Privacy and Safety Preserving Decentralized Trading Mechanisms for Transactive Microgrids

Power grids are undergoing major changes due to tan increase in the use of distributed energy resources (DER) and a rapid adoption of renewable energy resources, such as wind and solar power. Simultaneously, the battery technology costs per kWh have been dropping significantly. These trends are enabling a different vision for the future of power-grid operations: a decentralized system in which local communities are arranged in Microgrids. In this vision, energy generation, transmission, distribution, and storage (i.e., electric vehicles or wall-mounted residential batteries) can be strategically used to balance load and demand spikes. A key feature of this vision is the support for local peer-to-peer energy trading within microgrids to reduce the load on the distribution system operators (DSO), leading to the development of transactive energy systems. In this research project we are investigating the distributed and decentralized trading and control algorithms required to enable these next generation energy environments.

This project has been funded in part by a grant from Siemens, CT.

Transit Hub

In this project, we use the public transit system in the city of Nashville as a case study to develop tools and techniques for collecting the data, modeling and then analyzing these systems. The outcome of this project will be a smart phone application powered by a real-time decision support system that will enable the transit customers to engage more effectively with the system and allow the Metro transit authority to gain a better insight into several key aspects of the system, allowing them to make it more efficient.

This project has been supported in part by the National Science Foundation grant CNS-1528799.

Social Computing Platform for Multi-Modal Transit

This project addresses the problem of urban transportation and congestion by directly engaging individual commuters. Because of the widespread use of smart devices, users are modeled as active agents in a shared economy, with algorithms designed to incentivize them to take actions that are efficient for the overall transportation system. Many commercially available Internet of Things solutions for multimodal transit focus on what is best for each individual from his or her local perspective. As the number of these local solutions grows, the misalignment between objectives of individual and the overall system also grows. An information bottleneck also forms, since massive data is collected by municipalities and users, but neither has the resources to develop real-time analytics and controls. Currently, very little has been done to provide an overarching solution that balances the needs of multiple parties, including commercial companies, municipal service providers, and individuals. The project will configure a computing and information sharing platform that overcomes the incentive gap between individuals and municipalities. This platform offers mixed-mode routing suggestions and general system information to travelers and in turn provides service providers with high-fidelity information about how users are consuming transportation resources.

This project is funded by the National Science Foundation under award CNS-1647015.

Integrated Safety Incident Forecasting and Analysis

The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders in smart and connected communities. In prior art, as well as practice, incident forecasting and response are typically siloed by category and department, reducing effectiveness of prediction and precluding efficient coordination of resources. This research project provides a unique opportunity to study the problem by integrating both the data and emergency resources from distinct urban agencies in the City of Nashville along with other widely available data such as pedestrian traffic, road characteristics, traffic congestion, and weather. This will allow development of models for anticipating heterogeneous incidents, such as distinct categories of crime, as well as vehicular accidents. With these models we can develop decision support tools to optimize both resource allocation and response times. These tools will help the emergency responders determine which units to dispatch (police, fire, or both) in order to minimize expected response time, and what equipment is most appropriate, taking into account the time, location, and nature of incidents, as well as those predicted to occur in the future. Ultimately, the methods developed in this research can be applied to other domains where multi-resource spatio-temporal scheduling is a challenge.

This project is funded in part by the National Science Foundation under award CNS-1640624.

Resilient Information Architecture Platform for the Smart Grid

The future of the Smart Grid for electrical power depends on computer software that has to be robust, reliable, effective, and secure. This software will continuously grow and evolve, while operating and controlling a complex physical system that modern life and economy depends on. The project aims at engineering and constructing the foundation for such software: a 'platform' that provides core services for building effective and powerful apps, not unlike apps on smartphones. The platform will be designed by using and advancing state-of-the-art results from electrical, computer, and software engineering, will be documented as an open standard, and will be prototyped as an open source implementation.

This project has been funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000666 and funded in part by a grant from Siemens, CT.


The CHARIOT (Cyber-pHysical Application aRchItecture with Objective-based reconfiguraTion) project, aims to address the challenges stemming from the need to resolve various challenges within extensible CPS found in smart Cities. CHARIOT is an application architecture that enables design, analysis, deployment, and maintenance of extensible CPS by using a novel design-time modeling tool and run-time computation infrastructure. In addition to physical properties, timing properties and resource requirements, CHARIOT also considers heterogeneity and resilience of these systems. The CHARIOT design environment follows a modular objective decomposition approach for developing and managing the system. Each objective is mapped to one or more data workflows implemented by different software components. This function to component association enables us to assess the impact of individual failures on the system objectives. The runtime architecture of CHARIOT provides a universal cyber-physical component model that allows distributed CPS applications to be constructed using software components and hardware devices without being tied down to any specific platform or middleware. It extends the principles of health management, software fault tolerance and goal based design.

This project has been supported in part by a grant from Siemens Corporate Technology and in part by the National Science Foundation grant CNS-1528799

Diagnostics and Prognostics for Smart Grid

Reliable operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical for the seamless functioning of a vibrant economy. Sustained power outages can lead to major disruptions over large areas costing millions of dollars. Efficient computational techniques and tools that curtail such systematic failures by performing fault diagnosis and prognostics are therefore necessary. The Smart Electric Grid is a CPS: it consists of networks of physical components (including generation, transmission, and distribution facilities) interfaced with cyber components (such as intelligent sensors, communication networks, and control software). In this this project we are developing new methods to build models for the smart grid representing the failure dependencies in both physical and cyber components. These models will be used to build an integrated system-wide solution for diagnosing faults and predicting future failure propagations that can account for existing protection mechanisms. The original contribution of this work is in the integrated modeling of failures on multiple levels in a large distributed cyber-physical system and the development of novel, hierarchical, robust, online algorithms for diagnostics and prognostics.

This project has been supported in part by the National Science Foundation grant CNS-1329803

Distributed Real-Time Embeded Managed Systems (DREMS)

In this project we designed and Implemented a Secure Information Architecture for the DARPA Systems F6 program. The information architecture platform we developed is a layered stack containing a novel real-time operating system, middleware and a component layer. This work further enabled Distributed Real-time Embedded Managed Systems (DREMS), a special class of distributed embedded computing systems that are remotely controlled and managed, but they operate in and are integrated into a local physical environment. The complete software platform and a model-driven software development toolchain that can be used to design, implement, and operate DREMS can be obtained upon request.

Development of the DREMS code base was supported by the DARPA System F6 program through NASA ARC.

Resilient Software Systems (ReSoS)

Software has become a key enabler and integrator for modern systems. Understanding the physical mechanics of software fault propagation is difficult for general class of systems. Without this knowledge, we often see that the software breaks all the time and the system breaks as a result. In this project, we studied technicals, patterns and architectural frameworks to make the software intensive system more resilient. In this work we accepted that software is going to fail and developed techniques that can be used to compare different designs for resiliency. We also studied the tradeoff between redundancy and runtime reconfiguration in this project. Finally, we designed tools for mapping distributed application configuration models to reliability block diagrams and using the redundancy information to compute resilience metrics used for comparing alternative deployments. More information and the tools are available.

This project was sponsored by Air Force Research Laboratory