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Optimized IoT protocol stack for seamless smart home communication using Random Forest-based interoperability

A Sriram1, D Manikandan2, R Venketesan3

  • 1Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, Tamilnadu, India.

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|November 17, 2025
PubMed
Summary

This study presents an optimized Internet of Things (IoT) protocol stack using machine learning to enhance smart home device communication. The system improves connectivity, energy efficiency, and user experience by intelligently managing device interactions.

Keywords:
Adaptive interfaceDevice compatibilityIntelligent systemInteroperabilityIoTMachine learningProtocol stackRandom forestSeamless communicationSmart home

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Smart homes feature diverse devices with incompatible communication protocols, hindering seamless interoperability.
  • Existing solutions often lack adaptability, leading to communication failures and suboptimal performance.

Purpose of the Study:

  • To introduce an optimized and harmonized Internet of Things (IoT) protocol stack for smart home environments.
  • To develop a self-adaptive, machine learning-based system for improved device communication and network management.

Main Methods:

  • Utilized a Random Forest algorithm for interworking analysis to identify connectivity patterns and predict optimal communication pathways.
  • Implemented a system architecture comprising a context-aware protocol manager, learning-driven communication controller, and adaptive interface mapper.

Main Results:

  • Achieved a communication success rate exceeding 85% and a compatibility score over 0.7 for 70% of usage time.
  • Maintained latency under 150 milliseconds in 70% of cases, demonstrating efficient data exchange.
  • Reported up to 30% energy savings due to the integrated AI framework.

Conclusions:

  • The proposed IoT protocol stack significantly enhances smart home device interconnectivity, energy efficiency, and user experience.
  • The machine learning-based approach provides a robust and adaptive solution for complex smart home networking challenges.
  • The system contributes to improved smart home automation, safety, and comfort through intelligent device interaction.