Smart Public Transit - Transit Hub

This project addresses the problem of urban transportation and congestion by building analytical tools that help the customers and the transit agencies reduce uncertainties and optimize the transit operations. We adress this problem at three fronts - Data Analytics, Planning and analysis tool for understanding and projecting the impact of transportation choices, and developing scalable data stores that can enable cities to operate their own data lakes and analytics engines. As part of the project we also created an application called Transit Hub. Recently, we have been looking at the endogenous uncertainties and costs of transit operations as part of the the energy optimization project.

This project has been supported in part by the National Science Foundation and Siemens, CT.

Smart Emergency Response

The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders. The problem of dispatching emergency responders to service accidents, fire, distress calls and crimes plagues urban areas across the globe. 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. Further, most of these approaches are offline and fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Consider the classical problem of emergency response. The goal of responders is to minimize the variance in the operational delay between the time incidents are reported and when responders arrive on the scene.
Solving this problem requires not just sending the nearest emergency responder, but sometimes being proactive placing emergency vehicles in regions with higher incident likelihood. Sending the nearest available responder by euclidean distance ignores road networks and their congestion, as well as where the resources are stationed. Greedily assigning resources to incidents can lead to resources being pulled away from their stations, increasing response times if an incident occurs in the future in the area where responder should be positioned. Now, consider solving this problem when there is a high uncertainty in the veracity of the request due to either communication failures or due to the nature of the communication medium – in extreme disruptions the most common communication mechanism used is social media, however, the social media requests have a lot of uncertainty in terms of duplication, spatial location etc. Ultimately, the methods developed in this work can be applied to other domains where multi-resource spatio-temporal scheduling is a challenge. We collaborate with Prof. Yevgeniy Vorobeychik, WUSTL, Prof. Hemant Purohit, GMU and Prof. Saideep Nannapaneni, Wichita State.

This project is funded in part by the National Science Foundation.

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.

High-dimensional Data-driven Energy optimization for Multi-Modal transit Agencies (HD-EMMA)

The goal of the project is to enable the development and evaluation of tools to promote energy efficiency within mobility as a service system currently operational in Chattanooga. For this purpose, we are developing real-time data sets containing information about engine telemetry, including engine speed, GPS position, fuel usage and state of charge (electrical vehicles) from all vehicles in addition to traffic congestion, current events in the city and the braking and acceleration patterns. These high-dimensional dataset allow us to train accurate data-driven predictors using deep neural networks, for energy consumption given various routes and schedules. CARTA is planning to use these predictors for the energy optimization of its fleet of vehicles. We are planning to evaluate our framework by comparing the energy consumption, comfort, etc. of the routes and schedules found using our data-driven framework to existing routes and schedules. We believe that such predictors will revolutionize the transportation sector in a way that is similar to the capabilities provided by high-definition maps used in autonomous driving. This project complements the DOE national labs effort on vehicle energy consumption model by exploiting new data to investigate impacts of road/driver factors on vehicle energy consumption. We collaborate actively with Prof. Aron Lazka, University of Houston and Philip Pugliese, Chattanooga Regional Transit Authority and Prof. Yuche Chen from University of South Carolina in this project.

This project is funded by the Department of Energy.

Building Resilient Electric 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). 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 grants.


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 National Science Foundation grants.

Blockchains for Smart Communities

We are focusing on creating smart and connected community solutions, which provide participants the capability to not only exchange data and services in a decentralized and perhaps anonymous manner, but also provide them with the capability to preserve an immutable and auditable record of all transactions in the system. Blockchains form a key component of these platforms because they enable participants to reach a consensus on any state variable in the system, without relying on a trusted third party or trusting each other. Distributed consensus not only solves the trust issue, but also provides fault-tolerance since consensus is always reached on the correct state as long as the number of faulty nodes is below a threshold. However, it also introduces new assurance challenges such as privacy and correctness that must be addressed before protocols and implementations can live up to their potential. For instance, smart contracts deployed in practice are riddled with bugs and security vulnerabilities. Our group has been working on a number of projects in this interesting area, including work on transactive energy systems. Our research focuses on both the reusable middleware aspect as well as the foundational technologies required to ensure the rigor and correctness of the platform. We collaborate actively with Prof. Aron Lazka, University of Houston in this project.

The work in this area has been supported by grants from Siemens, CT and National Science Foundation.

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