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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on

Juan Ignacio Guerrero1,2, Antonio Martín1, Antonio Parejo2

  • 1Department of Electronic Technology, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, 41012 Sevilla, Spain.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

This study introduces a distributed analytic platform using edge computing to efficiently process complex calculations across networks. The novel distributed analytical engine (DAE) reduces communication by over 91% in smart grid analysis.

Keywords:
data integrationedge computinggenetic algorithmparticle swarm optimizationsmart grid

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Area of Science:

  • Computer Science
  • Data Science
  • Distributed Systems

Background:

  • Data fragmentation across diverse sources hinders analytical methods.
  • Centralizing data for complex modeling causes communication overload and privacy concerns.
  • Existing distributed data mining techniques often lack support for mathematical and stochastic models.

Purpose of the Study:

  • To propose a general-purpose distributed analytic platform leveraging edge computing.
  • To address the challenges of processing complex mathematical expressions in distributed environments.
  • To reduce data transmission and enhance privacy in distributed data analysis.

Main Methods:

  • Development of a distributed analytical engine (DAE) for decomposing and distributing calculations.
  • Utilizing computational intelligence algorithms (genetic algorithm, genetic algorithm with evolution control, particle swarm optimization) for task distribution.
  • Edge computing architecture to process data closer to the source.

Main Results:

  • The DAE successfully decomposes and distributes complex computational tasks.
  • Partial results are exchanged, avoiding the transmission of original sensitive data.
  • A case study on smart grid key performance indicators showed over 91% reduction in communication messages compared to traditional methods.

Conclusions:

  • The proposed edge computing-based distributed analytic platform effectively handles complex calculations in distributed networks.
  • The DAE mitigates communication bottlenecks and privacy issues associated with data centralization.
  • The platform demonstrates significant efficiency gains in real-world applications like smart grids.