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

Short-distance Transport of Resources02:12

Short-distance Transport of Resources

18.1K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
18.1K
Distributed Loads01:19

Distributed Loads

1.1K
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
1.1K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
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...
1.2K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

847
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:
847

You might also read

Related Articles

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

Sort by
Same author

HYDRA-XAI dual-backbone disaster scene recognition using ResNet50-Swin transformer feature fusion, explainable evidence, and an operational recommender.

Scientific reports·2026
Same author

Sustainable RCCI engine operation with an ANN based novel tri-fuel approach.

Scientific reports·2025
Same author

Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.

PloS one·2025
Same author

Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments.

Scientific reports·2025
Same author

Thermal performance prediction of a V-trough solar water heater with a modified twisted tape using ANFIS, G.L.R., R.T. and SVM models of machine learning.

Scientific reports·2024
Same author

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks.

Scientific reports·2024

Related Experiment Video

Updated: May 23, 2026

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

DRLO-VANET: a deep reinforcement learning-based offloading framework for low-latency and energy-efficient task

Sadineni Neelima1, S Rama Sree2, N Ramakrishnaiah3

  • 1Research Scholar, Department of CSE, UCEK, JNTUK Kakinada, India. neelima.sadineni@gmail.com.

Scientific Reports
|March 30, 2026
PubMed
Summary

This study introduces DRLO-VANET, a deep reinforcement learning framework for autonomous vehicles. It dynamically optimizes task execution between local processing and Multi-access Edge Computing (MEC), significantly reducing latency and energy use.

Keywords:
Autonomous vehiclesDeep reinforcement learningMulti-access edge computing (MEC)Task offloadingVehicular Ad hoc networks

Related Experiment Videos

Last Updated: May 23, 2026

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

Area of Science:

  • * Vehicular networking and edge computing.
  • * Artificial intelligence for intelligent transportation systems.

Background:

  • * Existing Multi-access Edge Computing (MEC) task execution strategies for autonomous vehicles face scalability and efficiency issues in dense traffic.
  • * Challenges include imbalanced roadside unit (RSU) utilization, high handover rates, and substantial energy consumption.
  • * There is a need for dynamic, multi-objective decision-making frameworks for real-time trade-offs in vehicular environments.

Purpose of the Study:

  • * To design and evaluate DRLO-VANET, a novel deep reinforcement learning framework for dynamic task offloading decisions in vehicular networks.
  • * To enable real-time optimization of latency, energy consumption, task completion, RSU utilization, and handover overhead.
  • * To address the limitations of current task execution strategies in challenging vehicular mobility and network conditions.

Main Methods:

  • * Development of DRLO-VANET, integrating NS-3 network simulator with ns3-gym for online learning using Deep Q-Networks (DQN) and Soft Actor-Critic (SAC).
  • * Definition of a global state space incorporating vehicular density, RSU load, task size, and channel quality.
  • * Joint optimization of multiple objectives including latency, energy, task completion ratio, RSU utilization, and handover overhead.

Main Results:

  • * DRLO-VANET reduces task execution latency by up to 40% and saves 30%-35% energy.
  • * Task completion ratio exceeds 90% in medium-density scenarios.
  • * Handover frequency is decreased by nearly 50% compared to baseline methods.
  • * DRL-activated policies demonstrate superior performance in balancing responsiveness and system stability.

Conclusions:

  • * DRLO-VANET provides a scalable, adaptive, and efficient framework for real-time task offloading in autonomous vehicles.
  • * The proposed framework effectively optimizes vehicular task execution, enhancing reliability and responsiveness.
  • * Proven performance through simulations makes DRLO-VANET suitable for next-generation autonomous transport systems.