CNS Core: Small: Application-Oriented Scheduling for Optimizing Information Freshness in Wireless Networks

点击翻译按钮获取中文摘要

The significant advancement in wireless networking technologies as well as the proliferation of mobile devices have enabled information-centric Internet of Things (IoT) systems, where timely information updating is normally required or preferred by users. Age of Information (AoI) has recently been introduced to quantify the freshness of the knowledge the controller has about the remote information sources. While AoI is information-centric, in the present state of the art, the evaluation of information freshness has been conducted mainly in a transmission-centric manner. This project aims to bridge the gap between transmission-level information freshness and application-level decision making by revealing involved fundamental research issues, developing generic modeling tools, and generating effective network protocols in some important practical scenarios. This proposed research integrates theoretical studies in the areas of wireless networking, optimization theory, control theory, information theory, and machine learning. Such an interdisciplinary project will not only provide various training projects to undergraduate and graduate students, but also inspire students to pursue high-quality research with a creative and open-minded perspective. Information freshness based wireless scheduling algorithms are of critical importance to next generation wireless networks and mobile applications, with a good potential to be transformed into practical solutions.The proposed research is expected to contribute to innovative information freshness optimization techniques with some fundamental intellectual merits. This study is to show that optimizing information freshness from an application-oriented angle (that is, incorporating user query patterns, correlations across different information sources, or cooperative decision-making among distributed agents) requires brand-new modeling/analysis techniques. Effective decomposition techniques and low-complexity algorithms for information freshness optimization are to be developed through predicting user query patterns and incorporating delay-tolerant responses at the application level. When multiple correlated information sources present, a fundamental obstacle to information freshness optimization is the lack of a well-defined application-level freshness metric; this issue will be tackled. Moreover, it is to be demonstrated that the intricacies raised by the correlations can be addressed efficiently through integrating domain knowledge and deep reinforcement learning techniques. With recent studies confirming the inadequacy of CSMA-based scheduling in terms of freshness optimization, a decentralized TDMA-based medium access control (MAC) protocol is to be developed and mathematically analyzed. Such a new MAC protocol is expected to provision quantitative information freshness guarantee in the scenario of multi-agent decision making; the outcome in this part hopefully can contribute an important component for freshness-oriented network design in the system level.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.