# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 4 Experimental Results
### Metrics and Methods
The improvement in resource utilization and the adaptability score, with a roughly 15% increase as nodes scaled, signifies more efficient resource utilization and data transfer, particularly valuable in resource-constrained edge environments. In conclusion, EdgeClusterML emerges as a robust solution for edge computing environments, offering concrete benefits in terms of accuracy, clustering speed, resource utilization, and adaptability. Its reinforcement-learning-driven dynamic clustering approach positions it as a valuable asset for real-time data analysis and decisionmaking in dynamic edge scenarios. In the next steps, we evaluate Algorithm 3 in \[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]2  
In our evaluation of the CompressEdgeML algorithm, significant improvements were observed across various performance metrics in edge computing environments. The algorithm, however, shows a good compression efficiency of up to 95% on single-device configurations, highlighting its effectiveness in data size reduction. This efficiency slightly decreases as the number of devices increases, stabilizing at 88% for configurations with 10 devices, indicating a high level of data compression consistency. Compression speed, measured in milliseconds, displayed a marked improvement with increasing device numbers. For a single device, the compression time was logged at approximately 1200 ms,  
\[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]3 Performance evaluation of CompressEdgeML. \[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]4 Performance evaluation of EdgeClusterML. which reduced logarithmically to around 300 ms for 10 devices. This reduction showcases the algorithm's capability to handle larger data streams more efficiently, a critical attribute in real-time edge computing scenarios. Data integrity post-compression was maintained above 90% across all device configurations, peaking at 98% in a single-device setup. This metric underscores the algorithm's reliability in preserving essential data characteristics during the compression process. Resource utilization also showed a positive trend, with efficiency increasing from 70% in a single-device scenario to 85% in a 10-device configuration. This improvement indicates the algorithm's scalability and its efficient use of computational resources, which is vital in resource-constrained edge environments. In summary, CompressEdgeML demonstrates robust performance in adaptive data compression, marked by high compression efficiency, accelerated processing speeds, reliable data integrity, and efficient resource utilization. Its adaptability and scalability make it well-suited for diverse edge computing applications. The next algorithm is Algorithm 4, which is evaluated in \[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]5  
In our assessment of the CacheEdgeML algorithm, tailored for predictive and tiered data caching in edge computing settings, we observed substantial enhancements in pivotal performance metrics. The algorithm exhibited a cache hit rate of 85% in a single-device environment, which progressively increased to 92% with the addition of more devices. This upward trend signifies the algorithm's enhanced accuracy in predicting data requests, a crucial factor in reducing redundant data retrieval operations. The cache update speed, a critical measure in dynamic environments, improved logarithmically from 400 ms for one device to 250 ms for ten devices.