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Smart Sensor Architectures for Multimedia Sensing in IoMT.

Javier Silvestre-Blanes1, Víctor Sempere-Payá2, Teresa Albero-Albero1

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Summary
This summary is machine-generated.

This study introduces a method to optimize the Interactive dynamic governor algorithm for embedded systems. It significantly improves power consumption by up to 62% without sacrificing application performance.

Keywords:
IoMTedge computinggovernornear sensor computing

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Embedded Systems

Background:

  • Modern embedded systems face constraints in computing power, energy consumption, cost, and network connectivity.
  • Trends like Industrial Internet of Things (IIoT), edge computing, and embedded vision exacerbate these challenges.
  • Processor frequency scaling is a key technique for energy reduction, but existing algorithms are not optimized for these advanced scenarios.

Purpose of the Study:

  • To present a novel method for determining the operating parameters of the 'Interactive' dynamic governor algorithm.
  • To enhance power efficiency in embedded systems without compromising application performance.
  • To address the lack of suitable algorithms for demanding workloads in IIoT and edge computing environments.

Main Methods:

  • Developed a method to define operating parameters for the 'Interactive' dynamic governor algorithm.
  • Evaluated the algorithm's performance under three distinct system load conditions (high, medium, low).
  • Measured improvements in power consumption and application performance.

Main Results:

  • The optimized 'Interactive' algorithm demonstrated significant power consumption improvements across different loads.
  • Observed power savings ranging from 62% (under high load) down to 26% (under lower load).
  • Crucially, these power savings were achieved without any reduction in application performance.

Conclusions:

  • The proposed method effectively optimizes the 'Interactive' governor for improved energy efficiency in resource-constrained embedded systems.
  • This approach is suitable for emerging applications like IIoT and edge computing.
  • The findings offer a practical solution for reducing energy consumption in modern embedded systems.