Businesses have a treasure trove of data at their disposal, but it’s only useful if it can be processed for actionable insights. Traditional computing methods have limitations that inhibit the processing of high volumes of data efficiently, rendering the data collected useless.
Computing Devices and Data
Data plays a major role in modern business, offering in-depth information and insights that allow business owners to take real-time action over mission-critical processes. Sensors and IoT devices operate in real time, often in remote or dangerous environments without human interaction, collecting data that may not be available otherwise.
There is a downside to the high volumes of data, however. Traditional computing isn’t capable of handling data processing from IoT devices as efficiently as possible. There are limits to how quickly any computer, no matter the processing speed, can process data.
Cloud computing cores, such as the NXP i.MX 8 single board computer, may be hundreds of miles from the source of the data and require a transfer from the device to the cloud. Often, these networks suffer from bandwidth limitations, latency, security breaches, and unpredictable obstacles that can slow those necessary insights.
Edge Computing for Real-Time Data Processing
With the number of internet-connected devices, the sheer volume of data, and the distribution of cloud storage centers, the demand for computing capability is rapidly outweighing what cloud computing can handle – not to mention the drain on the global internet from all that traffic.
One of the main benefits of IoT is that it can collect data without human intervention, so these devices can’t be moved closer to the cloud centers. The solution is to then move the data center closer to the source of the data – thus creating the concept behind edge computing.
One of the ways modern businesses can address the challenges of data processing is with edge computing. Though it may be implemented a number of ways, edge computing essentially moves the data processing and analytics closer to its source.
So, instead of raw data traveling back and forth from the device to the cloud for processing, then back to the device, it can be analyzed at the edge. Depending on the industry, this could be a remote oil rig, a retail location in another city, or an autonomous vehicle on the road. Only real-time insights go through, ensuring that the network isn’t clogged with low-value data.
Edge computing arose as a distributed solution to what is ultimately a distributed system. Instead of trying to manage device data centrally, edge computing can keep the processing on the edge of the network, complementing the capabilities and advantages of cloud-based storage centers. It’s not just about moving high volumes of data, but transferring and processing it quickly enough to provide rapid insights. With the cloud, time-sensitive data could be outdated and irrelevant by the time it travels to the central core and back to the device.
AI-Enabled IoT Devices
Like IoT, edge computing is continually evolving to improve performance and capability. As more industrial IoT networks adopt edge computing, its services are likely to become more available and contribute to more use cases.
With the addition of artificial intelligence (AI), edge computing can unlock the potential of IoT devices. Known as “edge intelligence,” edge computing for IoT devices with AI allow the devices to make mission-critical decisions in real time.
AI relies heavily on data transmission and computing power with complex machine learning algorithms. Edge computing brings AI and machine learning to the data’s source for computing, allowing faster computing and insights, better control over operations, and improved data security.
An efficient AI-enabled IoT network with edge computing is optimized on a NXP i.MX 6 board to handle more complex and high-volume data and workloads, both on the edge and near it. Combined with cloud-based storage solutions, this combination can provide never-before-seen performance and virtually limitless scalability with the opportunity for businesses to use their data to its fullest potential.
Some businesses are already using edge computing with AI-enabled IoT devices, but that’s likely to ramp up following the widespread adoption of 5G technology. This next-generation wireless technology can deliver higher multi-Gbps peak data speeds, better reliability, massive network capacity, and ultra low latency, improving the overall performance of the network.
All of these technologies make up for each other’s shortcomings and provide the most efficient, large-scale, and intelligent network options.
Computing Performance for Optimal Data Processing
Data is essential for businesses, and more than ever before, IoT devices allow businesses to collect larger volumes of data to inform operations and processes. In its raw form, data is only as good as what can be done with it, however. Traditional computing is limited with processing large volumes of data for real-time, actionable insights, but edge computing can bridge the gap by providing rapid data processing and insights close to the source.
Author Bio: Jason Khoo
Jason is the Head of SEM at SolidRun which is a global leading developer of embedded systems and network solutions, focused on a wide range of energy-efficient, powerful and flexible products which help OEMs around the world simplify application development while overcoming deployment challenges