Enhancing IoT Device Behavior Prediction through Machine Learning Models
محورهای موضوعی : IT Strategy
Shubham Minhass
1
,
Ritu Chauhan
2
,
Harleen Kaur
3
1 - Amity Institute of Information Technology, Amity university, Noida, Uttar Pradesh, India
2 - Artificial Intelligence and IoT Lab, Center for Computational Biology and Bioinformatics, Amity university, Noida, Uttar Pradesh, India
3 - Department of Computer Science and Engineering, Jamia Hamdard, Delhi, India
کلید واژه:
چکیده مقاله :
There is an urgent need for precise and trustworthy models to forecast device behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Naïve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device behavior forecasting. Our results show that Naïve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device behavior prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.
There is an urgent need for precise and trustworthy models to forecast device behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Naïve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device behavior forecasting. Our results show that Naïve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device behavior prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.
