Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal

Zhichen Ni, Honglong Chen, Zhe Li

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 30, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Optimizing an ultrasonic levitator with two confronting emitters to levitate wavelength-scaled disks.

    The Journal of the Acoustical Society of America·2026
    Same author

    Neurologic Diagnoses Before and After Traumatic Brain Injury: A Retrospective Cohort Study of Older Veterans.

    Neurology·2026
    Same author

    The surgical outcomes of modified Chen's U-suture technique compared with duct-to-mucosa anastomosis in laparoscopic pancreaticoduodenectomy: a multi-center cohort study.

    Surgical endoscopy·2026
    Same author

    Factors associated with persistent postoperative hyperthermia in pediatric craniocerebral injury: a retrospective cohort study and preliminary risk score.

    Perioperative medicine (London, England)·2026
    Same author

    Association of a standardized nursing protocol with postoperative complications following open radical gastrectomy: a retrospective cohort study.

    Open medicine (Warsaw, Poland)·2026
    Same author

    Hepatocellular Carcinoma Treatment with Immune Checkpoint Inhibitors: RECA and CRAFITY Scores Reveal Distinct Clinical Courses and Highlight the Role of Systemic Inflammation in Prognosis.

    Biomedicines·2026

    This study introduces a new Multi-Scenario offloading schedule (MSCET) for processing driver biometric data in connected vehicles. MSCET optimizes task offloading across cloud, edge, and vehicle systems to enhance performance.

    Area of Science:

    • Computer Science
    • Biomedical Engineering
    • Network Engineering

    Background:

    • Vehicular networks generate complex biomedical data requiring intensive processing.
    • Resource limitations in vehicles and edge servers challenge real-time data analysis.
    • Existing offloading strategies often neglect the collaborative potential of cloud, edge, and terminal resources.

    Purpose of the Study:

    • To propose a novel offloading schedule for biomedical data processing in collaborative vehicular networks.
    • To address the limitations of traditional edge-centric offloading by integrating cloud resources.
    • To optimize system utility by managing computational tasks across multiple scenarios.

    Main Methods:

    • Developed a Multi-Scenario offloading schedule (MSCET) for Cloud-Edge-Terminal collaborative vehicular networks.

    Related Experiment Videos

  • Constructed a virtual resource pool integrating multiple Edge Servers (ESs).
  • Optimized MSCET parameters to maximize overall system utility.

    • Main Results:

    • Simulations demonstrated the effectiveness of the proposed MSCET.
    • MSCET significantly outperformed existing offloading schedules in performance.
    • The collaborative approach effectively managed computation-intensive and delay-sensitive tasks.

    Conclusions:

    • The MSCET provides an efficient solution for biomedical data processing in advanced vehicular networks.
    • Cloud-edge-terminal collaboration is crucial for overcoming resource constraints and enhancing driver monitoring.
    • The proposed method offers a scalable and optimized approach for future intelligent transportation systems.