# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### TinyML Algorithms  
Table 6 provides a structured overview of various TinyML algorithms and their specific applications in different domains. It categorizes these algorithms into areas such as predictive maintenance, data compression, tool usage monitoring, and more, illustrating the range of TinyML's applicability. Each entry in the table is linked to a corresponding reference, offering a direct connection to the source material. This format effectively showcases the diversity of TinyML's real-world applications, highlighting its potential to transform various sectors through intelligent on-device data processing and analysis.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Use Case** & **Description** \\ \hline Predictive Maintenance & Real-time analysis of sensor data for early fault detection in machinery, reducing downtime and maintenance costs. \\ Health Monitoring & Continuous health monitoring with wearables for vital signs and anomaly detection, enhancing preventative healthcare. \\ Smart Agriculture & Adaptive agriculture practices based on sensor data, optimizing resource use for better crop yield. \\ Voice Recognition & Local processing of voice commands for quicker privacy-focused responses. \\ Face Recognition & Low-latency facial recognition for secure access control and personalization. \\ Anomaly Detection & Immediate detection of irregular patterns in industrial and environmental data for proactive response. \\ Gesture Control & Touch-free device control via gesture recognition, improving user interaction and accessibility. \\ Energy Management & Intelligent energy use in smart grids and homes based on usage patterns and predictive analytics. \\ Traffic Flow Optimization & Real-time traffic analysis for dynamic routing and light sequencing, enhancing urban traffic management. \\ Environmental Monitoring & Continuous monitoring of environmental conditions, with real-time adjustments and alerts. \\ Smart Retail & Analysis of customer behavior for tailored retail experiences and store management. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applications of TinyML and machine learning on embedded devices.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Application** & **Description** & **Ref.** \\ \hline Concrete Materials Damage & Lightweight CNN on MCU for damage recognition in concrete materials, showing TinyML’s & [63] \\ Classification & potential in structural health. \\ Predictive Maintenance & TinyML for predictive maintenance in hydraulic systems, improving service quality, performance, and sustainability. & [64] \\ Keyword Spotting & TinyML for efficient keyword detection in voice-enabled devices, reducing processing costs and enhancing privacy. & [65] \\ Time-Series Analysis & ML hardware accelerators for real-time analysis of time-series data in IoT, optimizing neural networks for on-device processing. & [66] \\ Asset Activity Monitoring & TinyML for continuous monitoring of tool usage, identifying usage patterns and potential misuses. & [67] \\ Environmental Monitoring & TinyML for monitoring environmental factors like air quality, contributing to smart systems for sustainability. & [68] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Various applications of TinyML in embedded devices.  
Table 7 presents an organized summary of cutting-edge research in the field of Tiny Machine Learning (TinyML). This table methodically categorizes various studies into distinct focus areas, covering a broad spectrum from optimizing deep neural networks on microcontrollers to applying federated meta-learning techniques in environments with limited resources. This structured presentation not only underscores the multifaceted nature of TinyML research but also highlights its significant role in advancing the functionalities of embedded devices for a wide array of applications.