IoT Quality Control for Data and Application Needs
Open accessAbstract
The amount of Internet of Things (IoT) data is growing rapidly. Although there is a growing understanding of the quality of such data at the device and network level, important challenges in interpreting and evaluating the quality at informational and application levels remain to be explored. This article discusses some of these challenges and solutions of IoT systems at the different OSI layers to understand the factors affecting the quality of the overall system. With the help of two IoT-enabled digital health applications, the authors investigate the role of semantics in measuring the data quality of the system, as well as integrating multimodal data for clinical decision support. They also discuss the extension of IoT to the Internet of Everything by including human-in-the-loop to enhance the system accuracy. This paradigm shift through the confluence of sensors and data analytics can lead to accelerated innovation in applications by overcoming the limitations of the current systems, leading to unprecedented opportunities in healthcare.
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The authors in [45] identified that quality of data is crucial not only at device or network level but also at application level.
“The authors in [45] identified that quality of data is crucial not only at device or network level but also at application level.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [45]
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- Authors
- Banerjee, T., & Sheth, A.
- Affiliations
- Wright State University (US)
- Venue
- IEEE Intelligent Systems
- Published
- 2017-03
- Volume
- 32
- Issue
- 2
- Pages
- 68-73
- ISSN
- 1541-1672 (ISSN-L)
- OpenAlex
- W2602129809
- Cited by
- 49
- References
- 12
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 5 · 2022: 12 · 2021: 8 · 2020: 6 · 2019: 8 · 2018: 2 · 2017: 4
- FWCI
- 10.18
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Intelligent Systems (publishedVersion)
- Journal of Bioresource Management (submittedVersion, cc-by)
- Scholar Commons (University of South Carolina) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- Internet of Things, information quality, OSI layers, semantics, multimodal data integration, clinical decision support, digital health applications, Internet of Everything, human-in-the-loop, sensor data analysis
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