NeTS: Small: Generalized Submodular Optimization for Integrated Networked Sensing Systems

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Wireless sensor networks are evolving from specialized platforms to shared infrastructure for multiple applications. Shared sensor networks offer flexibility, adaptivity, and cost-effectiveness through dynamic resource allocation among different applications. Shared sensor networks face the critical need for optimizing Quality of Monitoring (QoM) subject to resource constraints. The emerging QoM optimization problems in shared sensor networks are computationally challenging due to their nonlinear, discrete, and dynamic nature. This project exploits a key property known as submodularity that many QoM attributes of physical phenomena exhibit. This project develops efficient and theoretically sound distributed approaches for QoM optimization, through a novel integration of submodular optimization in a market-based approach. It further studies online algorithms which can quickly adapt to network and application dynamics, partition-based algorithms that scale effectively for large-scale networks, and new optimization algorithms that can accommodate the optimization of energy consumption and heterogenous networks. Expected results of this project include theory, algorithms, and software for managing and optimizing a new generation of integrated networked sensing systems with high societal and environmental impact. The project also deploys an integrated sensor network for environmental monitoring in Tyson Research Center of Washington University for environmental and ecological research. This study promotes interdisciplinary collaboration with environmental and biological scientists, as well as outreach activities for high-school students.