Data is everywhere – businesses have more access to data than ever before, especially with the use of IoT devices.
But data isn’t valuable on its own. It’s a means of gaining insights and information to make better decisions, so it’s only as good as the speed of computing devices that manage it.
No matter how large or powerful, all computing devices are limited by the speed of light through electron motion. Even as advancements happen, computing power will hit its finite limit. Growing data processing needs mean businesses need a strong foundation of computing power that can manage, route, and process data effectively.
The Finite Limit for Computing Devices
Memory density and processing power govern the limitations of computing devices. It all comes down to how quickly they can move information, and how quickly that information can be processed and used once it’s transferred.
Electronics, like computers, work by shifting electrons around. At the basic level, all computing devices are limited by the physical restrictions of an electron moving through matter. Information does move faster than electrons, however, which represents a loophole.
The wiring in electronics is packed with electrons. Signals travel through wires at the speed of light in metal, and when the information processor is turned on, they control the current and act as gatekeepers for electronic signals.
Clock speed is measured in megahertz (MHz), one million ticks per second, or gigahertz (GHz), one billion ticks per second. These units affect how fast a computer’s processing speed is, but it’s still governed by the length the ticks have to travel and the speed of light.
As a result, computing speed can be improved by making the device smaller. Signals aren’t able to move faster than the speed of light, but they can travel smaller distances. Smaller computing devices have smaller components to accomplish this.
But – computing devices can only be made so small. They must accommodate the quantum tunneling of electrons and the size necessary to do that, which is why we began using connected NXP i.MX 6 single board computers (SBC) – supercomputers – to handle the computational needs.
Limitations to Data Processing and Analytics
As discussed, computing power is limited by the device and its principles to start, then, it’s limited by the data processing and analytics. IoT devices are useful for their ability to amass large quantities of valuable data, but that has little value if it can’t be processed and analyzed.
Because of this, businesses have found new and innovative ways to address their computing needs. The cloud-based data center and conventional internet network aren’t satisfying the needs for high volumes of data.
Data needs to be transferred from devices to the cloud storage center. When high volumes of data are collected and transferred from IoT devices, the network is impacted by congestion, disruptions, and latency that affect the transfer and availability of data. In addition, the data also needs to be processed and analyzed, then the actions need to be applied in real time. When this entire process is slowed down, the insights from the data may be irrelevant by the time they can be acted upon.
How Edge Computing Improves Processing Power
Edge computing is an excellent solution to data processing and analysis. Rather than data being transferred to the cloud-based core, analyzed, then sent back to the device, edge computing does all the work near the device itself. Only the valuable insights are sent to the storage center, and the mission-critical tasks can be handled at the network’s edge.
This is beneficial for IoT in many industries, including the automotive industry with autonomous vehicles, which need to make rapid-fire decisions to adapt to changing conditions. Many industries are leveraging IoT to gather data in places that are too remote or dangerous for human employees as well. Edge computing ensures that information and decision-making isn’t compromised without an employee on site.
Edge Computing and Cloud Computing – The Ideal Combination
Edge computing and cloud computing use distributed computing power and data processing, but they reside in different locations.
As the name indicates, edge computing occurs close to the device and processes, filters, and analyzes data, choosing to send only the most valuable and essential data back to the cloud core for human intervention. This minimizes the volume and traffic on the network, as well as allowing for rapid decisions when necessary.
By contrast, cloud computing is a highly scalable solution that’s a mainstay for IoT deployment. Cloud storage centers are often used in industrial settings, but may be hundreds of miles from the source of data. This can lead to delays in data transfers and processing.
Cloud computing is often attractively packaged with all necessary services for businesses, however. Edge computing is decentralized and minimizes latency and improves performance, but it has higher maintenance and control needs.
Together with technologies like i.MX 8 single board computers (SBC), these two technologies can handle the workload and relieve the pressure on the network, ensuring the system is as efficient and cost-effective as possible.
Top Use Cases
Network Optimization
Edge computing can be used to monitor network conditions and performance, routing network traffic the best possible way. Similar to the way traffic light networks work in heavily populated urban areas, this can direct, reroute, and redirect network traffic to make sure critical information gets through and no network pathway becomes overloaded.
Transportation
Autonomous vehicles are one of the best-known use cases for IoT, and they’re also an example of how edge computing can leverage the potential of IoT. Autonomous vehicles need to think and act like human drivers, and to do that, they need to process the volume of information that human drivers do in an instant. This may include traffic conditions, road conditions, weather, location, predictable traffic patterns, and more. All of this needs to be analyzed in real time for the vehicle to make the best decision, and network delays could be a literal case of life or death.
Retail
Edge computing and IoT offers huge potential in retail environments. These businesses can analyze the data that comes from surveillance, sales information, and other sources, finding opportunities to improve customer experience or adapt to market changes. This may include optimizing inventory or launching a flash sale.
Healthcare
IoT has been used in healthcare for decades, but it’s seeing new applications with innovations like robotic surgery, telemedicine, and medical wearables. All of these technologies collect data and require immediate action on the insights. Like autonomous vehicles, real-time insights and actions have a life-or-death impact, especially for automated or AI uses.
Edge Computing as a Foundation for IoT
IoT brought a wealth of data to businesses, but the high volumes revealed inefficiencies in current systems and processes. Data’s value lies in the insights it provides, and edge computing can ensure that businesses can use their data to its fullest.
