Redefining Data Processing With Edge Computing

Edge computing is advantageous for real-time data processing. Cloud computing is ideal when you have large amounts of less time-sensitive data.

Modern businesses mainly rely on IoT data, sensor data, and user-generated data from the most unexpected places. However, outdated centralized processors are ill-suited to dealing with the vast amounts of data generated from all the interconnected devices in the business sphere.

Therefore, IT leaders are addressing this challenge by turning to edge computing.

In this article, we’ll dive into the world of data processing with edge computing and explore its profound impact on various industries.

Statistics On Data Processing With Edge Computing

•  According to Gartner, 75% of all enterprise-generated data will be processed outside centralized data centers

•  Experts predict that edge data centers will grow from 250 in 2022 to 1200 in 2026

• The global value of edge computing will also surpass $317 billion by 2026

Advantages Of Data Processing With Edge Computing

Edge computing addresses critical infrastructure challenges, like bandwidth limitations and network congestion. However, other benefits of edge computing can be applied to different situations. 

Reduced latency

One of the biggest problems with cloud computing is that it often requires communication over vast distances. Multiple hops between switches to and from the cloud can cause latency delays, especially during high-volume data demands. Edge computing eliminates latency problems, boosting the performance of edge networks and devices.

Data sovereignty

As countries develop strategies to prevent exposure of critical data, Edge helps government IT leaders meet ethical data processing and storage responsibilities, mitigating risks to national security and maintaining the privacy of citizens.

Popular Applications Of Edge Computing

Data analytics is becoming more pervasive (and more intelligent, thanks to AI and ML) across all industries, driven by the demand for improved performance. Some of the popular applications of edge in data processing include the following:

1. Autonomous vehicles

As edge computing finds its way into road infrastructure, more autonomous driving applications leverage V2X (Vehicle-To-Everything) communications to enable autonomous vehicles to make quicker and more accurate decisions. 

This technology can also reduce the vehicle's high energy requirements by relocating some onboard computing and sensing tasks to a grid of roadside devices with built-in real-time communication capability.

2. Energy management

Today’s energy sector creates exabytes of data during generation, transmission, and distribution, says Arnie de Castro, product manager at SAS. He adds that aggregating this data into a centralized cloud infrastructure requires substantial bandwidth. 

However, Edge computing can collect and organize all this information before transmitting it to the control center. Thus, the utility company can more easily identify outage areas for faster restoration.

3. Patient health monitoring

Health IoT is becoming popular both for home use and clinical settings and is beneficial in many ways including: 

• Monitoring and analyzing patients' condition in real-time from wearables such as fitness trackers and smartwatches

•  Conducting predictive maintenance and repair of medical equipment

•  Using augmented reality glasses (with edge rendering) to display the patient's history and complex treatment protocols

4. Manufacturing

Edge has become a game-changer in the Industrial Internet of Things (IIoT) and manufacturing. From robots scurrying around warehouses to cameras monitoring flaws in the assembly line. Here are other essential ways Edge is contributing to the industry.

•  Edge is applied in quality control automation settings such as packaging and canning to detect anomalies and other issues. 

• In warehouses, people handling functions like inventory management require Edge computing to make real-time optimal decisions.

•  Edge deployments use machine data in production line diagnostics to determine where the most moving parts in a manufacturing process will break down or require maintenance. 

Conclusion

Data processing with edge computing is redefining industries by reducing latency, enabling real-time decision-making, and enhancing the efficiency of systems. The future of the internet will likely consist of a hybrid between edge and cloud computing, merging the best qualities of both approaches.