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Three reasons why edge architectures are critical for IIoT

One of the many promises of the industrial internet of things is that it can help companies generate massive amounts of data. However, this data is only valuable if it can be accessed and acted upon quickly, efficiently and safely. Effectively accessing data can be especially challenging when you have “things” — such as sensors, devices, flow computers and more — that live on remote areas of the network. Often referred to as the “edge,” these remote areas can host trillions of machines that contain important industrial data.

The edge could be remote tools in the field, machinery on a plant floor across the world, or any other asset that provides data in a location far from where data is acted upon. The data from these remote sites has the potential to generate valuable business, but is often too far away, too expensive or too insecure to transmit for time-critical operations. Paradoxically, the importance of processing edge data grows as that edge gets farther away and harder to access — such as on an offshore oil platform, where accessible data can help proactively address high-risk safety issues and high-cost maintenance concerns.

Kepware_IIoT_EdgeComputingEdge computing devices can solve the challenge of making this data available in real time. Here are three reasons why edge computing should be a key component to your IIoT plan:

1. Edge computing is the next evolution of cloud computing

Companies often look to the IIoT to bridge the gap between information technology and operational technology. Many data-rich resources live in the cloud, but are not directly accessible between these two vital departments. Edge computing is the key to the proverbial data kingdom that exists within the cloud.

Edge computing pushes the intelligence, processing power and communication capabilities of a gateway or appliance directly into devices. These devices can then use edge computing capabilities to determine what data should be stored locally and what data should be sent to the cloud for further analysis. As the IIoT grows in capability and connectivity, there will be a move away from cloud computing and a move towards edge computing. Increasingly, edge devices will begin to handle their own processing and storage, while the cloud will morph into the strategic “brains” behind it all. These devices will send only the most important data to the cloud and the cloud will analyze and then share what it learned with all the devices.

2. Edge computing alleviates network bandwidth limitations and cuts costs

Transmitting large data sets over a wide network area has a high financial cost. The common solution is to store the same data twice — locally and at the enterprise data center. But this often requires new expensive levels of required bandwidth, resulting in service degradation, data latency and security concerns — all which still require additional cost.

Edge computing eliminates the need for costly bandwidth additions. Low-cost edge gateways can keep computing and data storage on the edge and host localized and task-specific actions to analyze edge data in near real time. This means much less data needs to be transmitted back to the core server — where enterprise-level applications reside, saving on bandwidth requirements.

3. Edge computing addresses security concerns

As discussed in my previous TechTarget post, data in transit is data at risk. Sensors and things on the edge can be especially vulnerable to security threats. Because industrial equipment is typically designed to last for decades, the majority of connected devices today — and for the foreseeable future — will be from legacy equipment already operating in the plant or field. Many of these protocols for industrial communications are not secure by today’s standards; some were specifically stripped down and designed for low bandwidth networks back in a time of simpler security threats.

Gateways designed to work with the edge can alleviate security concerns by keeping sensitive data within a local network and analyzing it within a secure system. Edge computing also accelerates awareness and response to possible security threats by eliminating a round-trip to the cloud for analysis. And instead of using unsecure legacy device protocols for communications across a wide area network, companies with edge computing can use more modern communication techniques — such as MQTT, OPC UA, AMQP and CoAP — designed for secure and efficient network communications.

Getting an “edge” on connectivity

Edge computing devices can improve safety, efficiency and productivity for companies looking to seamlessly analyze and act on data from the edge of their network. Collecting and processing data closer to where it is produced can help optimize IIoT initiatives to realize unforeseen benefits. As the industry moves more towards enterprise-wide connectivity, edge computing capabilities are key for success in creating a technology architecture built towards the future.

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