# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 1 Introduction
Despite its extensive potential for enabling machine learning in compact formats, TinyML encounters several challenges, including the absence of standardized benchmarks, limited development board support, programming language restrictions, preprocessing oversights, sensor selection issues, and a lack of sufficient labeled data [42; 44]. In regions like Africa, where the adoption of AI and embedded systems is still in its infancy, TinyML could address issues related to connectivity, energy, and costs [45]. Moreover, its ability to facilitate independent operation from cloud computing opens new areas for energy-efficient, secure, and private data processing [46], introducing a new era of novel localized applications in various fields. In the vast ecosystem of Big Data, TinyML stands out as a significant opportunity. Given the immense volume, velocity, and variety of data in modern systems, traditional Big Data management methods often reach their limits. TinyML presents an innovative solution, functioning more than simply as a tool for data processing but also as an effective technique for data management. By enabling devices to conduct initial data processing and improvement at the network's edge, TinyML significantly reduces the amount of data requiring centralized processing. The subsequent selective transmission and storage of critical data enhance both storage and processing efficiency. Moreover, this approach facilitates quick and immediate analytics, thereby enhancing the overall value gained from Big Data. The aim of this study focuses on the field of IoT data management by introducing and comparing a suite of specialized TinyML algorithms. Each algorithm, namely TinyCleanEDF (Algorithm 1), EdgeClusterML (Algorithm 2), CompressEdgeML (Algorithm 3), CacheEdgeML (Algorithm 4), and TinyHybridSenseQ (Algorithm 5), has been designed to address specific challenges inherent in IoT systems. TinyML algorithms are essential in this context as they enable efficient processing and intelligent decisionmaking directly on IoT devices, reducing the reliance on central systems and minimizing data transmission needs. By incorporating advanced techniques such as federated learning, anomaly detection, adaptive data compression, strategic caching, and detailed data quality assessment, these algorithms jointly enhance the overall efficiency, security, and reliability of data management within IoT networks. The comparative analysis provided in this study underscores the distinct functionalities and advantages of each algorithm, highlighting the necessity and versatility of TinyML in handling data in the increasingly complex landscape of IoT systems. The remainder of this study is organized as follows: Section 2 provides comprehensive background on TinyML, highlighting its emergence as a pivotal tool in managing Big Data within IoT environments and discussing its application in large-scale IoT systems and embedded devices. Section 3 outlines our methodology, covering our approach's advantages, framework design, hardware setup, and dataset configuration for TinyML evaluation. Section 3.7 details the proposed algorithms we developed to utilize TinyML in IoT contexts. The experimental results of the proposed algorithms are thoroughly presented in Section 4, demonstrating the practical implications and effectiveness of our approach. Finally, the study concludes in Section 5, summarizing the key findings and discussing future research directions, emphasizing TinyML's impact on IoT data management.