The freshness of information is of fundamental importance in information-update and data analytics applications (e.g., crowdsourcing, financial trading, social networks, and online learning), as well as networked control and monitoring systems (e.g., sensor networks, airplane/vehicular control, robotics networks, Internet of Things, and Cyber-Physical Systems). Two fundamental problems in this area are (i) what are appropriate measures for freshness and (ii) how to utilize these measures to improve the performance of real-time applications. In this talk, I will present some recent results obtained in our recent exploration of these problems. I will first introduce a few examples which suggest that nonlinear functions of the Age of Information are useful freshness measures for many applications. Then, I will discuss the relationship between nonlinear age functions and the mean-squared estimation error of Gauss-Markov signals, where the estimation error is a freshness measure used in many remote estimation problems. I will further present the optimal sampling strategies that optimize nonlinear age functions and remote estimation error. Insights will be drawn by comparing the optimal solutions to these two sampling problems. 

Yin Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University, Alabama. He received his B. Eng. and Ph.D. degrees in electronic engineering from Tsinghua University, Beijing, China, in 2006 and 2011, respectively, and received the Excellent Doctoral Thesis Award of Tsinghua University.  He was a postdoctoral scholar and research associate at the Ohio State University during 2011-2017. His research interests include wireless communications, communication networks, information freshness, and information theory. He is the founding co-chair of the 1st and 2nd Age of Information Workshops, in conjunction with the IEEE INFOCOM 2018-2019. The paper that he co-authored received the Best Student Paper Award at IEEE WiOpt conference 2013.