In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

College of Computer Science and Information Technology University of Anbar Ramadi 31001 Iraq

Department of Computer Engineering Faculty of Engineering Al Hussein Bin Talal University Ma'an 71111 Jordan

Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 70800 Ostrava Czech Republic

Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 70800 Ostrava Czech Republic

Industrial Engineering Brose Group Prumyslovy Park 302 74221 Koprivnice Czech Republic

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DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system