Machine learning and deep learning for predicting indoor and outdoor IoT temperature monitoring systems

Conference paper

Lestari, Nur Indah, Bekhit, Mahmoud, Mohamed, Mohamed, Fathalla, Ahmed and Salah, Ahmad. (2021). Machine learning and deep learning for predicting indoor and outdoor IoT temperature monitoring systems. IoT as a service 7th EAI international conference, IoTaas 2021. Sydney Australia 13 - 14 Dec 2021 Switzerland: Springer Nature. pp. 185 - 197 https://doi.org/10.1007/978-3-030-95987-6_13
AuthorsLestari, Nur Indah, Bekhit, Mahmoud, Mohamed, Mohamed, Fathalla, Ahmed and Salah, Ahmad
TypeConference paper
Abstract

Nowadays, IoT monitoring systems are ubiquitous. These systems utilized sensors to measure the temperature indoors or outdoor. These sensors can be temporarily unavailable for several reasons, such as power outages. Thus, the server that collects the temperatures should find an alternative for predicting the temperature during the downtime of temperature sensors. In this context, there are several machine learning models for predicting temperature. This work is motivated to study the performance gap of predicting outdoor and indoor temperatures. In the proposed study, we utilized a deep learning recurrent neural network called Gated Recurrent Units (GRUs) and four machine learning models, namely, random forest (RF), decision trees (DT), support vector machines (SVM), and linear regression (LR) for predicting the temperature during the downtimes of the temperature sensors. Then, we evaluated the proposed models on a realistic dataset. The results show that predicting the indoor temperature is more predictable than the outdoor temperature. Moreover, the results revealed that the SVM model was the most accurate model for this task.

Keywordsdeep learning; indoor; IoT; machine learning; temperature prediction; outdoor
Year01 Jan 2021
PublisherSpringer Nature
ISSN1867-8211
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-95987-6_13
Web address (URL)https://link.springer.com/book/10.1007/978-3-030-95987-6
Open accessPublished as non-open access
Research or scholarlyResearch
Publisher's version
License
All rights reserved
File Access Level
Controlled
Book titleIoT as a Service
Page range185 - 197
ISBN978-3-030-95986-9
Web address (URL) of conference proceedingshttps://link.springer.com/book/10.1007/978-3-030-95987-6
Output statusPublished
Publication dates
Online08 Jul 2022
Publication process dates
Deposited27 Jun 2024
Additional information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Place of publicationSwitzerland
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