# FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments
## 2 Background
### Filters
[34] present the fuzzy-folded Bloom filter (FFBF) for big data storage in the cloud, which extends the standard BF to incorporate a fuzzy-enabled folding approach, effectively reducing storage requirements without affecting the false positive rate and query time. These studies collectively highlight the potential of filter-based modules in enhancing data storage and retrieval in IoT environments. The optimization of data storage in IoT environments has been a significant area of research, given the explosive growth of data generated by IoT devices. Various approaches have been explored to address the challenges associated with efficient data storage, including data compression [35], deduplication [36], and the use of probabilistic data structures [24; 28]. Data compression techniques aim to reduce the size of data before storage, thus saving space and potentially improving transmission efficiency [37]. Techniques such as lossless and lossy compression have been widely studied and applied in IoT environments. Lossless compression algorithms like SZ lossy [38] are used to ensure that the original data can be perfectly reconstructed, which is crucial for applications requiring high data integrity. However, these methods can be computationally intensive and may not always provide sufficient compression ratios for the large volumes of data generated by IoT devices. Data deduplication is another approach to optimize storage by eliminating duplicate copies of repeating data. Techniques like chunk-based deduplication and file-level deduplication have been implemented in various storage systems. For instance, the work by [PERSON] et al. [39] introduced the aggregation of sensor data, followed by preprocessing steps to filter out irrelevant or noisy data, which significantly reduced storage requirements by identifying and eliminating redundant data. While effective, deduplication can introduce additional complexity in data retrieval and management, especially in dynamic IoT environments where data is continuously updated [36]. Probabilistic data structures, such as Bloom filters [23] and cuckoo filters [24], have been increasingly recognized for their potential in managing large-scale data efficiently. Bloom filters, introduced by [PERSON] [23], allow for fast membership queries with a controlled false positive rate, making them suitable for applications where space efficiency is critical. Cuckoo filters, proposed by [PERSON] et al. [24], build on Bloom filters by offering lower false positive rates and supporting deletions, thus providing more flexibility for dynamic datasets. In the context of B5G IoT, several studies have explored the use of these probabilistic structures to enhance data storage and retrieval. For example, [PERSON] et al. [40] designed a proposal to remove redundant data on the final layer and improve access times for stored data. [PERSON] proposed a Bloom filter-based solution at different layers of IoMT (edge-fog-cloud). Despite advancements in probabilistic data structures, their application to optimizing data storage in B5G IoT environments remains relatively underexplored. This gap serves as one of the main motivations for this work. ## 4 Analysis of Storage in IoT Environments

In B5G-enabled IoT ecosystems, storage becomes a critical bottleneck due to the need for real-time responsiveness and scalable data retention. This section examines three primary storage scenarios in such contexts: local databases, cloud-based services, and DLT (see Figure 2). Each scenario presents unique benefits and challenges in terms of storage efficiency, data accessibility, and resource management. * _Local databases_ provide on-premises storage solutions, allowing IoT devices to store and manage data without relying on external networks [41]. This approach ensures low-latency access and enhanced security, as data remains within a controlled environment. However, scalability is a major limitation, as the storage capacity is constrained by the physical hardware available.