Updated_Data_Storage_Aggregation_Analytics_IoT.pptx

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1. Data Storage, Aggregation, and Analytics Techniques in IoT Overview of Techniques and Technologies

2. Data Storage in IoT Key Techniques: • Edge Storage: Local data storage on devices • Fog Storage: Distributed storage close to the edge • Cloud Storage: Centralized, scalable data storage • Hybrid Solutions: Combination of edge, fog, and cloud

3. Data Aggregation in IoT (After Storage) Aggregation Techniques: • Sensor-Level: Combining multiple sensor readings • Gateway-Level: Aggregation at gateways • Hierarchical: Multi-level aggregation from edge to cloud • Temporal & Spatial: Summarizing trends over time/space

4. Data Analytics in IoT Analytics Techniques: • Descriptive: Summarizing past data • Predictive: Using data to forecast trends • Prescriptive: Suggesting actions based on predictions • Real-Time: Processing data as it's generated • Anomaly Detection: Identifying unusual patterns

5. Integrating Techniques in IoT Key Points: • Storage first ensures data availability for aggregation • Aggregation reduces data volume for storage and analytics • Analytics can be performed at edge, fog, or cloud • Design must consider security, scalability, and application requirements

6. Conclusion Summary: • IoT requires efficient data management techniques • Storage, aggregation, and analytics are interdependent • Choosing the right combination of techniques is crucial