This is the second article in a two-part series. Read part one here.
In my earlier article, I discussed the many benefits of smart devices, especially in industrial environments. I also discussed the challenges of capturing and analyzing data at the edge of the network. Now, it’s time to dive into how to establish an edge computing architecture for processing raw IIoT data.
The case for edge computing
While pushing all of your data up to the cloud is possible, it doesn’t provide you the desired value from a performance standpoint. The key to harnessing raw data produced by intelligent devices is to first turn the data into useful information at the source.
Edge computing is the best way to achieve this, as the technology provides cleaner data, better data protection and lower costs. Cleansed data ensures the supervisory systems receive the highest quality information, while isolated smart devices are better equipped for defending against cyberattacks across an entire enterprise. Significant cost savings are possible by incorporating edge computing into an existing IIoT-to-cloud system, driving data reduction.
Steps to success
As with all products, there are some challenges associated with edge computing. Considerations include developing a plan, fielding personnel support and factoring in cybersecurity. At the end of the day, the goal for an edge computing solution is to provide clean data in a secure real-time manner to support mission critical demands. So, how can your company achieve this?
A proper edge computing platform must be powerful, protected and autonomous. It must also be simple to deploy quickly and manageable by remote site personnel. If disaster does strike, a single button restore is the difference between getting back online in a few minutes versus a few days.
The first step is to establish an overall architectural strategy for your edge computing platform. This will provide the roadmap for connecting your smart devices at the edge and sharing that data with the cloud. A comprehensive architecture addresses four levels: interoperating with field devices, capturing data, controlling information and connecting with cloud intelligence. The final goal of your strategy here is to transmit useful information up to the cloud, enabling long-term analytics and even artificial intelligence.
Next, you need to establish a secure connection. Often, securely connecting assets involves logical concentrations of smart devices at the site. These concentrations must be identified so that the edge computing hardware can be located near these areas. This helps with troubleshooting and support. Often times, the best locations for an edge device are in harsh environments, requiring a ruggedized solution.
Here, you may want to consider a purpose-built edge computing hardware platform. These products are built for industrial service, optimized for the edge-located role, and tailored to the personnel who will support them. Integrated redundancy with no single point of failure incorporated into these products means that these systems will run indefinitely, while autonomous self-monitoring and remote management minimize maintenance effort.
As data available from IIoT and smart devices grows, the need for edge computing is becoming clearer and clearer. This type of data requires pre-processing at the edge of the network to foster efficient and cost-effective services. The most ideal way to meet the needs of smart devices is through edge computing solutions that use purpose-built platforms deliver accurate and secure data analysis.
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