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@article{ART002871905},
author={Taewon, SONG},
title={A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2022},
volume={8},
number={4},
pages={1-7},
doi={10.20465/KIOTS.2022.8.4.001}

TY - JOUR
AU - Taewon, SONG
TI - A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks
JO - Journal of Internet of Things and Convergence
PY - 2022
VL - 8
IS - 4
PB - The Korea Internet of Things Society
SP - 1
EP - 7
SN - 2466-0078
AB - As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.
KW - IoT;Artificial intelligence;Deep reinforcement learning;Deep Q learning;Buffer management
DO - 10.20465/KIOTS.2022.8.4.001
ER -

Taewon, SONG. (2022). A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks. Journal of Internet of Things and Convergence, 8(4), 1-7.

Taewon, SONG. 2022, "A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks", Journal of Internet of Things and Convergence, vol.8, no.4 pp.1-7. Available from: doi:10.20465/KIOTS.2022.8.4.001

Taewon, SONG "A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks" Journal of Internet of Things and Convergence 8.4 pp.1-7 (2022) : 1.

Taewon, SONG. A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks. 2022; 8(4), 1-7. Available from: doi:10.20465/KIOTS.2022.8.4.001

Taewon, SONG. "A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks" Journal of Internet of Things and Convergence 8, no.4 (2022) : 1-7.doi: 10.20465/KIOTS.2022.8.4.001

Taewon, SONG. A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks. Journal of Internet of Things and Convergence, 8(4), 1-7. doi: 10.20465/KIOTS.2022.8.4.001

Taewon, SONG. A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks. Journal of Internet of Things and Convergence. 2022; 8(4) 1-7. doi: 10.20465/KIOTS.2022.8.4.001

Taewon, SONG. A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks. 2022; 8(4), 1-7. Available from: doi:10.20465/KIOTS.2022.8.4.001

Taewon, SONG. "A Research on Low-power Buffer Management Algorithm based on Deep Q-Learning approach for IoT Networks" Journal of Internet of Things and Convergence 8, no.4 (2022) : 1-7.doi: 10.20465/KIOTS.2022.8.4.001