A Deep Learning Based Joint Channel Access and Switch in Internet of Wban Environment for Human Activity Recognition

A secure and energy-efficient Wireless Body Area Network (WBAN) for healthcare monitoring, intelligent channel management to ensure consistent patient data delivery, we introduced DJCSIW model is A Deep Learning based Joint Channel Access and Switch in Internet of WBAN Environment for Human Activity Recognition. It integrates body and environmental sensors to monitor health metrics, with data transmitted securely through clustered nodes using blockchain authentication. Optimized cluster heads (CH) are selected via the SineCosine Hunting Optimization (SHO) algorithm, while a multi-hop routing strategy using the MOORA method enhances data transmission efficiency. A three-layer NBWAN architecture facilitates responded communications with an emphasis on critical data. Patient access is managed through a Key Management Server (KMS). Using AI-driven techniques, including recurrent and deep neural networks to optimize channel selection and Modulation and Coding Schemes (MCS). The simulation parameters are used to calculate the DJCSIW model by throughput, delay, packet loss, residual energy and data success rate.