Big data and IoT-based applications in smart environments: A systematic review
By Yosra Hajjaji, Wadii Boulila, Imed Riadh El Farah, I. Romdhani, Amir Hussain. Computer Science Review, 2021
304 citations recorded by the source corpus.
Abstract This paper reviews big data and Internet of Things (IoT)-based applications in smart environments. The aim is to identify key areas of application, current trends, data architectures, and ongoing challenges in these fields. To the best of our knowledge, this is a first systematic review of its kind, that reviews academic documents published in peer-reviewed venues from 2011 to 2019, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. In order to examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that the integration of big data and IoT technologies creates exciting opportunities for real-world smart environment applications for monitoring, protection, and improvement of natural resources. The fields that have been investigated in this survey include smart environment monitoring, smart farming/agriculture, smart metering, and smart disaster alerts. We conclude by summarizing the methods most commonly used in big data and IoT, which we posit to serve as a starting point for future multi-disciplinary research in smart cities and environments.
Subfields: Computer Networks and Communications, Media Technology, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/3b529988da89bf94c4ebdf183831d07d4f31978e
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In Computer Networks and Communications
Citations in the corpus
- Blockchain for the Metaverse: A Review (2022, background): “The crossplatform capabilities of IoT devices are critical for sharing data between virtual worlds [92].”
- Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives (2021, background): “DL has been applied in many RS-related applications such as crop yield tracking, land cover change monitoring, disaster prediction, and urban planning, and it demonstrates excellent results as demonstrated in [1-6].”