# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 4 Experimental Results
### Metrics and Methods
\[\text{Scalability}=\frac{\text{Throughput at $n$ nodes}}{\text{Throughput at a single node}}\] \[\text{Response Time Ratio}=\frac{\text{Response Time at $n$ nodes}}{\text{Response Time at a single node}}\] \[\text{Load Balancing Efficiency}=\frac{\sum_{i=1}^{n}\text{Load on Node $i$}}{\text{ Ideal Load per Node}\times n} \tag{5}\]  
System Capacity Utilization\(=\frac{\text{Total Processed Load}}{\text{Total System Capacity}}\)  
Cost-Effectiveness Ratio\(=\frac{\text{Total System Cost at $n$ nodes}}{\text{Performance Improvement Factor}}\)Starting with the evaluation of the first Algorithm 1, the results are shown in Figure 2.  
Incorporating the TinyCleanEDF algorithm into an IoT data management system has demonstrated quantifiable improvements in several key performance metrics. As shown in the preceding Figure, the deployment of this algorithm across an increasing number of nodes--from 1 to 10--has yielded substantial benefits. Specifically, the anomaly detection accuracy improved with more nodes. For instance, there was a 10% increase in the accuracy on a single node compared to ten nodes. This improvement highlights the algorithm's enhanced capability to identify and respond to data anomalies as the collaborative network of nodes expands. Moreover, the data processing and model training times, both critical for the efficient operation of IoT systems, show a decreasing trend as more nodes are engaged. Log-scaled values indicate that processing time decreased fourfold when the number of nodes increased from 1 to 10, which suggests a notable enhancement in the speed of data handling. Communication efficiency also saw a rise, which is particularly relevant in scenarios where network bandwidth is a limiting factor. This increase indicates a more optimal use of available resources, allowing for smoother data transfer between nodes and the central server. Lastly, scalability, which is also a significant metric, reflects the algorithm's ability to maintain performance despite the growing scale of the network. The consistent upward trend across nodes validates that TinyCleanEDF is well-suited for environments where expansion is anticipated, ensuring that the system not only sustains its performance but actually improves as it scales. These results underscore the effectiveness of TinyCleanEDF in enhancing data quality and system robustness, making it a compelling choice for federated learning applications in distributed networks. Moving on to Algorithm 2, the results are presented in \[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]0  
The integration of the EdgeClusterML algorithm within an edge computing framework such as FL has yielded remarkable improvements in critical performance metrics. Notably, the algorithm achieved an impressive accuracy rate of approximately 90% when applied to real-world data streams. This represents a significant enhancement in the precision of data clustering, making it well-suited for applications like anomaly detection and data-driven decisionmaking. The observed increase in accuracy is particularly noteworthy as it directly impacts the algorithm's ability to effectively group datapoints. \[\text{Data Processing Time}_{\text{total}}=\max(T_{1},T_{2},\dots,T_{n})\quad\text{ and}\quad\text{Data Processing Time}_{\text{avg}}=\frac{\sum_{i=1}^{n}T_{i}}{n} \tag{1}\]1 Performance evaluation of TinyCleanEDF. Furthermore, our analysis reveals significant reductions in the clusterind speed. Specifically, the algorithm exhibited a time reduction of approximately 10% in the clustering speed when transitioning from one to ten nodes. These reductions are crucial in edge computing scenarios, ensuring real-time responsiveness and rapid adaptation to changing data patterns.