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Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure
Hamdy M Youssef1, Radwa Ahmed Osman2, Alaa A El-Bary2,3,4
1Mechanical Engineering Department, College of Engineering and Architecture, Umm Al Qura University, Makkah 21955, Saudi Arabia.
Sensors (Basel, Switzerland)
|May 11, 2024
Summary
This study introduces an intelligent Internet of Things (IoT) system to reduce smart home device interference. A hybrid approach combining deep learning and optimization enhances data reception reliability.
Area of Science:
- Computer Science
- Electrical Engineering
- Artificial Intelligence
Background:
- Smart home devices offer convenience but face communication challenges due to interference.
- Interference from diverse transmission systems degrades smart home network performance.
- Effective data sharing is crucial for the functionality of interconnected smart home systems.
Purpose of the Study:
- To present a novel framework for reducing interference among smart home devices.
- To enhance communication reliability and data reception in smart home environments.
- To explore the synergistic application of deep learning and mathematical optimization for interference management.
Main Methods:
- Developed an intelligent Internet of Things (IoT) system incorporating adaptive communication protocols and interference management algorithms.
- Created an optimization model for dynamic resource allocation, including frequency channels and transmission power.
- Integrated a deep learning technique for adaptive interference pattern prediction using real-time and historical data.
Main Results:
- The hybrid strategy effectively minimizes interference, leading to improved data reception performance.
- The deep learning component adapts to dynamic interference, while the optimization model manages resources efficiently.
- Experimental results demonstrate the system's robustness and flexibility in various smart home scenarios.
Conclusions:
- The proposed framework offers a comprehensive solution for interference reduction in smart homes.
- Combining deep learning and mathematical optimization significantly enhances the dependability of smart home data reception.
- This research paves the way for more robust and reliable smart home communication networks.
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