Edge Computing

Understanding edge computing in manufacturing:

Why Edge Computing:

Latency:
- Real-time processing requirements
- Millisecond response times
- Cloud round-trip too slow
- Local decision-making

Bandwidth:
- Data volumes overwhelming
- Sending everything impractical
- Local filtering/aggregation
- Reduced network costs

Reliability:
- Continue operating offline
- Network disruption tolerance
- Local autonomy
- Resilient architecture

Data Sovereignty:
- Keep sensitive data local
- Regulatory compliance
- Security requirements
- Control over data location

Edge Architecture:

Device Edge:
- On/in the machine
- Gateway devices
- Industrial PCs
- Embedded systems

Near Edge:
- Plant-level processing
- On-premise servers
- Local data centers
- Aggregation point

Far Edge:
- Regional processing
- Cloud edge locations
- CDN edge
- Last mile before cloud

Edge vs Cloud:

Edge Strengths:
- Low latency
- Bandwidth efficiency
- Offline operation
- Data control

Cloud Strengths:
- Massive scale
- Advanced services
- Global access
- Cost efficiency at scale

Optimal Approach:
- Edge for real-time
- Cloud for analytics
- Hybrid architecture
- Right processing at right place

Use Cases:

Real-Time Monitoring:
- Machine health
- Quality monitoring
- Anomaly detection
- Immediate alerts

Local Analytics:
- OEE calculation
- Process optimization
- Predictive maintenance
- Quality prediction

Protocol Translation:
- Legacy machine connectivity
- Protocol conversion
- Data normalization
- IT/OT bridging

Autonomous Operations:
- Local control loops
- Production continuation
- Safety systems
- Independent operation

Edge computing technology landscape:

Edge Hardware:

Industrial Gateways:
- Protocol conversion
- Data collection
- Edge processing
- Vendors: Cisco, Dell, HPE

Industrial PCs:
- More compute power
- Local applications
- Visualization
- Ruggedized options

Edge Servers:
- Near-edge deployment
- Significant processing
- Multiple workloads
- Data aggregation

Embedded Systems:
- Device-level edge
- Specific applications
- Low power
- Purpose-built

Software Platforms:

AWS IoT Greengrass:
- AWS services at edge
- Lambda at edge
- ML inference
- Cloud synchronization

Azure IoT Edge:
- Azure services at edge
- Container-based
- AI/ML modules
- Stream analytics

Google Cloud IoT Edge:
- TensorFlow at edge
- ML focus
- Kubernetes-based
- Cloud integration

Vendor Solutions:
- Siemens Industrial Edge
- Rockwell FactoryTalk Edge
- AVEVA Edge
- GE Predix Edge

Open Source:
- EdgeX Foundry
- KubeEdge
- OpenEdge
- Community development

Key Capabilities:

Connectivity:
- Industrial protocols (OPC-UA, Modbus)
- IT protocols (MQTT, HTTP)
- Legacy connectivity
- Data aggregation

Processing:
- Stream processing
- Complex event processing
- Rule engines
- Local analytics

AI/ML Inference:
- Run trained models
- Real-time inference
- Computer vision
- Anomaly detection

Management:
- Remote deployment
- Configuration management
- Monitoring
- Updates and patches

Deploying edge computing successfully:

Architecture Design:

Edge Placement:
- What processing at what layer
- Data flow design
- Latency requirements
- Bandwidth optimization

Data Strategy:
- What stays local
- What goes to cloud
- Aggregation logic
- Retention policies

Connectivity:
- Network architecture
- Protocol selection
- Security zones
- Redundancy

Security:

Physical Security:
- Device hardening
- Physical access control
- Tamper detection
- Secure boot

Network Security:
- Segmentation
- Encryption
- Authentication
- Monitoring

Application Security:
- Container security
- Update management
- Access control
- Vulnerability management

OT Security:
- IT/OT separation
- DMZ architecture
- Minimal attack surface
- Industrial protocols

Operations:

Deployment:
- Configuration management
- Automated provisioning
- Consistency
- Rollback capability

Monitoring:
- Health monitoring
- Performance metrics
- Alert management
- Remote diagnostics

Updates:
- Security patches
- Application updates
- Firmware management
- Zero-downtime updates

Lifecycle:
- Device management
- Decommissioning
- Replacement
- Asset tracking

Integration:

With Cloud:
- Data synchronization
- Model updates
- Central management
- Hybrid workloads

With OT:
- Controller integration
- Historian connection
- MES integration
- SCADA systems

With IT:
- Enterprise systems
- Identity management
- Network management
- Security operations

Building edge computing expertise:

Technical Roles:

Edge Engineer:
Deploy and manage edge:
- Edge platform expertise
- Connectivity
- Local processing
- $80,000-$120,000

IoT Solutions Architect:
Design edge-to-cloud:
- Architecture design
- Platform selection
- Integration
- $100,000-$150,000

Embedded Systems Engineer:
Device-level edge:
- Firmware development
- Hardware integration
- Protocol implementation
- $85,000-$130,000

Manufacturing Roles:

OT Cybersecurity Engineer:
Secure edge deployments:
- Security architecture
- Vulnerability management
- Monitoring
- $90,000-$140,000

Digital Manufacturing Engineer:
Apply edge to manufacturing:
- Use case implementation
- Integration
- Optimization
- $80,000-$120,000

Skills Required:

Technical:
- Networking (TCP/IP, industrial protocols)
- Linux/embedded systems
- Containers and orchestration
- Programming (Python, C)
- Cloud platforms

Domain:
- Manufacturing processes
- OT systems understanding
- Industrial protocols
- Security requirements

Key Technologies:
- Docker/Kubernetes
- MQTT, OPC-UA
- Edge platforms (AWS, Azure, vendor)
- Time-series processing

Learning Path:
1. Networking and protocols
2. Linux and containers
3. Cloud IoT fundamentals
4. Edge platforms
5. Security best practices

Certifications:
- Cloud IoT certifications
- Networking certifications
- Security certifications
- Vendor-specific training

Edge computing skills bridge IT and OT, positioning professionals for IIoT leadership.