# FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments
## 1 Introduction  
Technological advancements have driven the rapid proliferation of Internet of Things (IoT) devices, resulting in highly connected and automated smart environments [1, 2]. These devices generate large volumes of real-time data that enable automation and digital transformation across various domains such as supply chain monitoring [3], activity recognition [4], industrial automation [5], healthcare [6, 7, 8], smart homes [9], and smart cities [10, 11]. However, the exponential growth in data generation poses major challenges in storage and management. Traditional systems often struggle with the volume, velocity, and variety of data, leading to inefficiencies and increasing infrastructure costs.  
The emergence of Beyond 5G IoT (B5G IoT) [12, 13] has intensified these demands by introducing enhancements in performance, energy efficiency, security, reliability, andultra-low latency [14], thus supporting large-scale, latency-sensitive, and mission-critical applications [15]. Key B5G capabilities include massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) [16; 17; 18]. While mMTC enables connectivity for millions of low-bandwidth devices--ideal for distributed sensing and monitoring--URLLC supports applications requiring extreme reliability and real-time responsiveness, such as industrial automation, remote surgery, and autonomous driving. Complementing these capabilities, edge computing brings processing closer to data sources, reducing latency and offloading core network traffic [19; 20], and NETWORK SLICING allows the physical network to be partitioned into virtual slices tailored to specific application needs [21].  
These technologies converge to enable a next-generation IoT ecosystem that demands adaptive and efficient data management strategies. Among these, data storage efficiency is particularly critical, as redundant data storage leads to resource waste, performance degradation, and higher operational costs [22]. Probabilistic data structures such as Bloom and cuckoo filters offer a promising solution by enabling approximate membership queries with minimal memory usage [23; 24]. By accepting a small probability of false positives, these filters significantly reduce storage requirements and accelerate query operations, making them well-suited for resource-constrained environments.  
We hypothesize that _filter-based approaches optimize IoT data storage by avoiding duplication and reducing query time_. To support this claim, we explore the foundations of probabilistic data structures and demonstrate their applicability in IoT scenarios that demand efficient and scalable data management.  
This paper introduces FODIT (filter-based optimization for data storage in IoT), a lightweight module that applies advanced filtering techniques to prevent redundant data storage and improve query performance while preserving data integrity. We evaluate its effectiveness by analyzing FODIT's architecture and validating its performance through simulations of real-world case studies involving data storage in local databases, cloud-based systems, and distributed ledger technologies (DLTs). The results demonstrate substantial improvements in both storage efficiency and overall system responsiveness.  
The remainder of this paper is structured as follows. Section 2 introduces the IoT architecture within B5G environments and presents the fundamental concepts of filters, with a focus on cuckoo filters. Section 3 reviews the most relevant related work in the field. Section 4 analyses current IoT storage models, highlighting their specific characteristics. Section 5 describes the proposed FODIT module, detailing its architecture and workflow, and presenting its integration into the Phonendo framework [25]. Section 6 presents the evaluation case studies, experiments, results, and limitations. Finally, Section 7 summarizes the main contributions and outlines directions for future research.