University of Oulu

B. Zhou, W. Saad, M. Bennis and P. Popovski, "Risk-Aware Optimization of Age of Information in the Internet of Things," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9149370

Risk-aware optimization of Age of Information in the Internet of Things

Saved in:
Author: Zhou, Bo1; Saad, Walid1; Bennis, Mehdi2;
Organizations: 1Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
2Center for Wireless Communications, University of Oulu, Finland
3Department of Electronic Systems, Aalborg University, Aalborg, Denmark
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202102195361
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-19
Description:

Abstract

Minimization of the expected value of age of information (AoI) is a risk-neutral approach, and it thus cannot capture rare, yet critical, events with potentially large AoI. In order to capture the effect of these events, in this paper, the notion of conditional value-at-risk (CVaR) is proposed as an effective coherent risk measure that is suitable for minimization of AoI for real-time IoT status updates. In the considered monitoring system, an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status update process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted risk-aware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the risk-aware MDP is reduced to a standard MDP with an augmented state space, for which we derive the optimal stationary policy using dynamic programming. In particular, the optimal history-dependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is shown to be computationally tractable and able to minimize the AoI in real-time IoT monitoring systems in a risk-aware manner.

see all

Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-5089-5
ISBN Print: 978-1-7281-5090-1
Article number: 9149370
DOI: 10.1109/ICC40277.2020.9149370
OADOI: https://oadoi.org/10.1109/ICC40277.2020.9149370
Host publication: IEEE International Conference on Communications
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This research was supported by the U.S. National Science Foundation under Grant CNS-1836802, the Academy of Finland Project CARMA, the Academy of Finland Project MISSION, the Academy of Finland Project SMARTER, the INFOTECH Project NOOR, the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (ERC Consolidator Grant Nr. 648382 WILLOW), and the Danish Council for Independent Research (Grant Nr. 8022-00284B SEMIOTIC).
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.