes.

The ongoing research aims to develop rules to study and methods to coordinate a network of fully and partially self-driving vehicles, interacting with conventional vehicles driven by people on a complex road grid, so that overall safety and efficiency of the traffic system can be improved.

OSU SMOOTH (Smart Mobile ‎Operation: OSU Transportation Hub) is an autonomous vehicle testbed that aims at providing intelligent transportation systems research for the first- and last-mile of an individual’s commute. It augments current public transportation infrastructure by providing users access to a heterogeneous network of on-demand autonomous vehicles.

There is an increased research trend towards the application of distributed control algorithms for network power system control. We analyze the vulnerability of these distributed control algorithms to a potential attack on the communication network. We show that that the decentralized load-side control algorithm for frequency regulation in power system is fragile to communication channel uncertainty. We also propose an optimization-based framework for the design of distributed load-side control algorithm robust to communication channel uncertainty.

Fog computing refers to distributing cloud services and extending  them up to the edge of the network, allowing processing  close to the source of data ñ that guarantees a timely response, network traffic reduction, and scalability. In contrast to cloud computing, fog computing utilizes resources from various levels in the network hierarchy ñ thereby posing the need for seamless resource management.

Georgia Tech police department is using a proprietary system to manage over 700 cameras on campus. The current approach requires administrators to click on each camera pin on the map and watch the stream to find out useful information, or wade through backup camera videos to track incidents. This process requires a lot of human labor and is not scalable. We use fog computing infrastructure to provide real-time object tracking, with car tracking across space and time as a concrete use-case. 

This CPS project is driven by largescale online multimodal transit feeds to achieve equilibrium in demand and supply relationship in transportation CPS systems through cyber-control.