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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

700
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
700
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.2K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K

You might also read

Related Articles

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

Sort by
Same author

Exploring Smartphone-Based Edge AI Inferences Using Real Testbeds.

Sensors (Basel, Switzerland)·2025
See all related articles

Related Experiment Video

Updated: Aug 10, 2025

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay
07:39

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Published on: February 24, 2023

10.0K

Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm.

Virginia Yannibelli1, Matías Hirsch1, Juan Toloza1

  • 1ISISTAN (UNICEN-CONICET), Tandil 7000, Argentina.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an evolutionary algorithm to optimize smartphone battery charging for dew computing testbeds. The method significantly reduces the time needed to prepare multiple smartphones for energy utilization experiments.

Keywords:
Motrolbenchmarkingdew computingevolutionary computingprofilingsmartphones

More Related Videos

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device
14:48

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Published on: April 17, 2021

4.1K
Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
10:50

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

9.8K

Related Experiment Videos

Last Updated: Aug 10, 2025

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay
07:39

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Published on: February 24, 2023

10.0K
Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device
14:48

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Published on: April 17, 2021

4.1K
Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
10:50

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

9.8K

Area of Science:

  • Computer Science
  • Distributed Computing
  • Artificial Intelligence

Background:

  • Dew computing leverages nearby devices like smartphones for computation, reducing reliance on remote clouds.
  • Smartphone clusters are explored for computational tasks, necessitating efficient load balancing and energy management.
  • Evaluating energy-based load balancing strategies in real-world testbeds is time-consuming due to battery preparation.

Purpose of the Study:

  • To develop a method for minimizing smartphone battery preparation time in dew computing testbeds.
  • To address the challenge of synchronizing battery levels across multiple smartphones for experimental repetition.

Main Methods:

  • An evolutionary algorithm was designed to create optimized smartphone battery (dis)charging plans.
  • The algorithm incorporates variable charging speeds by utilizing energy-intensive components (CPU, screen) during charging.
  • The approach was evaluated using real-world smartphone battery charge/discharge traces.

Main Results:

  • The proposed evolutionary algorithm effectively minimizes the time required to prepare a set of smartphones for testing.
  • Comparison with individual maximum-speed charging demonstrated significant time reductions.
  • The algorithm's charging plans proved efficient in synchronizing battery levels across devices.

Conclusions:

  • The developed evolutionary algorithm offers an effective solution for reducing test preparation time in dew computing research.
  • Optimized battery management is crucial for efficient and repeatable energy utilization experiments with smartphone clusters.
  • This work facilitates more streamlined evaluation of load balancing strategies in edge computing environments.