By Jason Khoo, SolidRun
Once an emerging technology, IoT devices are becoming more commonplace in industrial and consumer applications alike. One of the primary benefits of IoT is the incredible volume of data that can be collected to glean vital business insights, but that data is virtually useless without sufficient computing power.
All computing devices have inherent limitations as a result of the motion of electrons as a function of the speed of light. Devices and components can get smaller, but there’s a limit to how small any one component can be.
As data processing demands grow and evolve, businesses will need solutions to computing power to manage, route, and process data for useful insights.
Finite Capability Of Computing Devices
All computing devices are constrained by processing power and memory density, which determine the speed of transmission and processing of information.
Electronics compute by the flow of electrons, so they’re only capable of computing as quickly as an electron can move through matter. Information can be transmitted faster than electrons can move, however, and electronic wiring is already packed with electrons.
When information processing transistorized switches are powered on, they regulate the signals in the wires. Electrons need to move from one side of the transistor to the other for processing. The clock speed of a computing device, measured in megahertz or gigahertz, estimates how quickly a computer can process information, which is influenced by the distance and speed of light.
This distance can be minimized, allowing signals to travel shorter distances and address these limitations. Computing components can’t be infinitely minimized, however, which is why networks of supercomputers are often used to handle high volumes of data.
Supercomputers aren’t ideal for all applications. With IoT devices in place, businesses need practical ways to process high volumes of data and gain important insights from it. Centralized data storage centers powered by the cloud – the gold standard for computing – aren’t a viable solution any longer.
When data has to travel from the edge of the network, where the devices are located, to a centralized data store, it can lead to latency, network congestions, security vulnerabilities, and other concerns.
Edge Computing As A Data-Processing Solution
Edge computing has emerged as a viable option for data collection and processing in IoT devices. The devices are located on the edge of the network, and edge computing applies processing power at the source.
So, instead of data having to travel to the centralized data core – sometimes miles from the device itself – it can be processed and analyzed on the edge. Only the most valuable insights are sent back to the core for human evaluation and decision making.
This allows IoT devices in remote environments to operate and provide data insights that can be accessed and acted upon in real-time for mission-critical operations.
Benefits And Limitations Of Edge Computing Vs. Cloud Computing
Cloud computing has been favorable for data storage and processing. The centralized system is large, scalable, and distributed, making it a good choice for IoT deployments. It’s also included in packages from cloud providers, so it’s quick and easy for businesses to set it up.
The cloud-computing core may be hundreds of miles from the IoT device, however, causing delays in data transfers that can compromise the timeliness and value of the insights. If data can’t be used for real-time actions, there’s little benefit in getting it quickly.
Edge computing addresses these issues by staying close to the data source and minimizing the distance data needs to travel. Data can be processed and analyzed near the IoT device, filtering raw data so only the most valuable insights are sent back to the core for human interactions.
With a combination of edge computing, i.MX 8 single board computers (SBC), and cloud computing, businesses can ensure the most efficient and insightful data processing capabilities in a distributed network for real-time insights with minimal latency and congestion.
Edge Computing Use Cases
Healthcare: IoT is revolutionizing healthcare. Medical devices and sensors collect valuable data on patients and provide immediate insights to help physicians make critical, life-saving decisions. With so much at stake, edge computing is important for real-time data processing.
Transportation: Semi-autonomous and autonomous vehicles are making their way into the mainstream, but they rely on data insights from the vehicle itself, the area, the traffic conditions, the weather conditions, and other environmental information to make critical decisions. For autonomous vehicles to function properly, the data must be analyzed promptly without network delays.
Network Optimization: One of the significant challenges with cloud computing is the strain on the network from latency and congestion as huge volumes of data are transferred and processed. Edge computing, such as i.MX 6, can optimize network performance by monitoring user conditions and determining the best path for network traffic, similar to the way road traffic patterns are prioritized and optimized to limit traffic jams.
Retail: Data is valuable for retailers to help with sales, customer information, and other vital business intelligence. Edge computing allows businesses to analyze the data from diverse sources efficiently, giving business owners the insights necessary to adapt to changing conditions.
Computing Power For IoT Data Processing
IoT holds promise for industrial uses, but the massive amounts of data it provides can burden computing devices and limit insights. Raw data doesn’t offer anything without analysis, and edge computing ensures that data can be processed close to the source for rapid, valuable insights that fuel business decisions.
Author The Author
Jason Khoo 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.