FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments

Bruno Ramos-Cruz, Francisco J. Quesada-Real (), Javier Andreu-Pérez and Jessica Zaqueros-Martinez
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Bruno Ramos-Cruz: Computer Science Department, University of Jaen, 23071 Jaén, Spain
Francisco J. Quesada-Real: Computer Science Department, University of Jaen, 23071 Jaén, Spain
Javier Andreu-Pérez: Centre for Computational Intelligencer, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Jessica Zaqueros-Martinez: Computer Science Department, University of Jaen, 23071 Jaén, Spain

Future Internet, 2025, vol. 17, issue 7, 1-22

Abstract: In the rapidly evolving landscape of the Internet of Things (IoT), managing the vast volumes of data generated by connected devices presents significant challenges, particularly in B5G IoT environments. One key issue is data redundancy, where identical data is stored several times because it is captured by multiple sensors. To address this, we introduce “ FODIT ”, a filter-based module designed to optimize data storage in IoT systems. FODIT leverages probabilistic data structures, specifically filters, to improve storage efficiency and query performance. We hypothesize that applying these structures can significantly reduce redundancy and accelerate data access in resource-constrained IoT deployments. We validate our hypothesis through targeted simulations under a specific and rare configuration: high-frequency and high-redundancy environments, with controlled duplication rates between 4% and 8%. These experiments involve data storage in local databases, cloud-based systems, and distributed ledger technologies (DLTs). The results demonstrate FODIT’s ability to reduce storage requirements and improve query responsiveness under these stress-test conditions. Furthermore, the proposed approach has broader applicability, particularly in DLT-based environments such as blockchain, where efficient querying remains a critical challenge. Nonetheless, some limitations remain, especially regarding the current data structure used to maintain consistency with the DLT, and the need for further adaptation to real-world contexts with dynamic workloads. This research highlights the potential of filter-based techniques to improve data management in IoT and blockchain systems, contributing to the development of more scalable and responsive infrastructures.

Keywords: B5G IoT; filters; data storage (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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