Learning Objectives
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# Learning Objectives - Understand cloud service models (IaaS, PaaS, SaaS) - Compare cloud, fog, and edge computing - Apply cloud to industrial use cases - Networking basics - Understanding of data processing ## 1. Cloud Service Models **IaaS (Infrastructure as a Service):** Virtual machines, storage, networks.
Learning Objectives
- Understand cloud service models (IaaS, PaaS, SaaS)
- Compare cloud, fog, and edge computing
- Apply cloud to industrial use cases
- Networking basics
- Understanding of data processing
1. Cloud Service Models
IaaS (Infrastructure as a Service): Virtual machines, storage, networks. AWS EC2, Azure VM. Most control, most responsibility. PaaS (Platform as a Service): Development and deployment platform. Azure IoT Hub, AWS Greengrass. Focus on applications. SaaS (Software as a Service): Ready-to-use software. Salesforce, Office 365. Least control, easiest to use.
2. Computing Continuum
Cloud: Centralized data centers. High latency (50-200ms), unlimited storage, massive compute. Best for historical analytics, ML training, long-term storage.
Fog: Intermediate layer (on-premise servers). Lower latency (10-50ms), moderate storage. Best for local dashboards, data aggregation, real-time alerts.
Edge: Devices near production (PLCs, gateways). Ultra-low latency (1-10ms), limited resources. Best for real-time control, data filtering, fast decisions.
3. Industrial IoT Platforms
- AWS IoT Core: Device management, data ingestion, rules engine
- Azure IoT Hub: Bi-directional communication, device twins
- Siemens MindSphere: Industrial IoT platform
- PTC ThingWorx: Industrial connectivity and AR
Q1: Compare IaaS, PaaS, SaaS with examples.IaaS: VMs (AWS EC2). PaaS: dev platform (Azure IoT Hub). SaaS: ready software (Office 365). Q2: When should edge computing be preferred over cloud?When low latency is critical (real-time control <10ms), bandwidth is limited, data privacy required, or connectivity is intermittent. Q3: What is the role of industrial IoT platforms?Device management, secure connectivity, data ingestion, message routing, device twins, analytics, integration with enterprise systems. Q4: What is fog computing?Decentralized infrastructure between cloud and edge. Typically on-premise servers that aggregate data from edge devices, run local analytics, and communicate with cloud. Q5: What is serverless computing?Run code without managing servers. Pay per execution. AWS Lambda, Azure Functions. Useful for event-driven IoT applications. Join Discord PreviousDigital Twins & SimulationNextCybersecurity in Industry 4.0