Internet of things offers maximum benefit to enterprises only when you are able to leverage the effectiveness of analytics as a growth engine. After all, faster analytics is what helps you make quicker and more accurate decisions related to various operational parameters. In this blog, I will discuss the significance of on-board (edge) analytics in an industrial IoT setting and its implementation.
Essentially, the IoT framework can be broadly visualized as two separate components, namely, ‘edge devices & gateways’ and ‘server side building blocks’ which include cloud systems for data storage, BI/analytics, and data retrieval mechanisms.
The challenge with traditional analytics
With the number of IoT field devices expected to rise to 50 billion by 2020 (MarketsandMarkets, 2016), a tremendous amount of data is being generated and sent across to the cloud every second. Some of the drawbacks of cloud computing include service outages, bandwidth costs and vulnerability to attacks. In such a scenario, the traditional approach to performing analytics (in a data center) is becoming increasingly burdensome because the data generated at the source is often raw and unstructured. Analyzing this unfinished data incurs bandwidth costs. Besides, there’s no guarantee that the data flow will be continuous in a real time environment. For instance, a streetlight could be turned off the moment its data was being analyzed elsewhere.
For enterprises and OEMs trying to enter the market space of industrial IoT systems, the real challenge is being cost-effective in transporting large volumes of information to the data center. This calls for a more closely monitored and controlled industry environment that can result in higher efficiency, lower costs and minimal downtime by way of predictive maintenance.
Implementing on-board analytics across IoT field devices
A pragmatic solution is injecting on-board analytics into the device itself instead of having a separate data center sort out your big data issues. Harnessing the sheer computational power of smart edge devices offers the following advantages over traditional methods:
- Reduced data traffic distance and volume
- CPU capabilities on an as-needed, real-time basis
- Decentralized storage, communication, control and measurement
- Higher scalability and low latency
- Improved QoS
- Better user experience
- Computational power at lower cost
How to implement edge analytics in IIoT systems
In order to implement edge analytics in industrial IoT systems, it’s important to have a mechanism that can concurrently use several leading edge technologies, that is, wireless sensor networks, mobile data acquisition, ID tracking, people counting, object identification, mobile signature analysis, P2P ad hoc networking objects, user profiling systems for security, decoy systems and many more.
Implementing edge analytics not only requires in-depth understanding of above technologies but also the time when it’s most effective to analyze the data at hand.
eInfochips, having worked extensively on edge devices development with on-board analytics for its clients in industrial automation and has a strong expertise across the sensors to analytics as far as IIoT goes, is very well equipped to deliver value to the clients through on-board analytics as a standalone service or complete IoT implementation. eInfochips has been able to harness the power of edge analytics in the client use case for video IPs. To know more about our capabilities, email us at marketing@einfochips.com
