Smart Cities are an important research, development and deployment target for CPS. They promise to enrich the lives of residents by providing better services while also empowering them to make efficient and informed decisions. Smart cities are multi-domain systems that can include the Electric Grid, Water supply, Transportation Networks, Information Services, Emergency Services, and Trauma Centers.

Extensibility and smartness are critical to this new CPS paradigm. Extensibility is the capacity to accommodate changes readily – adding new services, for example – while preserving the original function. Smartness is the capacity of a system to learn and adapt to a changing environment and unplanned circumstances, while also possessing the mechanisms to derive responses to a variety of queries.

Replicability, resilience, scalability and interoperability are other core requirements of effective smart city solutions. Replicability is the ability to deploy in many environments. It provides for economies of scale. Resilience entails the persistence of the avoidance of failures that are unacceptably frequent or severe, when facing changes. Scalability empowers communities large and small, including those experiencing rapid growth. Interoperability allows modular solutions, empowering communities with options to meet their needs and the ability to build systems over time through incremental budget investments.

Few selected  projects are listed below  Read about other projects at the smart computing lab.

• CHARIOT (Sponsor: Siemens, CT, PI : Abhishek Dubey, Co-PI: Doug Schmidt) - 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.
• Transit Hub (Sponsor, NSF PI: Abhishek Dubey, Co-PI: Jules White and Sandeep Neema) - 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.
• Data Analytics and Data Mining for modeling and analysis of complex situations –This project is a cross-cutting effort focused on developing data repositories and analytics and mining packages to address a number of problems related to smart cities. Currently, we are working with the data gathered from Metropolitan buses to predict bus arrival times and understand the historical patterns. Our goal is identify anomalous patterns and bottlenecks which will help the city optimize the routes. Finally, we will integrate this information with real time traffic data, weather conditions, and other environmental situations to build more accurate predictions of arrival times, and help commuters more realistically plan their trips using the public transportation system. For more information about this effort, contact Dr. Abhishek Dubey or Dr. Gautam Biswas.
• Education (PI: Gautam Biswas, Co-PI: Aniruddha Gokhale)- An added advantage of collecting and analyzing problems and relevant data relevant to the city creates opportunities for using these scenarios for authentic, challenge-based problems for STEM education in high school and undergraduate programs. We will work with the Metro Public Schools to introduce students to open data repositories and data analytics, stimulating their interest in mathematics and engineering. In addition, we will work with educators to link city-related problems and their research solutions to increase the relevance and improve engagement in the K-12 STEM curricula.