# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 1 Introduction  
The rapid evolution of the Internet of Things (IoT) has significantly influenced various sectors, including supply chains, healthcare, and energy systems. This technology's expansive data generation presents challenges in managing and interpreting vast volumes of information. Tiny Machine Learning (TinyML), combining the efficiency of embedded systems with advanced machine learning techniques, stands out as a critical solution for facilitating effective, localized data processing within the IoT framework. The research underscores the growing impact of IoT technologies like TinyML across multiple industries. In supply chain management, IoT's role is instrumental in enhancing inventory management, marking a crucial shift towards Industry 4.0 technologies and their potential to add value to businesses [1]. The healthcare sector benefits from IoT in enhancing services such as remote patient monitoring and real-time data analytics, albeit facing challenges in handling the growth in medical data [2]. TinyML, in particular, is recognized for initiating a new era of IoT and autonomous system applications, attributed to its power efficiency, enhanced privacy, and reduced latency in data processing [3]. The application of machine learning algorithms in embedded systems, including TinyML, is being extensively researched. Studies focus on analyzing performance and developing intelligent systems like solar collectors for thermal energy storage [4; 5]. Moreover, integrating multiple blockchain technologies [6; 7] and decentralized authentication systems [8] has been proposed as a viable strategy for secure and efficient IoT data management, addressing the digital challenges in the Internet of Everything era [9]. From an industrial perspective, effectively managing TinyML at scale, particularly with regard to the inherent hardware and software constraints of IoT devices, remains a focal area of ongoing research. Proposals suggest frameworks that utilize Semantic Web technologies for the integrated management of TinyML models and IoT devices [10]. Additionally, the advancement and implementation of sophisticated intelligent-anomaly-based intrusion detection systems within IoT networks, utilizing advanced machine learning models, have demonstrated significant efficacy in precisely detecting and proactively mitigating malicious cyber activities [11]. This approach is crucial for enhancing network security and ensuring the reliability of IoT systems in various industrial applications. The impact of TinyML extends to many areas, including wearable technology [12; 13; 14; 15; 16], smart cities [17; 18; 19; 20; 21; 22], smart homes [23; 24; 25], smart agriculture [26; 27; 28; 29; 30; 31], climatic change, environment protection, green AI sustainable applications [32; 33; 34; 35; 36; 37; 38], and automobiles [39; 40]. Overcoming TinyML's challenges, especially in hardware, is key and can be advanced through creating an open-source community. This initiative would support system development, enhance learning capabilities on small-scale hardware, and help unify software across different platforms [41]. TinyML is set to not only meet current technological demands but also shape the future of smart, efficient data processing. However, deploying TinyML on microcontrollers presents certain challenges. These include the selection of appropriate programming languages, limited support for various development boards, often overlooked preprocessing steps, the choice of suitable sensors, and a scarcity of labeled data for training purposes [42]. Overcoming these obstacles is essential for the development of TinyML systems that are both efficient and effective. Tiny Machine Learning (TinyML) offers significant progress in machine learning, focusing on low-resource embedded devices [43].