Jove
Visualize
Contact Us

Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

828
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...
828
Energy Stored In A Coaxial Cable01:31

Energy Stored In A Coaxial Cable

2.0K
A coaxial cable consists of a central copper conductor used for transmitting signals, followed by an insulator shield, a metallic braided mesh that prevents signal interference, and a plastic layer that encases the entire assembly.
In the simplest form, a coaxial cable can be represented by two long hollow concentric cylinders in which the current flows in opposite directions. The magnetic field inside and outside the coaxial cable is determined by using Ampère's law. The magnetic field inside...
2.0K
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

10.6K
Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
10.6K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

593
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.
593
Energy Budgets00:51

Energy Budgets

10.6K
Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
10.6K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

728
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:
728
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
  1. Home
  3. Cooperative Schemes For Joint Latency And Energy Consumption Minimization In Uav-mec Networks.
  1. Home
  3. Cooperative Schemes For Joint Latency And Energy Consumption Minimization In Uav-mec Networks.

Related Experiment Video

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

8.5K

Cooperative Schemes for Joint Latency and Energy Consumption Minimization in UAV-MEC Networks.

Ming Cheng1, Saifei He1, Yijin Pan2

  • 1School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|September 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We propose two multi-agent deep reinforcement learning schemes for Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) to optimize latency and energy consumption in the Internet of Things (IoT). Both schemes effectively manage complex, dynamic environments.

Keywords:
closed-form enhanced multi-armed bandit (CF-MAB)energy consumptionlatencymobile edge computing (MEC)multi-UAV networksmulti-agent proximal policy optimization (MAPPO)

More Related Videos

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

1.1K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Related Experiment 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

8.5K
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

1.1K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Telecommunications Engineering

Background:

  • Internet of Things (IoT) applications demand massive device collaboration, heavy computation, and low latency.
  • Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) offers flexible, wide-coverage services for User Devices (UDs).
  • Optimizing the trade-off between latency and energy consumption in dynamic, large-scale networks is a significant challenge.

Purpose of the Study:

  • To jointly optimize association, offloading, and computing resource allocation in UAV-assisted MEC systems.
  • To address the inherent trade-off between latency and energy consumption for improved system performance.
  • To develop intelligent schemes capable of handling complex and dynamic network environments.

Main Methods:

  • Formulated the joint optimization problem as a Partially Observable Markov Decision Process (POMDP).
  • Developed two multi-agent deep reinforcement learning (DRL) schemes: Multi-Agent Proximal Policy Optimization (MAPPO) and Closed-Form Enhanced Multi-Armed Bandit (CF-MAB).
  • UDs act as independent agents learning from interactions and historical data to maximize individual rewards, fostering implicit collaboration.

Main Results:

  • Both proposed DRL schemes demonstrate effectiveness in balancing latency and energy consumption.
  • The MAPPO scheme excels in collaborative decision-making for high performance in complex, dynamic environments.
  • The CF-MAB scheme provides rapid, independent response decisions by decoupling association from offloading and resource allocation.

Conclusions:

  • The proposed multi-agent DRL approaches successfully tackle the challenges of joint optimization in UAV-assisted MEC systems.
  • MAPPO and CF-MAB offer distinct advantages for different operational needs, enhancing system performance and efficiency.
  • These intelligent schemes provide a robust solution for future large-scale IoT applications requiring efficient resource management.