CIoT-Net: a scalable cognitive IoT based smart city network architecture
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Abstract In the recent era, artificial intelligence (AI) is being used to support numerous solutions for human beings, such as healthcare, autonomous transportation, and so on. Cognitive computing is represented as a next-generation application AI-based solutions which provide human–machine interaction with personalized interactions and services that imitate human behavior. On the other hand, a large volume of data is generated from smart city applications such as healthcare, smart transportation, retail industry, and firefighting. There is always a concern on how to efficiently manage the large volume of generated data. Recently many existing researches discussed the analysis of the large quantity of data using cognitive computing; however, these researches are failed to handle the certain problems, namely scalability, and flexibility of data gathered in a smart city environment. Data captured from millions of sensors can be cross implemented across various cognitive computing applications to ensure real-time responses. In this paper, we study the cognitive internet of things (CIoT) and propose a CIoT-based smart city network (CIoT-Net) architecture which describes how data gathered from smart city applications can be analyzed using cognitive computing and handle the scalability and flexibility problems. We discuss various technologies such as AI and big data analysis to implement the proposed architecture. Finally, we describe the possible research challenges and opportunities while implementing the proposed architecture.
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For AI-powered IoT architecture, research tended to focus mainly on the theoretical aspects of ML and data analysis, cognitive AI, knowledge-based DSS, and knowledge-based AI.
“For AI-powered IoT architecture, research tended to focus mainly on the theoretical aspects of ML and data analysis (e.g., Ref. [[213], [214], [215]]), cognitive AI (e.g., Ref. [216,217]), knowledge-based DSS [218], and knowledge-based AI [174].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [217]
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- Authors
- Park, J.-H., Salim, M. M., Jo, J. H., Sicato, J. C. S., Rathore, S., & Park, J. H.
- Affiliations
- Seoul National University of Science and Technology (KR)
- Corresponding author
- Park, Jong Hyuk
- ORCID
- Venue
- Human-centric Computing and Information Sciences
- Published
- 2019-08-01
- Volume
- 9
- Issue
- 1
- ISSN
- 2192-1962 (ISSN-L)
- OpenAlex
- W2964474528
- Cited by
- 121
- References
- 54
- Citations by year
- 2025: 13 · 2024: 8 · 2023: 22 · 2022: 25 · 2021: 27 · 2020: 24 · 2019: 2
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Human-centric Computing and Information Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Seoul National University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- cognitive Internet of Things, cognitive computing, scalability, big data analytics, sensor data management, real-time data processing, human-machine interaction, smart city applications
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