@inproceedings{f2e16537b6cd4dda92aae484f61eacc6,
title = "Intelligent Monitoring of IoT Devices using Neural Networks",
abstract = "The Internet of Things (IoT) has seen expeditious growth in recent times with 7 billion connected devices in 2020, thus leading to the vital importance of real-time monitoring of IoT devices. Through this paper, we demonstrate the idea of building a cloud-native application to monitor smart home devices. The application intends to provide valuable performance metrics from the perspective of end-users and react to anomalies in real-time. In this demo paper, we conduct the demonstration using Autoencoder (an unsupervised technique) based Deep Neural Networks (DNNs) to learn the normal operating conditions of power consumption of smart devices. When an anomaly is detected, the DNNs take proactive action and send appropriate commands back to the device. In addition, the users are provided with a real-time graphical representation of power consumption. This will help to save electricity on a domestic as well as industrial level. Finally, we discuss the future prospects of monitoring IoT devices.",
keywords = "Deep Learning, IoT devices, Microservices, cloud-native application",
author = "Ashima Chawla and Pradeep Babu and Trushnesh Gawande and Erik Aumayr and Paul Jacob and Sheila Fallon",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 24th Conference on Innovation in Clouds, Internet and Networks and Workshops, ICIN 2021 ; Conference date: 01-03-2021 Through 04-03-2021",
year = "2021",
month = mar,
day = "1",
doi = "10.1109/ICIN51074.2021.9385543",
language = "English",
series = "2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops, ICIN 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "137--139",
booktitle = "2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops, ICIN 2021",
address = "United States",
}