Analyzing data at the edge is a method used to collect, process, and analyze data at some non-central point within a network—at the network's edge—rather than on the cloud or within another centralized system. This analysis usually occurs at or near a sensor, network switch, peripheral node, or some other connected device. The decentralized, local nature of analysis performed on an edge device has a benefit over more traditional big data methods in that it is much faster, leading to quicker, more accurate business intelligence while also lightening the load on the network.
There are many benefits to edge analytics, including:
The IoT edge architecture consists of various devices that each play their role in an intelligent environment. They include:
Sensors are the types of “things” people usually think of when they think of the Internet of Things. Sensors on the IoT measure variables such as temperature, light, location, moisture, and anything else that collects data from a physical object, such as a piece of manufacturing equipment.
This is a critical component of IoT edge architecture. Smart gateways are like traditional IoT gateways, enabling data transfer between local devices and the cloud, but with additional “smart” features. These devices utilize common field protocols such as Bluetooth and Wi-Fi (among others) and can translate between these to cloud protocols like MQTT and HTTP. Industrial IoT Gateways can send local data to the cloud for storage and analysis as well as analyze local data for low-latency intelligence such as real-time anomaly detection.
Edge Computing for IoT architecture.
For a more in-depth look at edge computing and analytics, including a deep-dive on edge platforms, edge devices, and the importance of the edge for manufacturing, read our eBook: "The Edge: A New Frontier for Manufacturing Analytics." Or explore the MachineMetrics Edge Platform.
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