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

Distribution Reliability and Automation01:25

Distribution Reliability and Automation

497
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
497
Cognitive Learning01:21

Cognitive Learning

1.0K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

647
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
647
Machines: Problem Solving I01:22

Machines: Problem Solving I

689
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
689
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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...
1.1K
Quality Assurance01:19

Quality Assurance

956
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
956

You might also read

Related Articles

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

Sort by
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer scienceยท2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer scienceยท2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer scienceยท2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer scienceยท2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer scienceยท2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer scienceยท2026

Related Experiment Video

Updated: Jan 17, 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

8.5K

Quality of experience-aware application deployment in fog computing environments using machine learning.

P Jenifer1, J Angela Jennifa Sujana2

  • 1Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary

The Energy-Smart Component Placement (ESCP) algorithm optimizes artificial intelligence (AI) workloads on edge devices, improving efficiency and reducing energy consumption. This framework ensures quality of service and experience for real-time applications.

Keywords:
Cloud computingEdge intelligenceFog edge devices as a serviceLong short term memoryMachine learningMeta-heuristicMobileNet-V3Quality of experienceQuality of serviceXGBoost

Related Experiment Videos

Last Updated: Jan 17, 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

8.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • Edge intelligence is crucial for real-time sensor data processing, but faces challenges with bandwidth, latency, and data privacy.
  • Existing solutions struggle to efficiently deploy artificial intelligence (AI) workloads on resource-constrained edge devices.
  • The need for a dynamic architecture that guarantees quality of service (QoS) and quality of experience (QoE) in cloud-fog-edge environments is growing.

Purpose of the Study:

  • To introduce the Energy-Smart Component Placement (ESCP) algorithm for optimizing AI workload deployment on fog edge devices.
  • To develop a reliable and dynamic architecture for Fog Edge Devices as a Service (FEdaaS) that ensures QoS and QoE.
  • To enhance energy efficiency and performance of AI-edge systems.

Main Methods:

  • Developed the Energy-Smart Component Placement (ESCP) algorithm for fog devices (FCMNs, FNs) to allocate modules and deactivate inactive devices.
  • Implemented a meta-heuristic scheduler combining eXtreme Gradient Boosting (XGB) for instantaneous QoS scoring and Long Short-Term Memory (LSTM) for node congestion forecasting.
  • Designed a framework to transparently distribute compressed neural workloads across serverless cloud, fog, and extreme edge layers.

Main Results:

  • ESCP improved bandwidth utilization by 5.2%, scalability by 3.2%, energy consumption by 3.8%, and response time by 2.1% compared to a cloud-only baseline.
  • Maintained prediction accuracy within +0.4% while meeting QoE targets like 250 ms latency and 24-hour battery life for low-resource AI-edge devices.
  • Demonstrated the feasibility of orchestrating AI-edge devices through an adaptive framework to meet stringent application-level QoS and QoE demands.

Conclusions:

  • The ESCP algorithm and adaptive framework effectively optimize AI workloads on edge devices, enhancing performance and energy efficiency.
  • The proposed FEdaaS architecture provides a reliable solution for deploying AI services across cloud, fog, and edge layers.
  • Future work includes exploring federated learning for privacy and validating the architecture in real-time intensive care and smart city applications.