The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients’ health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person’s physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed “Stress-Track”. The device is intended to track a person’s stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

The rise of 6G-enabled smart industries necessitates secure, adaptive, and interpretable anomaly detection frameworks capable of operating under dynamic, adversarial, and resource-constrained environments. This study presents SAFEL-IoT, a novel Secure Adaptive Federated Learning framework with integrated explainability, specifically designed for anomaly detection in Industrial Internet-of-Things (IIoT) systems under Industry 5.0 paradigms. SAFEL-IoT introduces a dynamic aggregation mechanism based on temporal model divergence, a hybrid encryption scheme combining partial homomorphic encryption with differential privacy, and an interpretable anomaly scoring pipeline leveraging SHapley Additive exPlanations (SHAP) values and temporal attention mechanisms. Extensive experimentation on the SKAB industrial dataset demonstrates that SAFEL-IoT achieves a superior F1 score of 0.93, reduces training time to 63.7 s, and maintains explanation fidelity with only a 0.15 explanation error. Communication efficiency is improved by 70.3% through 6G network slicing, while detection latency remains below 12 ms across 100 distributed edge clients. Further analysis shows a 41.7% improvement in drift robustness and a 68.9% reduction in false positives compared to traditional federated learning baselines. Theoretical convergence guarantees, scalability under large node deployments, and resilience against adversarial attacks validate SAFEL-IoT as a comprehensive and practical solution for secure, explainable, and scalable anomaly detection in next-generation industrial ecosystems.

Wireless Body Area Networks (WBANs) play a critical role in real-time healthcare monitoring by enabling continuous data collection from body-worn sensors. However, energy inefficiency and data security vulnerabilities limit their large-scale deployment and long-term reliability. The primary aim of this research is to optimize both energy consumption and data integrity within WBAN-based health monitoring systems. To address these issues, a novel multi-layered framework is proposed. First, an energy-efficient mechanism is introduced using a threshold-based transmission controller. Each sensor node monitors its power levels and enters sleep mode when a defined energy threshold (0.4 J) is reached. A flag is transmitted to the sink node to indicate potential data unavailability, preventing misinterpretation of sensor silence. Transmission power is dynamically scaled to further minimize energy usage without compromising data delivery. Second, a lightweight security mechanism is incorporated using echo packet validation. A dedicated security block monitors the reverse packet flow for anomalies. Any deviation from the expected flow pattern triggers a breach alert, effectively identifying and mitigating spoofing or reverse-channel attacks. This ensures data integrity and confidentiality across the WBAN. Additionally, a trust aggregation engine is implemented to quantify the trustworthiness of nodes using both global and local ratings. The healthcare provider assigns global ratings, while local ratings are crowdsourced from remote users or monitoring agents. The aggregation logic at the Cluster Head (CH) ensures that only data with validated origin and computation consistency are accepted, significantly increasing trust in remote diagnostics. Simulations were conducted using MATLAB R2022b with 50 nodes across a 20 × 20 m² area. The proposed system achieved a 35% reduction in energy consumption, lowering it from 20 Joules (baseline) to 13 Joules, and improved data accuracy to 98%, compared to 78% and 80% in existing methods. The system also demonstrated a reduction in false positive alerts below 3.8%, a packet delivery ratio of 97.5%, and latency under 92 ms, indicating its real-time applicability and reliability. These results confirm that the proposed framework not only prolongs network lifetime but also secures data communication while maintaining high diagnostic fidelity, thereby offering a robust and scalable solution for next-generation WBAN healthcare applications.