Analytics done on the live IoT data in Edge helps businesses to take informed decisions to improve business efficiency, reduce cost and quicker response on issues. Analytics can be executed on the Edge side, Informed decision can be taken based on these analytics and overall cost can be reduced by 20% by moving Analytics to Edge, closer to where data are generated.
Edge Analytics is an approach to data collection and analysis in which automated analytical computations are performed on data close to the source instead of waiting for the data to be sent back to a centralized location. These analytical services can run on a router, switch, gateway, or server in the operational environment. Analyzing data as it is generated decreases latency in responding to emerging conditions, and increases scalability by filtering out low-value data and reducing the network burden of moving large volumes of data.By distributing data processing and analytics out to the edge of the network, you can more easily apply a rich set of analytics on site with the flexibility to start small and scale-up and still maintain continuous operations even if the network goes down.
Edge Analytics provides the opportunity to implement analytics on an Edge device that is deployed at the ‘edge’ of a network where data from connected devices is collected. This allows for Actions to be taken against the device data prior to that data being forwarded on PAASMER IOT Platform in the Cloud. Analyzing data at the Edge can decrease latency in the decision-making process on connected devices.
You can send data from Edge Analytics to your back-end system when you need to perform analysis that cannot be performed on the edge device, such as:
- Running a complex analytic algorithm that requires more resources, such as CPU or memory, that are available on the edge device.
- Maintaining large amounts of state information about a device, such as several hours worth of state information for a patient’s medical device
Edge Analytics communicates with your back-end systems through the following message hubs:
- MQTT – The messaging standard for IoT
- PAASMER IoT Platform – A cloud-based service that provides a device model on top of MQTT
- Apache Kafka – An enterprise-level message bus
- Custom message hubs
Your back-end systems can also use analytics to interact with and control edge devices.
- A traffic alert system can send an alert to vehicles that are heading towards an area where an accident occurred
- A vehicle monitoring system can reduce the maximum engine revs to reduce the chance of failure before the next scheduled service if it detects patterns that indicate a potential problem.
Edge Analytics can be used to filter the data on the Edge Device using any of the following methods:
- partitioned window
- continuous aggregation