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Exploring Smartphone-Based Edge AI Inferences Using Real Testbeds.

Sensors (Basel, Switzerland)·2025
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Updated: Aug 10, 2025

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

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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.