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On the Edge of More Accessible IoT Innovation

Blog: The Tibco Blog

We’re all well-versed with the narrative that pits the cloud and edge computing against one another as competing entities.

Both have jostled for the top status as IT’s core disruptor and been positioned as a stark choice to make, depending on a business’s priorities and capabilities. Yet this ‘either/or’ conundrum is a myth worth dispelling; they are entirely different concepts. The edge — that physical space that draws computing and intelligence ever closer to the data source — becomes a delivery mechanism for the disconnected elements of the cloud. As such, they can work in synergy, rather than as replacements, making an effective hybrid that combines the edge’s agility with the sheer processing power of the central cloud. It’s why both environments are deployment options for the new breed of developers creating smarter, event-driven microservices for faster, more flexible app development.

While it was predicted that connectivity costs would reduce to such an extent that locating system intelligence in the central cloud would become the preferred option, these predictions have not come to fruition. Instead, we have seen the gradual migration of IoT-created data to the edge and with it, the natural progression of enhanced connectivity and functionality. Indeed, intelligence at the very edge of the network is not only more accessible but captured in real time, in its purest form and freshest state. This makes it most valuable for informing immediate and accurate operational decisions.

The benefits rumble on; with the compute happening directly on device, bottlenecks caused by multiple devices communicating back to a centralized core network are consigned to the past. Furthermore, security risks are minimized, with the time that data is spent in transit and vulnerable to attack significantly slashed. When analytics are added to the mix, things become more interesting, as subsets of data can be collated with analysis localized for enhanced decision making.

While the case for the edge has always been compelling, not everything has traditionally thrived there. Today’s machine learning algorithms, for example, and their requirement for huge volumes of data and computing power, have long relied on the cloud to do the heavy lifting. Yet as artificial intelligence becomes a more mainstream reality, informing daily routines from smart cars to digital personal assistants, things have changed. Attention has now turned to how it can better deliver on the network periphery and the space closer to the mobile phones, computers and other devices where the applications which commonly harness this technology run.

We have already seen the benefits play out in the smart home area. Here, deep learning capabilities at the edge of the network inform the nuances and intuitive responses of IoT-enabled digital tools that integrate and interact to provide insight as situations change. They can then feedback real-time contextual information to the homeowner or, if intruders appear, to a professional monitoring resource.

This was just the start; bringing machine learning capabilities to the edge, on device, with no connectivity requirements, and simplifying the long-standing IoT integration challenges, has wider ramifications for a host of industries and applications beyond the consumer space. Solutions that can respond to events in milliseconds represent the most cutting edge of innovation in this space, unlocking ever greater value in territories as diverse as industrial settings to the medical sector.

Here, real-time information at its most accessible will drive intelligent diagnostic capabilities on medical devices and harness machine learning to make all manner of predictions such as the patients most at risk from a hospital infection or most likely to be readmitted after discharge. At this stage, we are not privy to the full potential of AI’s role in this environment. However, a future where a medical facility can offer patients the option of receiving online medical advice from an artificial intelligence software programme is on the horizon, and promises improvements from speed and efficiency, to patient care and cost savings. Equally, strides are being made in the context of industrial settings, where data must flow between a myriad of sensors, devices, assets and machinery in the field, often in unstructured or challenging and remote conditions. Detecting anomalies at the edge device offers the kind of agility needed for predictive monitoring and mission-critical decisions with the potential to save millions, in terms of addressing equipment failures before they do damage.

Crucially, open source projects that simplify the development and deployment of microservices and IoT applications become the bedrock of this innovation. By enabling autonomous device action for a smarter edge and an era of more accessible IoT development, the potential is limitless.

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