Directed by Anna Scaglione, the Signal, Information, Networks and Energy (SINE) laboratory (formerly known as the CRISP Lab) was established in Cornell University in 2001. It was proudly hosted by Cornell University (2001-2008) and University of California, Davis (2008-2014) prior to moving to Arizona State University. The SINE Lab’s research focuses on the intersection of signal processing, network science and energy systems.



Nowadays, the advancement in scientific technologies has provided the information and technological platforms for many engineering problems that involve massive sensing and high dimensional inferences, especially those associated with a large network. In particular, wireless networks and power systems are concrete applications that entail wide area infrastructure and scalable data processing. The vision is precisely to reduce the complexity and augment the flexibility of sensing, communication and computation architectures in the “big data” regime



Critical infrastructures, such as the power grid, rely on Industrial Control Systems (ICS) to operate and are exposed to traffic from a wide variety of sources. Attackers can inject information that appears to be consistent with industrial control protocols, and infiltrate firewalls protecting the control perimeter of the control network. Network Intrusion Detection Systems (IDS) software can exploit the knowledge of the predictable patterns of communication that support the protection schemes to determine traffic that is an outlier. The goal of this project is to design and develop identification and intrusion detection algorithms for protective relays in the power grid, based on the knowledge of the expected behavior of the system.



The modernization of power grid Industrial Control Systems (ICS) is likely to lead to the adoption of modern cloud services for data historians and to derive “big data” data analytics. The key idea is to determine a parallel computing strategy to extract the power grid state from the hybrid measurement systems on the field that would be easy to map onto a cloud service. Specifically we have investigated how to obtain an estimate of the grid state in a set of servers, each a repository for a portion of the measurements, either from SCADA or from PDC aggregating PMU data.



A method to provide decentralized common timing information. We propose a simple communication protocol, that uses simple to detect beacons to adjust every node to the common time. The set up we propose can also be used for scheduling in a shared communication medium.