# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### Data Management Techniques Utilizing TinyML in IoT Systems
Techniques like predictive imputation and adaptive data quantization exemplify this transformation. Predictive imputation, using TinyML, maintains data integrity by filling in missing values based on historical and neighboring data, thereby ensuring dataset completeness. Adaptive data quantization, on the other hand, optimizes data storage and transmission. TinyML's role here is to analyze current data trends and dynamically adjust quantization levels for optimal data representation. Sensor data fusion, another critical technique, is enhanced by TinyML's ability to process and merge data from various sensors in real time, thus providing a more comprehensive view and enhancing the accuracy of insights. Anomaly detection is particularly vital in IoT systems, and TinyML enhances this by continuously monitoring data streams to quickly identify and act upon unusual patterns or malfunctions. Intelligent data caching, enabled by TinyML, predicts future data needs, ensuring that frequently used or critical data are cached for instant access. Further, TinyML facilitates Edge-based clustering, grouping similar data at the network's edge to simplify analytics and processing. This on-device clustering leads to more efficient data aggregation and transmission. Real-time data augmentation and local data lifespan management are also key areas where TinyML makes a significant impact. TinyML augments sensor data in real time to enhance machine learning performance while also predicting the utility of data for effective local storage management. Contextual data filtering and on-device data labeling are other areas where TinyML shows its prowess. By using environment-aware models, TinyML filters data relevant to the current context, thereby enhancing decisionmaking processes. Additionally, it can automatically label data based on learned patterns, facilitating efficient data categorization and retrieval. These advanced data management techniques, powered by TinyML, are pivotal in harnessing the full potential of IoT systems, ensuring that they are not only more efficient and accurate but also more responsive to real-time demands. \begin{table}
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**Technique** & **Objective** & **Role of TinyML** \\ \hline Predictive Imputation & Maintain data integrity by compensating for missing or lost data. & Uses historical and neighboring data to predict and fill missing values, ensuring dataset completeness. \\ Adaptive Data Quantization & Optimize data storage and transmission. & Analyzes current trends and adjusts quantization levels dynamically for optimal representation. \\ Sensor Data Fusion & Integrate data from multiple sensors for a holistic view. & Processes and merges diverse sensor data in real time, enhancing accuracy and context. \\ Anomaly Detection & Identify unusual data patterns or device malfunctions. & Continuously monitors data streams, recognizing and flagging anomalies for prompt action. \\ Intelligent Data Caching & Provide instant access to frequently used or critical data. & Uses predictive analytics to anticipate future data needs, caching relevant data. \\ Edge-Based Clustering & Group similar data at the edge for efficient analytics. & Performs lightweight clustering on-device for efficient aggregation and transmission. \\ Real-time Data Augmentation & Enhance data to improve machine learning performance. & Augments sensor data in real time, enriching them for better analytics. \\ Local Data Lifespan & Manage the relevance and storage duration of data. & Predicts data utility, retaining or discarding them for effective local storage management. \\ Contextual Data Filtering & Discard or prioritize data based on the current context. & Filters data relevant to the situation, enhancing decisionmaking processes. \\ On-device Data Labeling & Annotate raw data for subsequent processing. & Automatically labels data based on learned patterns, aiding in categorization and retrieval. \\ \hline \hline \end{tabular}
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