Jove
Visualize
Contact Us

Related Concept Videos

Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Design of Transmission Shafts01:16

Design of Transmission Shafts

777
The design of a transmission shaft is governed by two primary specifications: the power it transmits and its rotational speed. These parameters guide the selection of the shaft's material and cross-sectional dimensions, ensuring that the material's maximum shearing stress remains within the elastic limit while transmitting the desired power at the given speed. The system's power is intrinsically linked to the applied torque. The torque applied to the shaft can be calculated by reconfiguring the...
777
Transmission Line Design Considerations01:23

Transmission Line Design Considerations

629
Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
629
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

505
Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
Next, use bending moment diagrams for the shaft to...
505

You might also read

Related Articles

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

Sort by
Same author

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization.

Sensors (Basel, Switzerland)ยท2018
See all related articles
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 Video

Updated: Jan 29, 2026

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device
07:05

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device

Published on: April 4, 2018

14.9K

Offloading and Transmission Strategies for IoT Edge Devices and Networks.

Jiheon Kang1, Doo-Seop Eom2

  • 1School of Electrical Engineering, Korea University, Seoul 02841, Korea. kanghead@korea.ac.kr.

Sensors (Basel, Switzerland)
|February 21, 2019
PubMed
Summary

We developed a machine learning approach for efficient data transmission and model offloading in resource-constrained Internet of Things (IoT) edge devices. This method optimizes energy use and reduces latency for time-sensitive IoT applications.

Keywords:
deep learningedge computinginternet of thingsoffloading

More Related Videos

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
08:42

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

12.6K
Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics
10:39

Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics

Published on: August 5, 2020

7.4K

Related Experiment Videos

Last Updated: Jan 29, 2026

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device
07:05

Perfusable Vascular Network with a Tissue Model in a Microfluidic Device

Published on: April 4, 2018

14.9K
Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
08:42

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

12.6K
Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics
10:39

Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics

Published on: August 5, 2020

7.4K

Area of Science:

  • Computer Science
  • Electrical Engineering

Background:

  • The proliferation of diverse Internet of Things (IoT) devices generates vast data volumes, straining limited bandwidth and causing latency.
  • Transmitting large datasets from edge devices to servers is costly and inefficient for time-sensitive operations.

Purpose of the Study:

  • To propose an efficient machine and deep learning method for offloading trained models and transmitting data packets from resource-constrained IoT edge devices.
  • To optimize energy efficiency, execution time, and packet count for IoT edge device operations.

Main Methods:

  • A novel offloading and transmission policy was developed, incorporating reinforcement learning to determine optimal contention window sizes for medium access control (MAC) protocols.
  • The method evaluates strategies for transmitting raw data, feature vectors, or model outputs, considering microprocessor and radio transceiver power consumption and transmission latency.
  • Performance was evaluated using ARM Cortex-M4 and Cortex-M7 processors in network simulations.

Main Results:

  • The proposed adaptive channel access and learning-based offload/transmission methods demonstrated superior performance compared to conventional schemes.
  • The methods effectively handle raw data packet transmission, proving beneficial for IoT edge devices and network protocols.

Conclusions:

  • The developed approach offers an effective solution for efficient data handling and model execution on resource-constrained IoT edge devices.
  • This research contributes to improving the performance and efficiency of IoT networks through intelligent offloading and transmission strategies.