# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 4 Experimental Results
### Metrics and Methods
This acceleration highlights the algorithm's efficiency in adapting to changing data patterns, thereby optimizing caching strategies in real time. Cloud synchronization latency, through the HPC server, is essential for maintaining data consistency between edge and cloud storage, and it was also optimized. It decreased from 95 ms to 99 ms as the number of devices increased, demonstrating the algorithm's effectiveness in synchronizing large volumes of data swiftly across distributed networks. Data retrieval efficiency, indicative of the algorithm's performance in providing timely access to cached data, showed, however, a negative trajectory, decreasing from 200% in single-device setups to 140% in scenarios involving ten devices. This decrease shows that the algorithm requires more time to streamline data access, particularly in multi-device edge computing networks where data are distributed. In summary, CacheEdgeML emerges as a robust and adaptive solution for data caching in edge computing environments. Its strengths lie in its high cache hit rate, improved cache update speed, and reduced cloud synchronization latency. These attributes collectively  
\[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]6 Performance evaluation of CacheEdgeML. ensure that CacheEdgeML is well-equipped for optimizing data caching processes in complex real-time edge computing scenarios. Lastly, we evaluate Algorithm 5 in \[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]7  
In our evaluation of the TinyHybridSenseQ algorithm, specifically designed for IoT sensors with a focus on data quality assessment and hybrid storage, we observed notable improvements in several key performance areas. The algorithm achieved a high Data Quality Score of 95% in a single-sensor setup, which marginally decreased to 90% as the number of sensors increased to ten. This trend highlights the algorithm's robustness in maintaining high data quality standards even as the sensor network scales. Anomaly Detection Speed, a crucial metric in real-time environments, showed significant optimization. It improved logarithmically from 500 ms in a single-sensor scenario to 350 ms with ten sensors, illustrating the algorithm's accelerated response in identifying data anomalies. This enhancement is pivotal for timely anomaly detection in dynamic sensor networks. Storage efficiency, essential in optimizing data storage across local and cloud resources, also saw progressive improvements. It increased from 85% to 91% as the number of sensors expanded. This increase indicates the algorithm's capability to efficiently manage storage resources, a vital aspect in IoT environments where data volume is substantial. Data transfer latency, critical in ensuring the swift movement of data between sensors and storage facilities, was optimized with the expansion of the sensor network. It reduced from 250 ms for a single sensor to 160 ms for ten sensors, signifying the algorithm's effectiveness in reducing data transfer times across a distributed network. In conclusion, TinyHybridSenseQ stands out as an effective solution for IoT sensors, excelling in data quality assessment and hybrid storage management. Its strengths lie in maintaining high data quality, fast anomaly detection, efficient storage utilization, and reduced data transfer latency. These attributes collectively position TinyHybridSenseQ as a highly capable tool in managing complex data workflows in IoT sensor networks, ensuring both data integrity and operational efficiency.