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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data

Ali Yavari1,2, Harindu Korala3, Dimitrios Georgakopoulos2

  • 16G Research and Innovation Lab, Swinburne University of Technology, Melbourne, VIC 3122, Australia.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

Sazgar IoT processes data directly on Internet of Things (IoT) devices, eliminating network delays and high costs for time-sensitive applications. This novel framework ensures timely and accurate data analysis using on-device computation and approximation techniques.

Keywords:
COVID-19Internet of Thingsapproximationcontextualisationpandemictime-sensitive computing

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Area of Science:

  • Computer Science
  • Data Science
  • Internet of Things

Background:

  • Cloud and edge-based Internet of Things (IoT) data analysis face network delays and high costs.
  • These issues negatively impact time-sensitive IoT applications.

Purpose of the Study:

  • Propose Sazgar IoT, a novel framework to address challenges in time-sensitive IoT data analysis.
  • Leverage on-device IoT computing resources and approximation techniques to meet application time-bounds.

Main Methods:

  • Utilize onboard computing resources of IoT devices for data analysis tasks.
  • Employ approximation techniques for data analysis to meet time-bound and accuracy requirements.
  • Optimize processing based on available computing resources.

Main Results:

  • Sazgar IoT successfully met time-bound and accuracy requirements for a COVID-19 citizen compliance monitoring application.
  • Experimental validation confirmed the framework's efficiency and scalability.
  • Demonstrated significant cost reduction compared to cloud and edge computing solutions.

Conclusions:

  • Sazgar IoT provides an efficient and scalable solution for time-sensitive IoT data processing.
  • The framework effectively mitigates network delays and reduces costs associated with cloud/edge infrastructure.
  • Sazgar IoT is a viable approach for real-time IoT applications requiring high performance and cost-effectiveness.