The 3 Internet of Things Advantages Driving Edge Computing
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The rise of edge computing shouldn’t come as a major surprise to experienced tech leaders. Not because edge computing isn’t a significant step forward, but because we’ve seen this kind of technological evolution before.

Looking back, it seems like it was inevitable that the internet would evolve. What started as a small set of connected mainframes became the vast landscape of feature-rich PCs. And in the same way, it’s inevitable that the Internet of Things evolves beyond simple sensors toward powerful edge computing.

But what, exactly, is driving the need to give IoT devices power to do more than just collect data and transmit it back to the core network over the cloud?

Unlocking the true value of the IoT means capitalizing on these three key advantages—and we can only access these advantages by pushing computing to the edge.

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1. Maximize Cost Efficiency of Connectivity

There are more than 20 billion connected devices in the world today. And as businesses continue to embrace the IoT in the coming years, the volume of connected devices will grow exponentially.

Without edge computing, these “dumb” devices would simply collect massive volumes of data and stream it to the core network, draining your resources and eliminating any value you hoped to get out of the IoT.

Achieving connectivity cost efficiency means limiting the need to interact with cloud data centers by filtering and analyzing data within IoT devices. Doing so ensures that devices aren’t consuming unnecessary resources both in the field (when connected via LTE) or in factory settings (when utilizing network bandwidth).

2. Reduce Latency, Speed Up Processing

It may not sound like much, but every millisecond counts when you’re working with IoT devices. Cutting out the time needed to move data from devices to the cloud and back opens up a new world of real-time capabilities.

Specifically, tasks that rely on machine learning can’t provide maximum value without on-device computing power. Capabilities like facial recognition, language processing, obstacle avoidance, and more all benefit from minimal latency.

You’ll still want to run complex data models on the core network, but more and more decisions are made in real time, making low-latency edge computing critical for IoT ROI.

3. Secure Data Processing and Analytics

While business leaders see the value of IoT devices for data collection and analysis, security pros know that connecting more devices to the network creates a larger threat landscape. However, the challenge of data privacy and protection can be solved by moving from device-to-cloud communications to on-device edge computing.

Consider, for example, a healthcare IoT device that collects data about a patient’s vitals. Transmitting protected health information (PHI) over the cloud can cause security and compliance problems. But if you can keep the data contained to the edge device, it can remain anonymous during analysis and deliver insights without sacrificing security.

Whether it’s cameras, microphones, sensors, or machine learning-powered systems, computing algorithms at the edge allows you to transmit data that’s protected, rather than transmitting raw streams through the cloud.

Simplifying Your Move to Edge Computing

Gateway sensors, micro data centers, cloudlets, fog fabric nodes—all of these technologies are facilitating the rise of edge computing.

With so much to think about when planning your digital transformation, trying to figure out the new world of edge computing might feel like the lowest priority. And yet, edge computing will play a critical role in digital transformation success.

That’s why we’ve put together this white paper, Demystifying the Edge: Making Sense of Cloud Computing, Fog Computing, and More. Download it today to cut through the edge computing noise and unlock the real value of the IoT.

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