Integration of Machine Learning and Iot based Multi-Layer Wireless Sensor Networks for Seamless Smart Home Automation

Abstract

The integration of machine learning algorithms with Internet of Things -based multi-layer wireless sensor networks offers a transformative solution to modern smart home automation and security. This paper seeks to explore both the Sensor Data Processing and Machine Learning Components used. In essence, the machine learning model leverages aggregate sensor data to improve the efficiency and safety of modern living environments. Through attention to detail during the preprocessing and feature engineering of sensor data, the system analyzed learned patterns of household activity, including motion intensity, door/window interactions, and normal occupancy. Subsequently, the features were utilized as input variables in a number of models, including Convolutional Neural Networks , Support Vector Machines, Recurrent Neural Networks, and K-Nearest Neighbors. By simulating both vacation and occupant mode, the models processed the available data using to detect potential threats or other deficiencies in the patterns. Overall the results led by the model suggest that the Convolutional Neural Network model is the best amongst all of the aforementioned algorithms. It demonstrated the best accuracy out of all the other models due to the advanced image processing and facial recognition features, solidified by its 98.56% accuracy rate in recognizing a potential intruder. Other models were also effective or reasonably accurate, with the following results: SVMs – 95.45%; RNNs – 93.4%; and KNNs – 91.23%. The evaluation process also involves the calculation of precision, recall, and F1 score, which also indicates the overall strength of the model. The aggregation of these results implies a considerable potential for internet-of-things systems in combination with machine learning methods to cultivate smarter and safer living environments.