A novel low-latency and energy-efficient task scheduling framework for Internet of Medical Things in an edge fog cloud system

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.Web of Science2214art. no. 532

Energy-Conservation Clustering Protocol based on

One of the most critical issues in wireless sensor networks is energy efficiency because of the limited energy network nodes can store in batteries. Therefore, these networks require robust wireless communication protocols that are designed to be energy-efficient. In this paper, we introduce a Routing protocol based on a Energy-Temperature Transformation principle, called RETT. Our objectives are to maximize the lifespan of the entire network rather than maximizing the life span for individual nodes. We do this to avoid sections of the network becoming unreachable when critical nodes along a routing path run out of power. In RETT, which is a cluster based protocol, the head of cluster will be able to select the optimal route for sending or relaying the data from the source towards the base station. RETT is based on a thermo dynamic analogy where by expected life spans are transformed into temperatures and the routing algorithm is searching for the hottest path between the source and the destination