Edge Computing in IoT: Processing Data Where It Matters
As IoT deployments scale to millions of devices generating petabytes of data, sending everything to the cloud becomes impractical and expensive. Edge computing brings intelligence closer to the data source, enabling real-time decisions, reducing costs, and ensuring operations continue even when connectivity fails.
The Latency Problem
Consider a manufacturing quality inspection system. A camera captures 30 frames per second. Each frame needs AI analysis to detect defects. The production line moves at 2 meters per second.
With cloud processing:
- Upload time: 50-100ms (depending on image size and network)
- Processing time: 100-200ms (including queue wait)
- Response time: 20-50ms
- Total: 170-350ms
In 350ms, a defective product has moved 70cm down the line. By the time you know there's a problem, it's too late to intervene.
With edge processing:
- Local inference: 20-50ms
- Total: 20-50ms
The product has moved only 10cm—plenty of time to trigger a rejection mechanism.
Why Edge Computing Matters
Ultra-Low Latency
Process data in milliseconds, not seconds. Critical for real-time control systems.
Data Sovereignty
Keep sensitive data on-premises. Essential for compliance and security requirements.
Network Independence
Continue operating even when cloud connectivity is interrupted.
Reduced Bandwidth
Process locally, transmit only what matters. Lower costs and network load.
Architecture Patterns
There's no one-size-fits-all approach to edge computing. The right architecture depends on your specific requirements:
Cloud-Centric
All processing in the cloud
- + Simple architecture
- + Unlimited compute
- + Easy updates
- - High latency
- - Bandwidth intensive
- - Cloud dependency
Low-frequency monitoring, non-critical analytics
Edge-Heavy
Most processing at the edge
- + Lowest latency
- + Works offline
- + Data privacy
- - Complex management
- - Limited compute
- - Update challenges
Real-time control, air-gapped environments
Hybrid
Intelligent workload distribution
- + Optimized for each use case
- + Resilient
- + Cost-effective
- - Architecture complexity
- - Synchronization needs
Most production IoT deployments
The Hybrid Architecture Deep Dive
Most production IoT systems benefit from a hybrid approach that processes data at multiple tiers:
Tier 1: Device Level
Processing happens directly on the sensor or actuator:
- Signal filtering and noise reduction
- Threshold-based alerts
- Data compression before transmission
- Local control loops (e.g., PID controllers)
Tier 2: Gateway/Edge Server
An on-premises server aggregates data from multiple devices:
- Protocol translation (Modbus, BACnet, etc. to MQTT)
- Real-time analytics and ML inference
- Local dashboards and alerting
- Store-and-forward during connectivity outages
- Cross-device correlation
Tier 3: Cloud Platform
The cloud handles workloads that benefit from centralization:
- Long-term data storage and historical analytics
- Cross-site aggregation and benchmarking
- ML model training (edge runs inference)
- Integration with business systems
- Global fleet management
Workload Placement Decision Framework
When deciding where to process a specific workload, ask these questions:
What's the latency requirement?
<100ms → Edge required | <1s → Edge preferred | >1s → Cloud acceptable
What happens if connectivity fails?
Critical operations continue → Edge required | Degraded but safe → Hybrid | Non-critical → Cloud OK
What's the data volume?
High frequency/volume → Edge aggregation | Moderate → Direct cloud | Low → Either
Are there data sovereignty requirements?
Yes → Edge processing with anonymized cloud sync | No → Either
Edge Hardware Options
The edge computing hardware market has matured significantly:
- Industrial PCs: Ruggedized servers for harsh environments (Advantech, Dell Edge, HPE Edgeline)
- AI Accelerators: Purpose-built for ML inference (NVIDIA Jetson, Google Coral, Intel NCS)
- IoT Gateways: Compact devices for protocol translation and basic processing (Raspberry Pi, industrial variants)
- Micro Data Centers: Self-contained compute pods for larger deployments
Cereb's Edge Strategy
The Cereb platform supports flexible edge deployment:
- Edge Agent: Lightweight container deployable on any Linux device
- Local Processing: Run alerting rules and basic analytics at the edge
- Store-and-Forward: Buffer data during cloud outages, sync automatically when reconnected
- Model Deployment: Push trained ML models from cloud to edge for inference
- Unified Management: Single pane of glass for both edge and cloud components
Design Your Edge Architecture
Our team can help you design the optimal edge-cloud architecture for your specific IoT requirements.