To design and implement the QoS -Aware Energy Efficient Routing Mechanism for the BAN-IoT networks in Smart Health care Applications.

The integration of Internet of Things (IoT) and miniature implantable sensors has significantly advanced Body Area Networks (BAN) for real-time monitoring of patients' physiological vital signs, including Electrocardiogram (ECG), Electromyogram (EMG), and Electroencephalogram (EEG). However, the limited bandwidth of wearable nodes and frequent body movements result in recurrent topological changes, causing unreliable and delayed transmissions. To address these challenges, a reliable and low-latency routing mechanism is required to ensure lossless data transfer and support timely clinical treatments. This paper proposes a QoS-aware routing protocol that dynamically calculates optimal routing paths by integrating Chaotic Theory with the Honey Badger Optimization (HBO) algorithm. The proposed protocol selects latency-aware, energy-efficient paths using key metrics such as Link Quality Factor (LQF), Distance (D), Received Signal Strength Indicator (RSSI), and Number of Hops (NoH) to achieve reliable and efficient data transmission. Comprehensive experiments are conducted in the Python 3.9 environment, and performance metrics including Packet Delivery Ratio (PDR), End-to-End Delay, Throughput, Routing Load, and Control Packet Overhead are calculated and compared with other existing optimization models. The proposed model is also statistically validated against state-of-the-art protocols. Results demonstrate superior performance and stability, effectively overcoming existing bottlenecks and providing improved QoS for IoT-enabled BAN systems in smart healthcare applications.