Work

A taxonomy proposal of information assurance and data quality solutions in smart cities

Year: 2025

Type: article

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.

Source: Franklin Open

DOI: 10.1016/j.fraope.2025.100436

Authors: Danladi Suleman, Rania Shibl, Keyvan Ansari, Polycarp Shizawaliyi Yakoi

Institutions: University of the Sunshine Coast, Murdoch University

Language: English

Topic: Smart Cities and Technologies

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,900