AI Based Adaptive QoS Optimization for IoT Devices in Healthcare Networks

This paper proposes an artificial intelligence-based adaptive Quality of Service (QoS) optimization framework for Internet of Things (IoT) devices in healthcare networks. IoT devices, such wearable monitoring and remote diagnostic tools, struggle to maintain optimal performance due to fluctuating network circumstances and hardware limitations. The suggested system uses machine learning and deep learning techniques to dynamically update QoS settings in order to achieve consistent, low-latency, and energy-efficient data transport. The AI model forecasts congestion, distributes resources optimally, and enhances network performance by continuously learning from real-time data and network conditions. This customisable solution improves healthcare delivery by providing timely, continuous services, resulting in better patient outcomes and ensuring the long-term viability of IoT healthcare networks. The framework's adaptive nature addresses scalability and flexibility challenges in healthcare IoT systems by constantly learning and optimising performance as network conditions change. The simulation results demonstrate how well the proposed solution works to improve QoS, ensure seamless healthcare delivery, and improve patient outcomes in dynamic network situations. This study paves the way for AI-powered healthcare IoT networks that are more reliable, responsive, and intelligent.