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

Protein Networks02:26

Protein Networks

4.7K
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,...
4.7K
Probability Distributions01:32

Probability Distributions

13.4K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
13.4K
Uniform Distribution01:19

Uniform Distribution

6.6K
The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
6.6K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

311
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
311
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

11.1K
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...
11.1K

You might also read

Related Articles

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

Sort by
Same journal

Trap tales: The influence of red alder stand conditions and forest fragmentation on family-level beetle bycatch diversity.

PloS one·2026
Same journal

MamNet-PT: A Mamba-enhanced hybrid architecture with selective state-space modeling for uncertainty-aware brain tumor segmentation.

PloS one·2026
Same journal

Multicenter evaluation of BACT-Info. and an infection algorithm using Urine Flow Cytometry among clinically diagnosed UTI patients in Indonesia.

PloS one·2026
Same journal

Cross-cultural adaptation and psychometric properties study of Prolonged Grief Disorder Questionnaire (PG-12-R) for caregivers of terminal cancer patients, Thai version.

PloS one·2026
Same journal

Design and in silico validation of donor DNA for RNA-guided recombinase-mediated knockout of mstnb gene in Labeo rohita.

PloS one·2026
Same journal

ViT-MultiRAGNet: A scalable and reliable retrieval-augmented Vision Transformer framework for memory-guided feature fusion multi-modal mammogram classification.

PloS one·2026

Related Experiment Video

Updated: Apr 1, 2026

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

1.2K

Bayes Node Energy Polynomial Distribution to Improve Routing in Wireless Sensor Network.

Thirumoorthy Palanisamy1, Karthikeyan N Krishnasamy2

  • 1Department of Computer Science and Engineering, Nandha Engineering College,Erode, Tamilnadu.

Plos One
|October 2, 2015
PubMed
Summary

This study introduces a new Bayes Node Energy and Polynomial Distribution (BNEPD) technique to improve wireless sensor networks (WSN). The method reduces energy consumption and communication overhead for more efficient data routing.

Related Experiment Videos

Last Updated: Apr 1, 2026

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

1.2K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSN) face challenges with high delay and energy consumption due to continuous data collection.
  • Existing routing methods in WSNs often lead to increased energy drain rates and communication overhead.
  • Efficient data management and routing are critical for the performance and longevity of WSNs.

Purpose of the Study:

  • To introduce an energy-aware routing technique for WSNs that minimizes energy consumption and delay.
  • To propose the Bayes Node Energy and Polynomial Distribution (BNEPD) technique for efficient data routing.
  • To reduce the energy drain rate and communication overhead in wireless sensor networks.

Main Methods:

  • The Bayes Node Energy Distribution (BNED) method groups sensor nodes detecting similar events using Bayes' rule.
  • Polynomial Regression Function is applied to combine and process data from sensors detecting similar events.
  • The Poly Distribute algorithm optimizes sensor node distribution and creates energy-efficient routing paths.

Main Results:

  • The proposed BNEPD technique significantly reduces the energy drain rate of sensor nodes.
  • Data aggregation at the sink node, based on polynomial regression, minimizes communication overhead.
  • The method ensures fairness among different users while enhancing network efficiency.

Conclusions:

  • The BNEPD technique offers an effective solution for energy-efficient routing in WSNs.
  • This approach addresses the limitations of continuous data collection in traditional WSN methods.
  • The study demonstrates substantial improvements in reducing energy consumption and communication overhead in WSNs.