Enhancing quality of service in IoT through deep learning techniques

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, ensur-ing its security is paramount. However, the vast amount of data generated by IoT devices makes it challeng-ing to manage QoS using conventional techniques, particularly when attempting to extract valuable charac-teristics 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 sam-ples 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 im-proving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms.