Home Automation System Base on IoT and ML

Abstract

With the proliferation of Internet of Things (IoT) devices and advancements in machine learning (ML) techniques, there is growing interest in developing intelligent home automation systems. These systems aim to enhance convenience, comfort, and energy efficiency in modern households. In this paper, we present a comprehensive study on the design, implementation, and evaluation of a home automation system leveraging IoT and ML technologies. Our proposed system integrates various IoT devices such as sensors, actuators, and smart appliances to create a networked environment within the home. These devices collect and transmit real-time data about environmental conditions, user preferences, and energy consumption patterns. We employ machine learning algorithms to analyse this data and make informed decisions to automate various aspects of home management and control.Key components of our system include data preprocessing, feature extraction, model training, and decision-making modules. We explore different ML algorithms such as regression, classification, and clustering to address specific tasks such as temperature regulation, lighting control, security monitoring, and energy optimization. Furthermore, we investigate techniques for model deployment, monitoring, and adaptation to ensure the robustness and reliability of the system in dynamic home environments. To evaluate the effectiveness of our approach, we conduct experiments using a prototype implementation deployed in real-world households. We measure performance metrics such as accuracy, responsiveness, energy savings, and user satisfaction to assess the practical viability of the proposed system. Our results demonstrate significant improvements in home automation capabilities compared to traditional rule-based approaches, highlighting the potential of IoT and ML integration in shaping the future of smart homes.