Traffic prediction and fast uplink for hidden Markov IoT models
Eldeeb, Eslam; Shehab, Mohammad; Kalør, Anders E.; Popovski, Petar; Alves, Hirley (2022-10-06)
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
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022100661268
Tiivistelmä
Abstract
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.
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