Age-of-Information (AoI)

Age-of-information (AoI) is a metric used to assess the freshness of data by measuring latency from receivers’ perspectives. It is simply defined as the time elapsed since the latest useful piece of information that reached its intended destination has been generated at its source.

AoI is very relevant in applications in which information is time-varying, such as in vehicular monitoring systems, industrial sensor networks and surveillance videos, among others.

Techniques used to optimize other latency metrics, such as rate (throughput) and transmission delay, are often not optimal in AoI-centric frameworks. It is therefore necessary to develop new methodologies to optimize and analyze systems that use AoI, or more generally any functional of AoI, as their performance metric.

One main research goal of our group is to study the fundamental nature of AoI, and how useful it can be in communications, networks and control applications.

Below are some examples of the landscape that we cover in this regard (sorted by topic name alphabetically).

Channel Coding

Cloud Computing

  • Age and Value of Information Optimization for Systems with Multi-Class Updates
    A. Arafa and Roy D. Yates
    IEEE International Conference on Communications (ICC), Denver, Colorado, June 2024.

  • Timely Cloud Computing: Preemption and Waiting
    A. Arafa, R. D. Yates, and H. V. Poor
    Allerton Conference on Communication, Control, and Computing, Monticello, Illinois, September 2019.

Economics and Data Markets

Energy Harvesting Communications

Fundamentals

  • Age of Information in Mobile Networks: Fundamental Limits and Tradeoffs
    M. Zhang, H. H. Yang, A. Arafa, and H. V. Poor
    ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Athens, Greece, October 2024.

Large-Scale Networking

Machine Learning

  • Towards Understanding Federated Learning over Unreliable Networks
    C. Feng, A. Arafa, Z. Chen, M. Zhao, T. Q. S. Quek, and H. H. Yang
    Submitted.

  • Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
    H. H. Yang, A. Arafa, T. Q. S. Quek, and H. V. Poor
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.

Privacy-Preserving Systems

Remote Estimation and Tracking