University of Oulu

E. Eldeeb, M. Shehab, A. E. Kalør, P. Popovski and H. Alves, "Traffic Prediction and Fast Uplink for Hidden Markov IoT Models," in IEEE Internet of Things Journal, vol. 9, no. 18, pp. 17172-17184, 15 Sept.15, 2022, doi: 10.1109/JIOT.2022.3195067

Traffic prediction and fast uplink for hidden Markov IoT models

Saved in:
Author: Eldeeb, Eslam1; Shehab, Mohammad1; Kalør, Anders E.2;
Organizations: 1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-10-06


In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models, such as time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI.

see all

Series: IEEE internet of things journal
ISSN: 2372-2541
ISSN-E: 2327-4662
ISSN-L: 2327-4662
Volume: 9
Issue: 18
Pages: 17172 - 17184
DOI: 10.1109/jiot.2022.3195067
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The work of Eslam Eldeeb, Mohammad Shehab, and Hirley Alves was supported in part by the Academy of Finland 6Genesis Flagship under Grant 318927; in part by FIREMAN under Grant 326301; and in part by the European Commission through the Horizon Europe Project Hexa-X under Agreement 101015956. The work of Anders E. Kalør and Petar Popovski was supported in part by the Danish Council for Independent Research (SEMIOTIC) under Grant 8022-00284B, and in part by the Villum Investigator Grant “WATER” from the Velux Foundation, Denmark.
EU Grant Number: (101015956) Hexa-X - A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see