Enhancing quality of service in IoT through deep learning techniques

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