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Edge Computing in IIoT - Part 1
One of the key attributes brought about by industrial IoT (IIoT) is the shift of focus to increased efficiency and lower costs using smart edge devices and Big Data analytics. However, massive data transmissions to and from many IIoT devices in remote sites, over diverse network connections and in a timely manner is challenging. Also challenging is the need for actionable information (i.e., real-time analytics) to make sense of all the data collected. Enter edge computing – a nifty approach to addressing these issues that has become increasingly popular.
So what is edge computing?
The most common view of edge computing is a decentralized cloud environment, where distributed storage and processing of data is performed closer to the end points (i.e., “the edge of the network”) rather than at the data canter. Such end points include load breakers, meter concentrators, sensors, CCTV cameras, traffic monitoring and control units, remote PLC controllers, and other automation-enabled devices, among many others. These are connected to the operational network and control center via an IIoT gateway. The addition of cloud computing resources to the IIoT gateway at the edge enables local storing and processing of data. This presents compelling benefits when compared to centralized cloud computing as it provides:
- Higher reliability, as data doesn’t need to travel to a central cloud, thereby avoiding interrupted communication if the link is down
- Lower latency and consumption of network resources, for the same reason
- Better security and compliance with regulation as data isn’t exposed when traveling over public links
Edge computing in IIoT
Cloud computing and virtualization enable multiple applications to run simultaneously and independently on the same hardware. In the context of IIoT, this means that a single IIoT gateway hosts networking and non-networking functions. The latter also include software-based industrial functions and customized applications for IIoT management, which can be independently and quickly delivered.
The end result is less “boxes”, as less networking and IIoT devices are required. This also means better security as the function that needs to be secured is virtualized within the securely connected IIoT gateway itself. In addition, more anomaly detection functions (both cyber and network related) can be used.
Simply put, edge computing provides the required insight and agility, while reducing the number of required devices in remote sites.
To learn more about edge computing in IIoT click here.
In our next blog post in the series, we’ll review the factors impacting the choice of virtualization technology for IIoT.
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