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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250074" target="_blank" >RIV/61989100:27240/22:10250074 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/1424-8220/22/14/5327" target="_blank" >https://www.mdpi.com/1424-8220/22/14/5327</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s22145327" target="_blank" >10.3390/s22145327</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20200 - Electrical engineering, Electronic engineering, Information engineering

Result continuities

  • Project

    <a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Sensors

  • ISSN

    1424-3210

  • e-ISSN

    1424-8220

  • Volume of the periodical

    22

  • Issue of the periodical within the volume

    14

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    36

  • Pages from-to

    nestrankovano

  • UT code for WoS article

    000833827800001

  • EID of the result in the Scopus database