# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Proposed Algorithms
```
1:procedureCompressEdgeML (\(dataStream\))
2:Step 1: Initialize Neural Network-based Selective Compression Model
3: Define neural network \(NN_{\text{comp}}\) for data compression
4: Train \(NN_{\text{comp}}\) on dataset \(D_{\text{train}}\) for compression patterns
5: Initialize compression ratio \(CR\leftarrow\text{CR}_{\text{init}}(D_{\text{train}})\)
6:Step 2: Deploy Adaptive Compression Techniques
7: Monitor network conditions \(N_{\text{cond}}\)
8: Define adaptive function \(F_{\text{CR}}(N_{\text{cond}},CR)\) for compression ratio
9: Balance between compression quality and network efficiency
10:for each \(dataPoint\in dataStream\)do
11:Step 3: Data Compression Using Neural Network
12:\(compressedData\gets NN_{\text{comp}}(dataPoint,CR)\)
13: Measure size \(|compressedData|\)
14:Step 4: Adapt Compression Level
15: Update \(N_{\text{cond}}\) based on network bandwidth and storage
16: Adjust \(CR\) using \(F_{\text{CR}}(N_{\text{cond}},CR)\)
17: Ensure \(|compressedData|\) fits network and storage constraints
18:Step 5: Store Compressed Data
19: Store \(compressedData\) in targeted storage system
20:endfor
21:endprocedure
```  
**Algorithm 3** CompressEdgeML: Adaptive Data Compression  
CacheEdgeML, outlined in Algorithm 4, represents an advanced approach to data caching in IoT networks. This algorithm efficiently handles data requests by utilizing a predictive analytics model that bases its forecasts on historical access patterns. By training the model, denoted as \(P\), to predict the likelihood of future data requests, CacheEdgeML can effectively identify and prioritize high-importance data. It employs a multi-tier caching strategy, where data are organized into various tiers (\(T_{1},T_{2},\dots,T_{n}\)) according to priority and frequency of access. This setup not only simplifies the management of data but also ensures the efficient utilization of storage resources. Moreover, CacheEdgeML works together with cloud services to manage data storage effectively. It establishes a synchronization mechanism with cloud storage and devises strategies for offloading data when the cache reaches capacity, maintaining consistency between edge and cloud storage. The algorithm dynamically manages cache tiers, continuously re-evaluating data priority and relocating frequently accessed data to higher tiers. This adaptability ensures that the most relevant data are readily available, thus enhancing the system's overall efficiency and responsiveness. TinyHybridSenseQ, as detailed in Algorithm 5, is a sophisticated TinyML-based algorithm specifically designed for IoT environments. It employs advanced machine learning models deployed on edge devices, focusing on the critical tasks of analyzing, categorizing, and efficiently storing data collected from a wide array of sensors. The core competency of this algorithm is in its robust data quality assessment module, which thoroughly evaluates the integrity and accuracy of sensor measurements. This evaluation is crucial for filtering out incorrect data, ensuring that only reliable and high-quality information is processed further. TinyHybridSenseQ also performs well in data management, implementing a dynamic and data-aware hybrid storage strategy. It efficiently determines the optimal storage location for each data packet--be it local storage for less critical data or immediate transfer to a central database for high-priority information. This strategic approach not only simplifies data handling but also significantly enhances the overall efficiency of data transfer processes in IoT networks. Furthermore, TinyHybridSenseQ continuously evolves through its model adaptation feature, which refines its analytical capabilities based on incoming sensor data, thereby maintaining high accuracy and relevance in ever-changing IoT environments.