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.

IoT is capable and helpful in interdisciplinary areas along with the convergence of multiple technologies and platforms. This study adheres the adaptation of data mining technologies along with big data and cloud computing with the IoT system with detailed discussion. This paper supports and provide systematic review and analysis based on the computational parameters and performance analysis. The analysis and discussion are based on the communication capability, system component communication, aspects of data mining, big data and cloud computing in IoT. Different types of transmission and communication barriers have also been discussed and analyze. Finally, based on the study and analysis a framework has been suggested for the smooth functioning of the IoT protocols.

Fog computing has grown in popularity in recent years because to its potential to deliver real-time processing, low latency, and reduce network congestion. However, the implementation of Internet of Things (IoT) enabled smart devices in environments using fog computing may lead to resource limitations and higher computational demands. Load balancing and fault tolerance strategies are necessary to tackle these difficulties for optimal resource usage and system availability. In order to accomplish fault tolerance aware load balancing in fog computing, a hybrid meta-heuristic approach in this study that combines the Modified Harris-Hawks Optimization (MHHO) and Ant Colony Optimization (ACO) is proposed. The MHHO algorithm is utilized for load balancing, whereas the ACO algorithm is used for fault tolerance. By employing the proposed technique, the load on fog nodes is balanced, the makespan time is minimized, energy consumption and execution costs are minimized, and fault tolerance in fog computing environments is ensured. It is evaluated using a simulation framework built on the iFogSim toolkit. In terms of load balancing, fault tolerance, and other factors, the results of the experiments show that the suggested hybrid algorithm performs better than earlier state-of-the-art methods.