IoT_Architecture_Detailed_Presentation.pptx

IoT architecture pot

1. IoT Architecture and Components Components, Comparisons, Simplified View, Functional Stack, Data Management

2. IoT Architecture Components • Devices & Sensors: Collect data from the environment. • Network: Enables communication (Wi-Fi, 5G, LoRaWAN). • Edge Devices: Preprocess and filter data. • Cloud Platforms: Store, analyze, and manage IoT data. • Applications: Deliver insights and user interaction. • Security: Protects data, devices, and networks.

3. Comparing IoT Architectures • Traditional Architecture: - Layered: Perception, Network, Processing, Application. • Cloud-Centric Architecture: - Focuses on centralized cloud services. • Edge-Centric Architecture: - Processing occurs closer to devices. • Hybrid Models: - Combine cloud and edge benefits for flexibility.

4. A Simplified IoT Architecture 1. Perception Layer → Sensors & Actuators. 2. Network Layer → Communication technologies. 3. Processing Layer → Edge/Cloud computing. 4. Application Layer → User-facing services. • Simplified architecture focuses on essential data flow and service delivery.

5. Core IoT Functional Stack • Device Layer → Sensors, actuators, IoT devices. • Connectivity Layer → Protocols and networks. • Edge/Processing Layer → Data filtering and edge analytics. • Data Management Layer → Storage and security. • Application Layer → Services and business logic. • Management Layer → Device management, provisioning, monitoring.

6. IoT Data Management and Compute Stack • Data Ingestion → Collect data from devices. • Data Transport → Secure transmission via protocols. • Data Storage → Cloud databases, data lakes. • Data Processing → Stream and batch processing. • Compute Models → Cloud, Edge, Fog computing. • Data Analytics → Machine learning, AI insights. • Data Visualization → Dashboards and applications.

7. Conclusion • IoT architectures can vary from layered to cloud/edge-centric. • Simplified models highlight the essential data flow. • Core functional stacks ensure scalability and security. • Data management and compute stacks drive analytics and insights. • Together, these ensure a robust IoT ecosystem.