Industries have seen a change in how manufacturing, power generation, and processing facilities gather and process information on their operational data. There are numerous connected sensors that provide continuous streams of information on equipment condition, production process, environment, energy consumption, etc.
However, the direct transmission of all this data to the cloud is not always feasible.
This is one of the reasons why edge computing is becoming an essential part of contemporary industrial architecture.
The Problem with the Cloud-Based Solution
The cloud allows for scalability, centralized analysis, and long-term data storage. At the same time, the industrial landscape creates some additional challenges that were not intended when creating cloud-based applications.
Namely:
- Network connectivity problems
- Latency issues
- A huge amount of data generated by sensors
- Critical operations
- Remote locations
In case there is no cloud connectivity for the manufacturing facility, this may result in delay of decision-making.
What Is Edge Computing?
Edge computing means performing calculations using data that was collected nearby.
It is not necessary to transfer all the data collected by sensors to a server since edge devices process the data on-site and transmit only the essential data to the cloud.
Advantages of Edge Computing
Shorter Response Time
In many cases, industrial machines require prompt action in case something goes wrong.
Edge devices can analyze the data and provide a reaction instantly, rather than waiting for cloud processing.
Increased Reliability
Internet connection at factories or industrial plants may be unstable from time to time.
In this case, edge devices still collect and process the data on-site until the connection is established again.
Lower Bandwidth Cost
The collection of high-frequency data results in tremendous data exchange through the network.
Edge devices can filter, compress or aggregate data to minimize cloud storage and communication cost.
Increased Security
Data collected by the organization stays within its limits without being unnecessarily transmitted to outside parties.
It adds another layer of security to the existing cybersecurity approach.
Common Industrial Applications
Edge computing is now widely applied in:
- Equipment condition monitoring
- Predictive maintenance
- Environmental monitoring
- Energy management
- Machine vision
- Industrial automation
- Quality inspection
- Emission monitoring
Local processing provides fast operations and retains centralized view in all the cases above.
Creating a Scalable Architecture
A successful implementation of Industrial Internet of Things usually uses both edge computing and cloud computing, not choosing one at the expense of another.
Edge computing makes operational decisions instantly, while cloud computing does analytics and reporting based on the history.
It is a scalable and performant solution.
The Future
While Industrial IoT is developing, edge computing is going to gain greater importance. Developments in AI, machine learning and intelligent edge hardware will allow detecting anomalies, making predictions and operating autonomously right in the industrial sites.
Companies creating resilient edge-first architectures now will benefit from it in the future digital transformation projects.
Final Words
Edge computing has now become a basic building block for dependable Industrial IoT architectures. By processing data closer to industrial assets, businesses will be able to achieve improved reaction time, increased dependability, lower cost, and sound decision-making.
For further reading on Industrial IoT, environmental monitoring, emissions stack technologies, and connected industrial solutions, visit the website of Emissions and Stack.
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