S&AS: FND: Cognitive and Reflective Monitoring Systems for Urban Environments

University Of California-Irvine, Irvine CA

Investigators

Abstract

As the urban population grows, a pressing need arises for technological solutions capable of making city systems more effective and efficient. Many of the envisioned Smart City systems, such as intelligent transportation, and vehicular and security networks, require having access to a wide spectrum of online/offline data that characterizes the state of operation and the events taking place throughout the city. However, the practical deployment of city-wide sensor and processing systems faces several challenges, including financial cost, availability of network resources to transport large amounts of data with stringent quality of service requirements, and coexistence with other existing services using the same resources. This project's objective is to develop a cognitive and reflective network of mobile sensors capable of minimizing their impact on critical city communication resources using a notion of intelligence permeating the layered communication, processing, and sensing infrastructure that characterizes urban environments. The successful realization of this project will contribute significantly to fulfilling the promise of real-time Urban Internet of Things (IoT) systems in the context of limitations imposed by technology and cost. The project also includes a multi-tiered education, mentoring, and outreach plan to train the next generation of IoT systems designer and professionals. The proposed system enhances the ability of individual sensors to make navigation decisions with an intelligent layered architecture capable of providing real-time feedback on the usefulness of their produced data from the perspective of a global computational objective. The real-time adaptation process, then, is expressed over the layers of the architecture, where the agents dynamically learn utility models and control at different geographical and temporal scales. These utility models are used to orchestrate the mobile sensor's navigation within the city to enable detection and monitoring of events and dynamic processes. The outcome of this project is the first architecture of this kind where cognition and intelligence spread across scales of an urban sensing, communications, and processing infrastructure. Furthermore, the system envisioned in this project represents one of the few and most innovative examples of edge computing architecture, where the availability of low-delay processing is used to optimize the system's operations. The construction of a framework for such a complex layered scenario, which involves devices with different sensing and computation capabilities, presents inherent technical challenges which will be addressed by producing several innovations in the area of distributed and hierarchical learning and robot navigation