Reducing Cloud-dependency and boosting data-crunching capabilities, Edge Analytics might prove to be a game-changer in the IoT era
As devices increasingly adapt to the Internet of Things (IoT) in an always-connected communication environment, innovators are focussing more on finding out improved methods of data collection. As a result, we see a diverse range of data sources and endpoints being pressed into use with great urgency. But that means there is an increasing need to effectively process this huge gamut of data in real-time environments. And processing is just the beginning – because all collected data are essentially unstructured and requires to be arranged in a way that can lead to meaningful harvesting of the database. This is now being capably done by analytics-oriented Cloud platforms. However, there it has to constantly struggle with “latency” – also known as “total round trip time” in networking terms.
Latency is defined as “the delay between a user’s action and a web application’s response to that action.” In networking, this means the total round-trip time a data packet takes to travel. Measured in milliseconds, latency is unavoidable in network communication. Four main factors contribute to latency:
- The physical medium of transmission. For example, copper cables have a higher latency than optic fibers.
- Latency depends on the distance between two communicating nodes – farther the nodes are placed, higher the latency.
- Router efficiency.
- Delay in processing and retrieving data from storage pools.
Edge analytics has successfully negotiated around reach of these hurdles to enable faster quick data processing with low latency. In Edge analytics, incoming data streams are analysed at a non-central point in the system. This means data is processed by the IoT-enabled device itself or by local servers – and not being transmitted to remote data centres or Cloud. Such processing takes place at or near a sensor, network switch, peripheral node, or other connected devices.
Edge analytics software are usually embedded within connected devices that are optimized for low power and cost but lack the ability to retain and perform proper analytics on voluminous data. Edge, however, performs a small manoeuvre which makes it the game changer: it combines the incoming data with prior data already existing within Edge, and comes up with new analytical output – all without the need for any Cloud-based intervention!
However, experts do not foresee Edge computing totally replacing the Cloud technology; each has its own place and use in the big picture.
The Sharp Edge
- The decentralized model grants Edge analytics an “edge” over the traditional Big Data approach. It is a more reliable method to manage the surge of data that IoT technology brings with it.
- Edge emerges as a faster, quicker, and more precise business intelligence system that also lightens data-load on the network.
- Edge analytics reduces dependencies on backend servers and enables analytical capabilities in remote locations by relying on metadata rather than network transmission.
- It offers the added benefit of reducing bandwidth and yet enhancing data security and privacy safeguards.
- It enables real-time alerts for outages, ensures consistent uptime and provides real-time data streams for machine learning and predictive maintenance initiatives.
- Edge supports autonomous decisions-making by identifying what and when to transmit information and enabling succinct reporting without missing out on key information.
- Edge analytics is predicted to augment computer vision and video analytics as it can support distributed structured video data processing – by analysing frame-by-frame footage directly from camera in realtime.
- Such capabilities will prove vital in emerging technologies like intelligent traffic systems, autonomous vehicles, voice-driven AI assistants, and remote healthcare, to name a few.