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

Economics and Data Markets

  • Economics of Fresh Data Trading
    M. Zhang, A. Arafa, J. Huang, and H. V. Poor
    Chapter in Age of Information: Foundations and Applications, B. Zhou, W. Saad, H. Dillon, N. Pappas and M. A. Abd-Elmagid, Eds., Cambridge University Press, Cambridge, UK, 2022, in press.

  • Optimal and Quantized Mechanism Design for Fresh Data Acquisition
    M. Zhang, A. Arafa, E. Wei, and R. A. Berry
    IEEE Journal on Selected Areas in Communications, 39(5): 1226–1239, May 2021.

  • Pricing Fresh Data
    M. Zhang, A. Arafa, J. Huang, and H. V. Poor
    IEEE Journal on Selected Areas in Communications, 39(5): 1211–1225, May 2021.

  • Optimal Mechanism Design for Fresh Data Acquisition
    M. Zhang, A. Arafa, E. Wei, and R. A. Berry
    IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, July 2021.

  • How to Price Fresh Data
    M. Zhang, A. Arafa, J. Huang, and H. V. Poor
    International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Avignon, France, June 2019.

Energy Harvesting Communications

Large-Scale Networking

Machine Learning

  • Towards Understanding Federated Learning over Unreliable Networks
    H. H. Yang, A. Arafa, Z. Chen, Y. Fu, M. Zhao, and T. Q. S. Quek
    Submitted December 2021.

  • 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