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Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things.

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Summary
This summary is machine-generated.

This study introduces a Sustainable Data-driven Secured optimization model (SDS-GIoT) for Green-IoT networks. It enhances connectivity and security using edge computing and dynamic programming for sustainable smart applications.

Keywords:
Internet of Thingsblockchainedge computinggreen processoptimizationsustainable computingtechnological development

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Environmental Science

Background:

  • Smart applications increasingly rely on wireless technologies and artificial intelligence.
  • The Internet of Things (IoT) offers cost-effective communication but lacks intelligent green computing strategies.
  • Green-IoT (G-IoT) networks face challenges in reliable connectivity and energy efficiency.

Purpose of the Study:

  • To propose a Sustainable Data-driven Secured optimization model (SDS-GIoT) for Green-IoT networks.
  • To enhance learning capabilities and optimize node selection for edge networks.
  • To address connectivity issues and improve security in resource-constrained IoT environments.

Main Methods:

  • Integration of edge computing with dynamic programming for enhanced learning.
  • Graph-based link predictions for optimal edge node identification.
  • Offloading services via edge computing to reduce processing load on low-constraint nodes.

Main Results:

  • The SDS-GIoT model successfully identifies optimal nodes and predicts links for edge networks.
  • It establishes sub-path routing for continuous data transfer during communication disruptions.
  • Edge computing effectively reduces processing power requirements for constrained devices.

Conclusions:

  • The SDS-GIoT model demonstrates significant improvements in Green-IoT network performance.
  • It offers a sustainable solution for smart applications by enhancing connectivity, security, and energy efficiency.
  • Experimental validation confirms the model's effectiveness against existing solutions.