Enhancing quality of service in IoT through deep learning techniques

Adil M. Salman, Haider Rasheed Abdulshaheed, Zinah S. Jabbar, Ahmed Dheyaa Radhi, Poh Soon JosephNg

Abstract


When evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensuring its security is paramount. However, the vast amount of data generated by IoT devices makes it challenging to manage QoS using conventional techniques, particularly when attempting to extract valuable characteristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data samples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in improving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms.

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References


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DOI: http://dx.doi.org/10.21533/pen.v11i3.3577

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Copyright (c) 2023 Adil M. Salman, Haider Rasheed Abdulshaheed, Zinah S. Jabbar, Ahmed Dheyaa Radhi, Poh Soon JosephNg

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This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License