EAGER: Collaborative Research: Fundamental Limits on Information Freshness

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Information freshness is of critical importance in a variety of networked monitoring and control systems such as Internet of Things (IoT), intelligent vehicular systems, feedback in wireless communication systems, environmental monitoring, robotic networks, and real-time monitoring and control in cyber-physical systems. In addition it is important in several information-update and data analytics applications including financial trading, social networks, crowdsourcing, consensus systems, and online learning. In all these applications, stale information can lead to incorrect decisions, unstable control loops, and even compromises in safety and security. Since timely updates are critical in these and many other contemporary applications, an important problem is to understand the fundamental limits of information freshness in networked status update systems. This project will further our understanding of the fundamental limits of information freshness in general multi-source/multi-monitor settings, and will develop strategies to achieve these limits. The project also includes engaging research experiences for both undergraduate and graduate students.The approach in this project differs from prior work in age of information (AoI) by considering general multi-source/multi-monitor networks with explicit contention and using techniques from graph theory to study information freshness under the AoI metric in general network topologies with interference constraints. This approach is expected to lead to new insights in realistic networks with general topologies. The research plan is organized into three related tasks to better understand the theoretical foundations of AoI in the explicit contention framework: (i) simultaneous transmission and network coding, (ii) generalized information mappings and age weightings, and (iii) large-network limits. The focus in all of these tasks is on characterizing fundamental limits of information freshness and developing strategies to approach or achieve these limits in general classes of wireless and wired networks. The proposed work will generalize the explicit contention framework for AoI to a rich, realistic class of problem settings including settings where nodes can simultaneously broadcast information updates, settings where nodes have access to common or correlated information, and settings where certain information is more critical than other information.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.