1 - Centralized Data Aggregation
- Data Reception: Securely retrieve processed data from local nodes.
- Aggregation: Combine multiple data streams over time to highlight patterns.
- Normalization: Standardize schemas so that metrics from different sensors align for deeper insights.
2 - Heavy Computational Tasks
- Advanced Analysis: Use deep learning to build rich merit assessments.
- Model Training: Continuously retrain models with aggregated historical data.
- Resource Allocation: Dynamically scale computing resources for large user populations or extended analytics.
3 - Data Synchronization Protocols
- Scheduling: Automate data uploads at regular intervals (hourly or daily).
- Conflict Resolution: Merge conflicting sensor logs by prioritizing timestamps.
- Bandwidth Optimization: Compress large media files to save network capacity.