Distributed Resource Allocation in IoT Networks Based on Deep Reinforcement Learning


Xueping Han, Journal of Information Processing Systems Vol. 22, No. 1, pp. 88-99, Feb. 2026  

https://doi.org/10.3745/JIPS.01.0117
Keywords: Average Delay, Collision probability, power consumption, Resource Allocation, Success Rate
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Abstract

Currently, most Internet of Things (IoT) resource-allocation solutions are based on centralized management, rendering it difficult to successfully establish diverse and dynamic IoT networks. Consequently, the fields of reinforcement learning and distributed computing require additional technological advancements. We propose an innovative approach for slot scheduling in IoT networks. This approach focuses on the utilization of distributed resource blocks. The purpose of this work is to demonstrate, via simulations, the influence of distributed slot assignment on the signal-to-interference ratio (SIR) and the probability of accidents occurring. The results of this research suggest that the proposed approach, in which each device in the IoT network is provided with an appropriate slot that possesses acceptable SIR levels, was successful. As the process of distributed slot allocation progresses, it is beneficial to build network convergence by utilizing the learning capabilities of each device. This was accomplished using a distributed slot-allocation process. Therefore, the existing bandwidth can be utilized more efficiently.


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Cite this article
[APA Style]
Han, X. (2026). Distributed Resource Allocation in IoT Networks Based on Deep Reinforcement Learning. Journal of Information Processing Systems, 22(1), 88-99. DOI: 10.3745/JIPS.01.0117.

[IEEE Style]
X. Han, "Distributed Resource Allocation in IoT Networks Based on Deep Reinforcement Learning," Journal of Information Processing Systems, vol. 22, no. 1, pp. 88-99, 2026. DOI: 10.3745/JIPS.01.0117.

[ACM Style]
Xueping Han. 2026. Distributed Resource Allocation in IoT Networks Based on Deep Reinforcement Learning. Journal of Information Processing Systems, 22, 1, (2026), 88-99. DOI: 10.3745/JIPS.01.0117.