ArticleSensors (Basel, Switzerland)2022
A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A translational IoT healthcare framework integrating secure sensing, energy-aware intelligence, and adaptive emergency response.Scientific reports · 2026Article
- Integrating Energy Efficiency into Healthcare Operations: A Discrete-Event Simulation Approach for Surgical Pathways.Healthcare (Basel, Switzerland) · 2026Article
- Optimization of hospital resource scheduling efficiency based on dynamic weighted distance anomaly detection algorithm.Scientific reports · 2026Article
- An integrated queuing and certainty factor theory model for efficient edge computing in remote patient monitoring systems.Scientific reports · 2025Article
- Priority-Aware Multi-Objective Task Scheduling in Fog Computing Using Simulated Annealing.Sensors (Basel, Switzerland) · 2025Article
- Internet of Medical Things Systems Review: Insights into Non-Functional Factors.Sensors (Basel, Switzerland) · 2025Review
- Quality of experience-aware application deployment in fog computing environments using machine learning.PeerJ. Computer science · 2025Article
- An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach.PloS one · 2024Article
- DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system.Scientific reports · 2023Article
- Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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