## 2 Literature Review  
### Related Works  
In 2019, [PERSON] et al. [20] developed a novel approach termed HealthFog that integrated \"ensemble deep learning in Edge computing\" and employed it to produce a realistic appliance for the automated analysis of heart disease. HealthFog delivered medical care as fog services using IoT and effectively managed health information of heart patients that arrived upon the user's request. In addition, FogBus was deployed for testing the performance of the developed approach in terms of jitter, accuracy, execution time, latency, and network bandwidth and power utilization. In 2020, [PERSON] et al. [21] presented an Energy Efficient Particle Swarm Optimization (EEPSO) method for the effectual assortment of Cluster Heads (CHs) amongst varied IoT devices. The IoT devices deployed for finding medical data were clustered and a Cluster Head (CH) was selected using EEPSOC. The chosen CH forwarded the information to cloud servers. Subsequently, the CH was accountable for conveying information from IoT devices to cloud servers via fog devices. Following this, an Artificial Neural Network (ANN) classifier was used for diagnosing the medical data in the cloud server to recognize the severity of disease. In 2020, [PERSON] et al. [22] presented a health monitor scheme, which applied a lightweight block encryption technique for the remote provision security of health data in cloud-oriented IoT. Here, the health status of patients was portrayed via forecasting essential circumstances through data mining techniques. It was used for examining their biological information by smarter health IoT, where a lightweight secured block encryption method was deployed for ensuring the patients' confidential data. The investigational outcome demonstrated that the K-star technique achieved the most results with high recall, precision, and accuracy. In 2018, [PERSON] et al. [9] presented a novel efficient model for diabetic disease and the associated health data were produced by exploiting the \"UCI Repository dataset\" and the medicinal sensors were used for forecasting the individuals who were suffering from diabetes. Moreover, a Neural Classifier was proposed for treating the diseases with rigorousness. Finally, experiments were conducted that proved the effectiveness of the suggested scheme over extant models. In 2017, [PERSON] et al. [3] adopted a novel IoT-oriented cloud-centric framework for disease diagnosis that predicted the probable disease and its severity level. Major terminologies were described to produce user-based health measures by analyzing the concepts of computational science. The resultants were evaluated after processing the health measures in a definite environment. Diagnostic systems were deployed using diverse algorithms and the results were evaluated depending on varied metrics. In 2020, [PERSON] et al. [23] introduced IoT-Pulse for predicting alcoholic obsession that offered realistic data using machine-learning in fog surroundings. Here, data were gathered from 300 patients with alcoholism as a case study for training machine-learning schemes. The performances of IoT Pulse were evaluated over extant works using different parameters together with precision, specificity and sensitivity and accuracy that showed higher improvements. In 2019, [PERSON] et al. [24] proposed an effective technique for identifying individuals affected by dengue at previous stages, so that the health check team could offer treatments. The technique presented here classified the individuals based on their symptoms and sent an alert to the individuals instantaneously via mobile device. The model aids medical professionals in discovering the impacts of disease by examining the results and acting efficiently within a restricted time period. In 2018, [PERSON] et al. [25] proposed a Fog-oriented model for health monitoring and fall recognition.