The Rise of Edge Analytics

Author is Balaji Sivakumar, Director – Product Marketing, Western Digital

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Imagine a child holding a red balloon in a crowded park with her mom on a beautiful spring afternoon. Her mom looks away momentarily, distracted by some noise and when she looks back, her child is gone. She desperately yells her child’s name & bystanders join in the desperate search. They quickly report to the information desk in the park, where the security officers view the surveillance camera feeds both closest to the location where she went missing and across the park camera network, looking for a child with a red balloon. The officers locate the child who had wandered off, within just a few seconds. Sounds like a sci-fi movie? Nope, in fact, the capabilities have progressed even more than this simple example with the use of Artificial Intelligence (AI). In a recent real-life example, China’s facial recognition surveillance camera network was able to apprehend a suspect in a crowd of 60,000 concert goers – possible today thanks to real-time “Edge” analytics.

What is Edge Analytics?

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In a typical Internet of Things (IoT) system, every connected device except for the cloud is considered an Edge device e.g. mobile phones, surveillance cameras, connected automobiles, internet gateways etc. Today all our data is digital & the number of connected devices is expected to grow to 27 Billion in just three years, by 2021. From these devices, there has been an explosion of data generated & the location where data is stored & analysed has become critical to extract maximum value from the data.

Now, imagine in the missing child scenario, if the surveillance video feeds from multiple cameras were sent to the cloud over 4G, analysed, and then information sent back to the authorities on the ground. The time to act would be delayed & results may have been tragic. Real-time analytical tools need to work with real-time data, and the best place to do that is where the real-time data sits – on the Edge. Edge analytics is the lynchpin that drives real-time decision-making. So, Edge analytics means increased intelligence on the Edge devices. This translates to increased processing capabilities (compute) as well as higher storage on the Edge devices.

The ability to analyse and extract value from data real-time is a game changer for all industries. Edge analytics is leveraged by all vertical segments – smart city, manufacturing, retail, healthcare etc.

The Edge device delivers timely, informed decisions

At a high level, there are three crucial functions that define an Edge device – Compute (processor), Storage and Communication. Depending on the application, there may be a need for faster processing, multi-core to save power, more sensors, contextual awareness, and huge storage capacity.

In the past, raw data content sits at the Edge and metadata is sent to the cloud but increasingly, context complements content. Both content and context drive informed real-time decisions. Raw content from the constant inflow of data from sensors must be turned readily into information or context. Technologies like AI, Machine Learning (ML), image recognition, when applied onto Edge devices, interact with real-time data to generate this context. With context, we gain quick and actionable insights to immediate environmental stimuli. Access to this kind of information ultimately creates a more efficient and effective environment.

For instance, imagine a home surveillance camera, in the past, when someone rings your door bell, you could determine whether it’s someone you know – such as a relative or maid – before you open the door. But by adding facial recognition software, the technology could verify that a delivery truck driver is truly an employee of that company by tapping into an employee database. If, the software could intimate you that the person is un-recognised, then this actually warns you to open the door with more caution, if really required.

Another consideration is in the automotive industry. With connected cars or fully autonomous vehicles, proximity sensors are critical to identify impending danger and play a key part in autonomous driving. These vehicle’s proximity sensor data however may never be pushed to the cloud because it can’t afford the lag time. When your vehicle is too close to an object for comfort, the analysis needs to be in real-time, on real-time data, in the car itself for a real-time response. On the completion of the journey however, the proximity data may no longer be needed (since the environment has changed). Thus, this kind of data could be flushed from memory post occurrence (usefulness).

To truly take advantage of actionable insights and real-time response on Edge devices, both reliable, resilient storage and powerful analytical tools will need to reside at the Edge. Where timeliness is critical, this actually converts content into context and passive decisions into informed decisions.

Embedded solutions help capture, aggregate, transform and preserve data across IoT devices from smart phones to drones and surveillance cameras, connected cars, appliances, personal devices, sensors and more. These devices live at the Edge where our embedded solutions work behind the scenes in increasingly smaller, but simultaneously more powerful solutions. AI is hard at work at the Edge, analysing and processing data to give real-time feedback.

Edge Analytics – Future Trends

AI seems to be touching our lives at every turn. It used to be that you would listen to radio traffic reports during your morning commute. You might hear about a car accident on the radio and take a different route. Today, AI-infused talking personal assistants on our smart phones can tell us the best way home and even reroute us along the way. We know more about our surroundings than ever before.

We are also starting to learn more about what’s going on inside the human body. There are new Edge devices that go beyond tracking steps, heart rate and calories burned. These devices are enabled by AI to have machine-learning (ML) capabilities. They can detect early signs of sickness or stress and suggest appropriate course of actions: take your vitamins, get some sleep, or go see a doctor! The so-called empathetic AI to help with depression is already here and will only be advanced to help us live a healthier and more fulfilling life.

Although we are seeing more AI-driven applications in our everyday world, AI is still in its infancy. We’ve only scratched the surface of what it will become. As we can see from a series of talks by Jason Silva, a technology enthusiast and self-proclaimed futurist, data is now alive. It’s talking back to us. It’s driving IoT to ultimately becoming a world of intelligence, by leveraging the Edge.

FORWARD-LOOKING STATEMENTS: This article contains forward-looking statements, including statements relating to expectations for storage products, the market for storage products, product development efforts, and the capacities, capabilities and applications of Western Digital products. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed in the forward-looking statements, including development challenges or delays, supply chain and logistics issues, changes in markets, demand, global economic conditions and other risks and uncertainties listed in Western Digital Corporation’s most recent quarterly and annual reports filed with the Securities and Exchange Commission, to which your attention is directed. Readers are cautioned not to place undue reliance on these forward-looking statements and we undertake no obligation to update these forward-looking statements to reflect subsequent events or circumstances.

 

Syeda Beenish

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