Details of Research Outputs

In the domains of health and medicine, current technology facilitates the quicker identification of effective solutions. Smart electronic health networks based on IoT–fog are one of these technologies. It combines the Internet of Things with computing in a fog environment to enable fast and accurate health data processing, transfer, and collecting from patient devices and sensors for caregivers. To reduce fog computing burden and enhance resource allocation, the concept of combining fog computing with the Internet of Things (IoT) has been put out. This research provides a novel method of applying inertial weighted multi-objective particle swarm optimization to optimize simulated e-health smart networks. The term “IoT–fog SEH” refers to this specific technique. The IoT–fog SEH’s (Smart E-Health) notable importance of inertia weight makes it easier to modify the search space’s dimensions and get the best answer. The IoT–fog SEH approach is used to compare the Cloud-HMS algorithm, Throttled method, and HGWDE algorithm. In terms of reaction time, IoT–fog SEH beats Cloud-HMS, Throttled, and HGWDE algorithms, with improvements of 52.86, 81.02, and 80.44 ms, respectively. IoT–fog SEH beats Cloud-HMS, Throttled, and HGWDE by 51.87, 80.12, and 79.64 ms, respectively, in processing time. The HGWDE algorithm performs better in terms of cost efficiency than the IoT–fog SEH method. It is important to keep in mind that there is no statistically significant difference between these two approaches. The investigated approach was evaluated with the iFogSim program, and the results were contrasted with those obtained with the current methodology. Experimental results show a significant reduction in latency, energy consumption, and network bandwidth use when comparing this study’s methodology to previous research endeavors. Specifically, the recommended method leads to a 25% reduction in network bandwidth usage, a 37% reduction in energy consumption, and a 45% reduction in delay.