Mental health monitoring in 5G Edge-Enabled Cognitive IoT with Temporal Shift Transformer and integrated Stackelberg Game Theory and Nomadic People Optimizer.

This research proposes an innovative framework for mental health monitoring in 5G Edge-Enabled Cognitive internet of things (IoT) environments, integrating Stackelberg Game Theory and the Nomadic People Optimizer (NPO) algorithm. The temporal shift transformer is introduced as a key component for effective prediction of mental health. The Stackelberg Game Theory ensures strategic decision-making between the central authority and decentralized agents, optimizing resource allocation and enhancing the overall system's performance. The Nomadic People Optimizer algorithm contributes to the efficiency of the decision-making process, providing an adaptive and dynamic solution for personalized mental health monitoring. The framework aims to address the challenges associated with nomadic lifestyles, leveraging 5G edge capabilities for real-time data processing and analysis. Personalized recommendations are provisioned based on the insights derived from cognitive processing, offering tailored interventions during critical mental health situations. According to experimental data, the suggested framework outperforms baseline models like CNN, GRU, and ResNet-50 + LSTM by achieving 96.38% accuracy, 96.2% F1 score, and 97.2% specificity. Additionally, real-time alert creation with an end-to-end latency of less than 46 ms is made possible by the integration of 5G edge computing, guaranteeing prompt mental health treatments. The proposed approach demonstrates promising results in terms of accuracy, adaptability, and scalability, showcasing its potential to revolutionize mental health care for nomadic populations within the evolving landscape of cognitive IoT and 5G technologies.