A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Abstract

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.

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

“…There have been attempts to construct hierarchies among cloud nodes (e.g., cloudlet [1]) or FNs (e.g., multi-layer FNs [17]), aiming to increase utilization efficiency while reducing latency, as these are still not replaceable in fog-cloud hierarchical task scheduling with more diverse features. The most adopted strategies for fog-cloud task scheduling generally follow the principle that latencytolerant and large-size tasks are assigned to cloud nodes, and latency-sensitive tasks, to FNs [9], based on which minimizing the overall makespan, maximizing resource utilization efficiency, or load balancing is targeted [16,[18][19][20][21].…”

Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things

“…A plethora of works have aimed at the optimisation of IoT; however, such works either focus on the allocation of resources to the detriment of resource scheduling and vice versa or are domain-specific. Domain-specific works, for instance, include the works that focus on task scheduling and/or task management in the health sector [ 24 , 25 , 26 ], or on task offloading and scheduling in transport applications [ 27 ], or even on industrial automations [ 3 ]. It therefore suffices to say that a generalised IoT framework applicable in all IoT scenarios is yet to be fully accepted and implemented.…”

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

“…latency is the time delay between the input and output of a computation. It is a crucial factor for the performance and reliability of embedded biomedical devices [47]. Embedded biomedical devices typically have limited resources, such as memory, power, and bandwidth, and need to process large amounts of data from sensors or other sources.…”