# FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments
## 2 Background
### Filters
Specifically, the false positive rate can be approximated by \(\epsilon\approx\frac{2b}{2^{\prime}}\), indicating that even modest increases in fingerprint length yield exponential improvements in accuracy. As shown in Table 5, a fingerprint length of \(f=10\) bits is sufficient to ensure a false positive rate of 1% when \(b=4\). However, for applications requiring much lower false positive rates, such as \(10^{-6}\) or lower, fingerprint lengths must increase accordingly, as shown in Figure 11. This practically eliminates the occurrence of false positives in the system, far exceeding the needs of most real-world applications. While there is no theoretical consensus on the optimal number of entries per bucket, empirical studies have shown that \(b=4\) offers a good trade-off between space efficiency and accuracy [49]. Therefore, the chosen parameters \(b=4\) and \(f=256\) provide a robust configuration for high-integrity applications, offering both excellent performance and minimal risk of false positives. ### Discussion

Based on Experiments 1 and 2, we can conclusively demonstrate that filter-based optimization significantly enhances data management in B5G IoT environments. The experimental results reveal a dual advantage, as follows: (1) storage efficiency improves through automatic duplicate detection and prevention, and (2) query response times accelerate compared to classical approaches. This performance comes from the FODIT module's pre-processing capability, which eliminates redundant storage operations and bypasses unnecessary database scans during queries. Particularly in bandwidth-constrained B5G scenarios where edge devices generate massive data streams, this optimization reduces both storage overhead and network transmission costs. The filter mechanism proves especially valuable for time-sensitive IoT applications like healthcare, where real-time data access is critical. However, these benefits come with a minor computational overhead for filter creation and maintenance, suggesting an optimal use-case balance between write and read operations. ### Study Limitations

The following limitations of the proposed approach have not been addressed, as they fall outside the scope of this paper. However, they will be considered in future work. The first limitation concerns security. The use of FODIT may, to some extent, centralize access to data that is persistently stored in local databases, cloud services, or a blockchain. This could introduce a vulnerability, as in the event of an attack, some data patterns could potentially be inferred. While this may not be critical in all scenarios--for instance, in public blockchains, data is accessible to any user--it could become a privacy concern in

Figure 11: Figure presents comparative graphs of fingerprint length in bits vs. false positive rate on a logarithmic scale. cases where data confidentiality is required. One possible mitigation would be to encrypt the filter, thereby protecting both read and write operations. However, this is not a trivial solution, as encryption could significantly impact the system's performance, particularly in terms of processing speed. Another important limitation is the lack of dynamic context awareness. FODIT currently does not adapt the filter size or the time window based on the scenario where it is implemented. Introducing context-driven optimization mechanisms would allow the FODIT to adapt to the needs of different smart environments, improving efficiency and responsiveness. Finally, although we have proposed a solution to prevent inconsistencies between the filter and the data stored on the blockchain, it would be valuable to explore alternative approaches. This would allow for a comparative analysis and help identify the most suitable data structure for maintaining consistency and reliability. ## 7 Conclusions

In this work, we have presented FODIT, a module designed to optimize data storage and querying in B5G IoT environments using probabilistic filtering techniques.