# Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure

## TL;DR

This paper introduces a smart home IoT system that reduces interference between devices to improve communication and living comfort.

## Contribution

A novel hybrid framework combining deep learning and optimization for interference reduction in smart home IoT systems.

## Key findings

- The hybrid approach effectively minimizes interference and improves data reception performance.
- The system adapts to changing environments through real-time interference pattern learning.
- Experimental results confirm the robustness and flexibility of the proposed framework.

## Abstract

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system’s robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.