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Biometrics-based Internet of Things and Big data design framework.

Kenneth Li-Minn Ang1, Kah Phooi Seng2

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Summary

This paper introduces a framework for Application Specific Internet of Things (ASIoT) designs using biometrics. The proposed seven-layer BiometricIoT architecture addresses challenges in resource-constrained devices, multimedia data, and big data processing for enhanced security.

Keywords:
Application specificBig dataInternet-of-Thingsbiometricparallel computation

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Area of Science:

  • Computer Science
  • Information Technology
  • Cybersecurity

Background:

  • Internet of Things (IoT) devices face limitations with resource-intensive security protocols.
  • Biometric systems generate multimedia data, increasing processing demands.
  • The proliferation of biometrics-based IoT generates significant big data challenges.

Purpose of the Study:

  • To propose a framework for designing Application Specific Internet of Things (ASIoT) tailored for biometrics applications.
  • To present a comprehensive seven-layer architecture for Biometrics-based IoT (BiometricIoT) systems.
  • To address key challenges including hardware constraints, multimedia data handling, and big data processing in biometrics IoT.

Main Methods:

  • Development of a seven-layer BiometricIoT architecture.
  • Analysis of design factors: parallel divide-and-conquer (D&C) computation, computational complexity, device security, and algorithm efficacies.
  • Experimental validation of the D&C approach for efficiency.

Main Results:

  • The proposed BiometricIoT architecture effectively handles challenges posed by biometrics applications.
  • Experimental results demonstrate the effectiveness of the parallel divide-and-conquer computation strategy.
  • The framework provides a viable approach for resource-constrained biometrics IoT systems.

Conclusions:

  • The developed BiometricIoT architecture offers a robust solution for secure and efficient biometrics-based IoT applications.
  • The study validates the efficacy of D&C computation in managing big data and computational load.
  • Further research and development of ASIoTs for biometrics are motivated by these findings.