Jove
Visualize
Contact Us

Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

131
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
131
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

229
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
229
Energy and Power Signals01:17

Energy and Power Signals

322
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
322
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

666
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
666
Maximum Power Transfer01:16

Maximum Power Transfer

278
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
278
Electrical Energy01:10

Electrical Energy

1.2K
Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
1.2K

You might also read

Related Articles

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

Sort by
Same author

Development of a Fully Autonomous Offline Assistive System for Visually Impaired Individuals: A Privacy-First Approach.

Sensors (Basel, Switzerland)·2025
Same author

On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections.

Sensors (Basel, Switzerland)·2020
Same author

FDIPP: False Data Injection Prevention Protocol for Smart Grid Distribution Systems.

Sensors (Basel, Switzerland)·2020
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: Jul 15, 2025

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

591

Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking.

Ramiza Shams1, Atef Abdrabou1, Mohammad Al Bataineh1,2

  • 1Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al-Ain P.O. Box 15551, Abu Dhabi, United Arab Emirates.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study introduces a new method using software-defined networking and deep neural networks to manage energy consumption in multiconnected devices. The approach effectively reduces power usage while improving network performance for 5G and beyond wireless networks.

Keywords:
congestion controlenergy consumptionmulticonnectivitymultihomingmultipath TCPneural networkssoftware-defined networkingwireless

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jul 15, 2025

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

591
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Multiconnectivity enables simultaneous connections to multiple radio access technologies (5G, 4G LTE, WiFi), crucial for meeting escalating mobile data demands.
  • Multipath TCP (MPTCP) facilitates reliable data transmission over these diverse links, but increases energy consumption in battery-powered devices.
  • Managing energy efficiency in multihomed wireless devices is a significant challenge for current and future mobile networks.

Purpose of the Study:

  • To develop and evaluate an energy management strategy for multiconnected devices utilizing MPTCP.
  • To leverage Software-Defined Networking (SDN) and Deep Neural Networks (DNNs) for optimizing energy consumption.
  • To enhance network throughput performance alongside energy savings.

Main Methods:

  • Implementation of two lightweight algorithms on an SDN controller for managing multiconnectivity.
  • Utilizing a hardware testbed with dual-homed wireless nodes connected to WiFi and cellular networks.
  • Employing a DNN trained on diverse network scenarios to refine network connection decisions.

Main Results:

  • The proposed SDN and DNN-based approach significantly reduces device energy consumption.
  • The method achieves improved network throughput performance compared to single-path TCP and standard MPTCP algorithms (Cubic, BALIA).
  • Experimental validation demonstrates the effectiveness of the algorithms in real-world dual-homed network environments.

Conclusions:

  • SDN and DNN integration offers an effective solution for managing energy consumption in multiconnected devices.
  • The developed algorithms provide a practical method for optimizing resource utilization in 5G and future wireless networks.
  • This approach balances the need for high performance with the critical requirement of energy efficiency for mobile devices.