Deep learning approaches for human-centered IoT applications in smart indoor environments:a contemporary survey


The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.

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Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © Springer Nature B.V. 2021. The final publication is available at Springer via
Uncontrolled Keywords: Activity recognition,Deep learning,Indoor positioning and tracking,Internet of things,Smart indoor environments,Decision Sciences(all),Management Science and Operations Research
Publication ISSN: 1572-9338
Last Modified: 01 Apr 2024 07:45
Date Deposited: 13 Jun 2022 14:39
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 479-021-04164-3 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-07-08
Published Online Date: 2021-07-08
Accepted Date: 2021-06-11
Authors: Abdel-Basset, Mohamed
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Hawash, Hossam
Chakrabortty, Ripon K.
Ryan, Michael



Version: Accepted Version

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