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 Concept Videos

Sleep-Wake Cycles01:24

Sleep-Wake Cycles

3.1K
Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
3.1K
Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

4.6K
The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent...
4.6K
Understanding Sleep01:11

Understanding Sleep

1.8K
Sleep, an essential biological state, involves significant reductions in physical activity, sensory awareness, and interaction with the environment. This complex physiological process is primarily regulated by specific brain regions, notably the hypothalamus and pons, which govern the sleep-wake cycle or circadian rhythm.
The circadian rhythm, a nearly 24-hour cycle, is deeply influenced by environmental light cues. Light exposure directly affects the hypothalamus, which in turn regulates...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Effect of EGR1/LIPT1 regulatory axis on cuproptosis in chromophobe renal cell carcinoma.

Briefings in functional genomics·2026
Same author

MEIS2 Modulates Oxidative Phosphorylation and ROS Generation to Affect CD8<sup>+</sup> T Cell Antitumor Immunity in Prostate Cancer.

The Prostate·2025
Same author

E2F1-induced transcriptional activation of MAL2 inhibits sunitinib sensitivity and promotes the malignant progression of bladder cancer.

Journal of chemotherapy (Florence, Italy)·2025
Same author

Defending Against Neural Network Model Inversion Attacks via Data Poisoning.

IEEE transactions on neural networks and learning systems·2025
Same author

SPC25 Activates the Warburg Effect to Inhibit Ferroptosis in Prostate Cancer Cells.

American journal of men's health·2024
Same author

Prognostic Value and Clinical Significance of Vesicular Transport-Related Genes in Clear Cell Renal Cell Carcinoma.

Kidney & blood pressure research·2024

Related Experiment Video

Updated: Mar 6, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.9K

A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks.

Dayong Ye, Minjie Zhang Au

    IEEE Transactions on Cybernetics
    |March 10, 2017
    PubMed
    Summary

    This study introduces a novel self-adaptive sleep/wake-up scheduling approach for wireless sensor networks. It enhances node lifetime by autonomously managing operation modes without sacrificing packet delivery efficiency.

    More Related Videos

    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
    10:56

    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

    Published on: August 2, 2017

    10.6K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K

    Related Experiment Videos

    Last Updated: Mar 6, 2026

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
    08:36

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

    Published on: August 8, 2019

    12.9K
    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
    10:56

    Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

    Published on: August 2, 2017

    10.6K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K

    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Network Engineering

    Background:

    • Wireless sensor networks (WSNs) face energy limitations due to unrechargeable sensor nodes.
    • Effective sleep/wake-up scheduling is crucial for maximizing WSN node lifetime and maintaining packet delivery efficiency.

    Purpose of the Study:

    • To propose a self-adaptive sleep/wake-up scheduling approach for WSNs.
    • To overcome the energy saving versus packet delivery delay tradeoff inherent in duty cycling methods.
    • To enable decentralized, autonomous operation mode decisions for individual sensor nodes.

    Main Methods:

    • A reinforcement learning technique is employed for decentralized, autonomous decision-making.
    • Each node independently determines its operational state (sleep, listen, or transmission) per time slot.
    • The approach avoids traditional duty cycling, eliminating its associated tradeoffs.

    Main Results:

    • Simulation results validate the proposed approach's effectiveness across diverse network conditions.
    • The method successfully balances energy conservation with efficient packet delivery.
    • Nodes autonomously adapt their sleep/wake-up schedules without central coordination.

    Conclusions:

    • The proposed self-adaptive scheduling approach offers a superior alternative to duty cycling in WSNs.
    • Reinforcement learning enables robust and efficient energy management in decentralized WSNs.
    • This method significantly enhances the operational lifetime of wireless sensor nodes.