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

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.