Technological advancements have made it possible to monitor, diagnose, and treat patients remotely. The vital signs of patients can now be collected with the help of Internet of Things (IoT)-based wearable sensor devices and then uploaded on to a fog server for processing and access by physicians for recommending prescriptions and treating patients through the Internet of Medical Things (IoMT) devices. This research presents the outcome of a survey conducted on healthcare integrated with fog computing and IoT to help researchers understand the techniques, technologies and performance parameters. A comparison of existing research focusing on technologies, procedures, and findings has been presented to investigate several aspects of fog computing in healthcare IoT-based systems, such as increased temporal complexity, storage capacity, scalability, bandwidth, and latency. Additionally, strategies, tools, and sensors used in various diseases such as heart disease, chronic disease, chikungunya viral infection, blood pressure, body temperature, pulse rate, diabetes, and type 2 diabetes have been compared.

The Internet of Things (IoT) revolutionises modern technology, offering unprecedented opportunities for connectivity and automation. However, the increased adoption of IoT devices introduces substantial security vulnerabilities, necessitating effective anomaly detection frameworks. This Paper proposes a secure IoT anomaly detection framework by utilising four machine learning algorithms such as: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). By generating synthetic datasets with induced anomalies, the framework employs AWS IoT Core infrastructure and Python-based analysis to identify irregularities in device performance. The proposed framework achieved a high detection accuracy ranging from 91 to 98% across the tested algorithms, with CART showing the best performance. Key performance metrics, including precision, recall, and F1-score, confirmed the model's reliability in distinguishing between normal and anomalous IoT data. Experimental results demonstrate superior detection accuracy across all methods, validating the robustness of the proposed approach. This research offers a scalable solution for IoT security, paving the way for improved anomaly detection and mitigation strategies in connected environments. The integration of machine learning algorithms with IoT infrastructure allows for real-time monitoring and proactive anomaly detection in diverse IoT applications. The proposed framework enhances security measures and contributes to the overall reliability and efficiency of connected systems.