CCSS: Online Learning for IoT Monitoring and Management

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At the core of several emerging technological advances lies the notion of Internet-of-Things (IoT). Conceptually, IoT is envisioned as an intelligent network infrastructure with a huge number of ubiquitous smart devices present in diverse application domains such as smart buildings, group and personalized healthcare, as well as self-driving connected vehicles, to name a few. Today, a number of IoT applications have already improved many aspects of daily life. However, critical challenges need to be addressed before embracing the full potential of IoT. This in turn calls for innovative machine learning approaches that account for scalability, heterogeneity, adaptivity, and robustness to unpredictable uncertainties -- what are the central challenges facing the emerging IoT monitoring and management tasks. Novel algorithms and their performance need to leverage recent advances in data science, optimization, statistical signal processing, communications, and networking. In addition to markedly influencing future IoT modules, insights gained from this project's learning and inference will also cross-fertilize benefits to a gamut of additional domains, including smart grids, smart cities, and self-driving vehicles. At a broader scale, the developed technologies will provide valuable tools for foundational science and engineering research, and advocate societal embracing of the emergent IoT technologies. Broader impact will be further effected by the integration of research with an educational plan designed to train the new cadre of next-generation of IoT professionals, as well as foster cross-pollination of academic research to industry needs, while promoting and embracing diversity in Science and Engineering.To address the core IoT challenges, this project puts forth foundational tools for real-time interactive function learning using an ensemble of experts. Learning algorithms will be developed with adaptivity and quantifiable performance even in environments with unpredictable dynamics, but also with ability to scale in terms of i) the huge number of ‘Things’ in IoT; ii) the high-dimensional feature vectors involved in sophisticated learning tasks; and iii) the massive data collected, processed, and exchanged over the IoT graph – what is desired for IoT monitoring. Scalability, adaptivity, and robustness benefits in learning nonlinear functions will be further permeated to interactive black-box Bayesian optimization, and reinforcement learning with an ensemble of experts -- merits that will boost performance in open- and closed-loop IoT management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.