IIoT initiatives generally begin on a smaller scale with a handful of sensors, gateways, and dashboards in the cloud. However, the challenge comes later when the very same IIoT system is expected to handle hundreds or even thousands of different devices across several locations.
However, scaling IIoT initiatives does not solely involve expanding the architecture. This is the way you design reliable, secure and manageable systems despite growing complexity. Here are five key engineering principles for doing so.
Manage Devices as Assets
Devices become assets that have to be managed during their entire lifecycle.
Scalable infrastructure should provide the following features:
- Device onboarding
- Remote configuration updates
- Firmware version management
- Certificates & credentials management
- Monitoring devices' state
If there is no management, operating a big fleet of devices becomes costly very soon.
Data Processing at the Point of Its Sensibility
It is not always practical to process all sensor information in the cloud.
There are plenty of applications where processing on the spot is more logical than processing in the cloud. It will help avoid latency issues, reduce the amount of necessary bandwidth and still work without internet.
Edge computing examples:
- Threshold-based alerts
- Anomaly detection on premises
- Control of the machine
- Filtering of data for cloud processing
Cloud computing still serves useful for archival purposes, analysis, and fleet-wide reporting, but edge computing is a better choice for improving response time.
Design Security into the System
Don’t regard security as an add-on feature that gets implemented shortly before going live.
For example:
- Secure communication between devices
- Mutual authentication
- Credential rotation
- Least privilege access policy
- Behavioral monitoring of devices
Industrial deployments may continue to operate for years, and that’s why it’s important to plan for security long term.
Establish Communication Standards Early
As deployments grow, inconsistent communication techniques will cause unnecessary complexity.
Utilizing existing protocols and consistent data models can contribute to increased interoperability of devices, gateways, analytics engines, and operational systems.
Common technology includes:
- MQTT
- OPC UA
- Modbus
- Ethernet/IP
Establishing these standards at the outset may greatly reduce any future integration efforts.
Monitor Business Outcomes alongside Technical Performance
Technically successful deployments do not necessarily equate to business successes.
Aside from monitoring uptime and device connectivity, companies should be tracking metrics like:
- Planned downtime reductions
- Maintenance cost reductions
- Asset utilization
- Efficiency
- Production output
Engineering decisions that lead to operational outcomes make future value more tangible.
Conclusion
The Industrial IoT system becomes much more efficient when engineering choices focus on reliability, maintainability, and operation results instead of merely adding to the number of connections.
The most successful cases involve the marriage of solid engineering approaches and business goals enabling the scaling without making things more complicated than they need to be.
If you are looking for a more general view on the subject of AI, Industrial IoT, and connected industrial systems, Aperture Venture Studio will share some technical information on them.
Scaling is not about foreseeing all your future needs but building systems capable of growing without starting from scratch.
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