Enhancing Activity Monitoring in Smart Homes with IoT enabled Sensor Networks using Machine Learning

Authors

  • Sristi Laxmi Lalitha, K. Srujana, B. Harshith, V. Soumith Reddy, B. Akhil

Keywords:

Keywords: Activity Monitoring, Sensor Fusion, Human Activity Recognition, Edge Computing in IoT.

Abstract

The rapid advancement of the Internet of Things (IoT) has enabled the incorporation of intelligentsensor networks in smart homes, enhancing activity monitoring, security, and automation capabilities.This design emphasizes the creation of an advanced IoT-enabled detector network aimed at real-timeexertion monitoring within smart homes, thereby improving security, energy efficiency, and supportedliving operations.

References

Ku, A. L., Qiu, Y., Lou, J., Nock, D., and Xing, B. (2022). Changes in hourly electricity consumption under COVID mandates: a glance to future hourly residential power consumption pattern with remote work in Arizona. Appl. Energy 310:118539. doi: 10.1016/j.apenergy.2022.118539

Chinthavali, S., Tansakul, V., Lee, S., Whitehead, M., Tabassum, A., Bhandari, M., et al. (2022). COVID-19 pandemic ramifications on residential smart homes energy use load profiles. Energy Build. 259:111847. doi: 10.1016/j.enbuild.2022.111847

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Published

2025-04-09

How to Cite

Sristi Laxmi Lalitha, K. Srujana, B. Harshith, V. Soumith Reddy, B. Akhil. (2025). Enhancing Activity Monitoring in Smart Homes with IoT enabled Sensor Networks using Machine Learning . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 134–142. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2282

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