Show simple item record

AuthorSayed, Aya Nabil
AuthorHimeur, Yassine
AuthorBensaali, Faycal
Available date2022-12-29T07:34:46Z
Publication Date2022
Publication NameEngineering Applications of Artificial Intelligence
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.engappai.2022.105254
URIhttp://hdl.handle.net/10576/37855
AbstractThe building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage are gaining growing interest in BIoT applications. Typically, analyzing big occupancy data gathered by BIoT networks helps significantly identify the causes of wasted energy and recommend corrective actions. Within this context, building occupancy data aids in the improvement of the efficacy of energy management systems, allowing the reduction of energy consumption while maintaining occupant comfort. Occupancy data might be collected using a variety of devices. Among those devices are optical/thermal cameras, smart meters, environmental sensors such as carbon dioxide (CO2), and passive infrared (PIR). Even though the latter methods are less precise, they have generated considerable attention owing to their inexpensive cost and low invasive nature. This article provides an in-depth survey of the strategies used to analyze sensor data and determine occupancy. The article's primary emphasis is on reviewing deep learning (DL), and transfer learning (TL) approaches for occupancy detection. This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. The main concerns affecting the current occupancy detection system in terms of privacy and precision were thoroughly discussed. For occupancy detection, several directions were provided to avoid or reduce privacy problems by employing forthcoming technologies such as edge devices, Federated learning, and Blockchain-based IoT. 2022 The Authors
SponsorThis paper was made possible by the Graduate Assistant-ship (GA) program provided from Qatar University (QU). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.
Languageen
PublisherElsevier
SubjectBig data
Deep learning
Edge devices
Energy efficiency
Internet of energy
Non-intrusive
Occupancy detection
Transfer learning
TitleDeep and transfer learning for building occupancy detection: A review and comparative analysis
TypeShort Survey
Volume Number115
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record