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CC BY-NC-ND V4.0, Open Access
Journal article
A taxonomy proposal of information assurance and data quality solutions in smart cities
Franklin Open, Vol.13, 100436
2025
Abstract
The concept of smart cities continues to gain traction as urban and rural areas increasingly adopt Internet-of-things (IoT), sensors and smart devices, generating vast amounts of data. However, the collection, processing, and transmission of this big data introduce multi-dimensional challenges, intensifying the need for robust Information Assurance (IA) and Data Quality (DQ) solutions. Researchers have proposed various methodologies to address these challenges, including encryption techniques (e.g., homomorphic and lightweight encryption, cryptographic methods), deep learning models (e.g., LSTM), tree-based machine learning algorithms, government regulations (e.g., GDPR, ePrivacy Directive), blockchain-based integrity frameworks, and cloud-centric security and DQ architectures. This study iteratively classifies these methodologies. While researchers and experts have employed these methodologies and solutions to address IA/DQ challenges, our survey reveals a critical gap. There is a lack of holistic strategies for integrating IA and DQ in smart cities, particularly in big data and IoT use cases. Unlike prior surveys, this paper provides a novel IA/DQ-centric perspective, highlighting unresolved challenges such as governing standards for real-time data and DQ policy. As such, we provide a guide for future research toward developing a cohesive end-to-end assurance framework for smart cities.
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Details
- Title
- A taxonomy proposal of information assurance and data quality solutions in smart cities
- Authors/Creators
- Danladi Suleman - University of the Sunshine CoastRania Shibl - University of the Sunshine CoastKeyvan Ansari - Murdoch University, College of Science, Technology, Engineering and MathematicsPolycarp Shizawaliyi Yakoi - Murdoch University, School of Information Technology, Murdoch, WA 6150, Australia
- Publication Details
- Franklin Open, Vol.13, 100436
- Publisher
- Elsevier Inc.
- Identifiers
- 991005831946707891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- School of Information Technology; College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Journal article
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