# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### TinyML
#### 2.2.3 Applications of TinyML on Embedded Devices  
Table 4 illustrates various applications of TinyML and machine learning in embedded devices across different sectors. In predictive maintenance, TinyML models analyze real-time sensor data from machinery, enabling early detection of potential failures and reducing maintenance costs. This technology is also pivotal in health monitoring, where wearable devices equipped with TinyML offer continuous health tracking, instantly analyzing critical health metrics while ensuring user privacy. In agriculture, TinyML enhances efficiency by adjusting operations based on real-time environmental data, leading to optimal resource usage and increased yield. Voice and \begin{table}
\begin{tabular}{l l} \hline \hline
**Challenge** & **TinyML Solution** \\ \hline Data Overload & On-device preprocessing to reduce data transmission. \\ Real-time Processing & Edge-deployed models for swift decisionmaking. \\ Connectivity Issues & Local processing for uninterrupted operation. \\ Energy Efficiency & Optimized tasks to conserve energy and extend device life. \\ Security & Local data processing to minimize transmission risks. \\ Privacy & In situ data processing to enhance privacy. \\ Device Longevity & Reduced transmission strain to extend device life. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TinyML solutions for Big Data and IoT challenges. \begin{table}
\begin{tabular}{l l l} \hline \hline
**IoT System Characteristic** & **Implication** & **Enhancement with TinyML** \\ \hline Distributed Topology & \begin{tabular}{l} Numerous devices scattered across different locations \\ lead to data decentralization and increased latency. \\ \end{tabular} & \begin{tabular}{l} TinyML facilitates edge computation, reducing latency \\ and ensuring real-time insights. \\ On-device TinyML prioritizes, compresses, and filters \\ data, managing storage and reducing transmission \\ needs. \\ \end{tabular} \\ Voluminous Data Streams & \begin{tabular}{l} Continuous data generation can overwhelm storage and \\ transmission channels. \\ \end{tabular} & \begin{tabular}{l} On-device TinyML prioritizes, compresses, and filters \\ data, managing storage and reducing transmission \\ needs. \\ \end{tabular} \\ Diverse Device Landscape & \begin{tabular}{l} Variety in device types introduces inconsistency in data \\ formats and communication protocols. \\ \end{tabular} & \begin{tabular}{l} TinyML standardizes data processing at source, \\ ensuring unified data representation across devices. \\ TinyML models maximize computational efficiency, \\ conserving device resources. \\ \end{tabular} \\ \begin{tabular}{l} Real-time Processing Needs \\ \end{tabular} & \
\begin{tabular}{l} Delays in data processing can hinder time-sensitive \\ applications. \\ \end{tabular} &
\begin{tabular}{l} TinyML ensures rapid on-device processing for \\ immediate responses to data changes. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of large-scale IoT systems and enhancements with TinyML. face recognition technologies in embedded devices benefit from TinyML through faster localized processing, enhancing reliability and privacy. TinyML also plays a crucial role in energy management within smart grids and home automation, optimizing energy use for cost and environmental benefits. In urban development, it contributes to traffic flow optimization by analyzing real-time vehicle and pedestrian movements, improving urban mobility. These examples showcase TinyML's significant impact in enhancing operational efficiency, user experience, and sustainable practices across various industries. Table 5 presents a detailed overview of TinyML applications across a range of fields. It includes concise descriptions of each application and corresponding academic references. The table illustrates the versatility of TinyML, from implementing CNN models on microcontrollers for material damage analysis to its use in environmental monitoring.