# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### Data Management Techniques Utilizing TinyML in IoT Systems  
In the field of Tiny Machine Learning (TinyML), data management techniques are essential for handling machine learning models on devices with limited resources, such as those in IoT networks. One method involves augmenting thing descriptions (TD) with semantic modeling to provide comprehensive information about applications on devices, facilitating the efficient management of both TinyML models and IoT devices on a large scale [81]. Additionally, employing TinyML for training devices can lead to the creation of a decentralized and adaptive software ecosystem, enhancing both performance and efficiency. This approach has been effectively implemented in the development of a smart edge computing robot (SECR) [82]. Such methodologies are increasingly important in sectors like supply chain management, where they play a crucial role in predicting product quality parameters and extending the shelf life of perishable goods, including fresh fruits in modified atmospheres [83]. Moreover, the growing complexity in communication systems, spurred by diverse emerging technologies, underscores the need for AI and ML techniques in the analysis, design, and operation of advanced communication networks [84]. In the context of IoT systems, data management techniques incorporating TinyML focus on effective data handling, ensuring privacy and security, and leveraging machine learning for insightful data analysis. One strategy employs distributed key management for securing IoT wireless sensor networks, utilizing the principles of elliptic curve cryptography [85]. Another method involves applying data-driven machine learning frameworks to enhance the accuracy of vessel trajectory records in maritime IoT systems [86]. Power \begin{table}
\begin{tabular}{l l l} \hline \hline
**Application Area** & **TinyML Algorithm** & **Reference** \\ \hline Predictive Maintenance & RUL prediction using LSTMs and CNNs & [69] \\ Data Compression & Tiny Anomaly Compressor (TAC) & [70] \\ Tool Usage Monitoring & TinyML for handheld power tool monitoring & [67] \\ On-Device Training & Neural network training for dense networks & [71] \\ IoT Compression & Evolving TinyML compression algorithm & [72] \\ Safety-Critical Applications & Software-implemented hardware fault tolerance & [73] \\ Model Optimization & Unified DNAS for compressible models & [74] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification of TinyML algorithms by application areas. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Study Focus** & **Key Contributions** & **Reference** \\ \hline Deep Neural Network & Reduced Precision Optimization for DNN on-device learning in & [75] \\ Optimization & MCUs. & \\ Unsupervised Online & Adaptive TinyML algorithm for driver behavior analysis in & [76] \\ Learning & automotive IoT. & \\ Anomaly Detection & TinyML algorithm for anomaly detection in Industry 4.0 using & [77] \\ Low Precision & Empirical study on quantization techniques for TinyML & [78] \\ Quantization & efficiency. & \\ Sparse tinyML Accelerator & Development of RAMAN, a re-configurable and sparse tinyML & [79] \\  & accelerator for edge inference. & \\ Federated Meta-Learning & TinyEptle: federated meta-learning algorithm for TinyML on & [80] \\  & MCUs. & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification and focus of recent TinyML algorithm studies. management also plays a crucial role in IoT systems, particularly those reliant on compact devices and smart networks. This often includes the adoption of low-power communication protocols and the integration of autonomous power systems, which are frequently powered by renewable energy sources [87]. Furthermore, AI-based analytics, processed in the cloud, are increasingly being utilized for healthcare-related data management, such as systems designed for managing diabetic patient data [88]. Table 8 illustrates how TinyML is revolutionizing data management techniques in IoT systems, bringing efficiency and accuracy to various processes.