Data fusion (DF) addresses the challenge of integrating heterogeneous data sources to improve decision-making and inference. Although DF has been widely explored, no prior systematic review has specifically focused on its application to low-cost sensor (LCS) data in environmental monitoring. To address this gap, we conduct a systematic literature review (SLR) following the PRISMA framework, synthesising findings from 82 peer-reviewed articles. The review addresses three key questions: (1) What fusion methodologies are employed in conjunction with LCS data? (2) In what environmental contexts are these methods applied? (3) What are the methodological challenges and research gaps? Our analysis reveals that geostatistical and machine learning approaches dominate current practice, with air quality monitoring emerging as the primary application domain. Additionally, artificial intelligence (AI)-based methods are increasingly used to integrate spatial, temporal, and multimodal data. However, limitations persist in uncertainty quantification, validation standards, and the generalisability of fusion frameworks. This review provides a comprehensive synthesis of current techniques and outlines key directions for future research, including the development of robust, uncertainty-aware fusion methods and broader application to less-studied environmental variables.
@article{oduori2026datafusion,title={Data Fusion for Low-Cost Sensors: A Systematic Literature Review},author={Oduori, Gabriel and Cocco, Chiara and Sajadi, Payam and Pilla, Francesco},journal={Information Fusion},pages={104124},year={2026},issn={1566-2535},doi={10.1016/j.inffus.2026.104124},url={https://www.sciencedirect.com/science/article/pii/S1566253526000035},keywords={Data Fusion, Low-Cost Sensors, Environmental Monitoring, Systematic Literature Review, Satellite Imagery, Geostatistics, Machine Learning, Spatio-temporal Data, Uncertainty Quantification},}
## Preprints
2026
Hybrid Generalized Additive-State Space Modelling for Urban NO2 Prediction: Integrating Spatial and Temporal Dynamics
Gabriel Oduori, Chiara Cocco, Payam Sajadi, and 1 more author
2026
Manuscript submitted to Environmental Modelling and Software; preprint available at SSRN