Airborne networking utilizes direct flight-to-to-flight communication for flexible information sharing, safe maneuvering, and coordination of time-critical missions. It is challenging because of the high mobility, stringent safety requirements, and uncertain airspace environment. This project uses a co-design approach that exploits the mutual benefits of networking and decentralized mobility control in an uncertain heterogeneous environment.

The goal of this project is to create an integrative framework for the design of coupled biological and robotic systems that accommodates system uncertainties and competing objectives in a rigorous, holistic, and effective manner. The design principles are developed using a concrete, end-to-end application of tracking and modeling fish movement with a network of gliding robotic fish.

In recent years, we have seen a number of successful cyber-attacks against high-profile targets, which have demonstrated that resourceful and determined attackers can penetrate even highly secure systems. In light of these attacks, it becomes apparent that defenders of cyber-physical systems cannot focus solely on preventing attackers from penetrating their systems. Instead, they must also prepare to promptly detect and mitigate security breaches, thereby limiting the impact of successful attacks.

Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field.

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 goal of this project is to establish a theoretical and empirical foundation for secured and efficient energy resource management in the smart grid - a typical energy-based cyber-physical system and the future critical energy infrastructure for the nation. In this study, we focus on the detection threats and propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to mitigate data integrity attacks.

This poster exposes the numerous fundamental results that stemmed from this project within the area of cyber-physical systems security.

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.

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.