Edge computing & mobile edge computing (MEC)

Examples, use cases, applications, benefits for businesses

Decisions in milliseconds, local data security, and faster processes where the action happens: that's what edge computing is all about. But what does this mean in practice for businesses? Thierry Van Nuffelen, Cloud Expert at Proximus NXT, explains several real-world use cases, highlights the benefits for organizations, and clearly outlines the differences between edge computing and the cloud.

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What is edge computing?
What is mobile edge computing (MEC)?
Examples of edge computing: use cases
The benefits of edge computing
Edge computing versus cloud: a comparison
Edge, cloud, or a hybrid model?
How to implement edge computing?
Data security and GDPR with edge computing
How does Proximus NXT support businesses?
Why Proximus NXT for edge computing?

What is edge computing?

Edge computing is a distributed computing model in which data processing takes place as close as possible to the source of the data, such as on sensors and machines. The goal is to bring computing capacity and application responsiveness closer to end-users. This minimizes response time (latency) and bandwidth usage, which is essential for real-time, low-latency applications such as edge AI and industrial IoT over private 5G networks in Belgium. In short, edge computing delivers faster response times, improved cybersecurity, and enhanced data privacy.

Because data is processed directly at the source, real-time decisions can be made with extremely fast response times.

For companies, this local data processing translates into faster actions and stronger cybersecurity, while sensitive data remains on the premises.

A major advantage lies in direct on-premises data analysis, allowing decisions to be made within milliseconds, which is critical for processes that cannot tolerate delays.

Combined with private 5G networks, edge computing for businesses is transforming industries through fast, stable, and secure connections for smart applications, including edge AI in Belgian companies.

"Applications running centrally in data centers require all transactions to be processed there before a response is sent back to the user. For many applications, such as autonomous driving or real-time monitoring, this delay is unacceptable. Edge computing provides an extremely fast response time (low latency) by processing data locally, allowing critical decisions to be made at lightning speed."

Thierry Van Nuffelen, Product Manager Cloud (Proximus NXT)

What is mobile edge computing (MEC)?

A specific and powerful form of edge computing is mobile edge computing (MEC), also known as multi-access edge computing. In this model, computing power is located not at a fixed business site, but at the “edge” of the mobile network itself, for example at 5G antennas.

MEC is specifically designed to serve mobile devices, from smartphones to connected vehicles and mobile IoT sensors, with extremely low latency. Because data processing takes place within the 5G network, response times are drastically reduced. This capability enables advanced real-time applications such as augmented reality, smart traffic systems, and live video analysis on the move.

Examples of edge computing: use cases

Edge computing enables numerous innovative applications that rely on ultra-fast response times and local AI. Examples include the Internet of Things (IoT), edge AI, and real-time analytics running on private 5G networks. Many successful computing case studies demonstrate its potential.

Some concrete examples of applications include:

Production lines

Immediate defect detection through real-time image analysis, a key advantage of edge computing in manufacturing

Logistics

Real-time tracking and management of goods without delay

Retail

Instant analysis of customer behavior to improve the shopping experience

Healthcare

Rapid interaction with AI-powered medical systems for an initial diagnosis

Benefits of edge computing

The benefits of edge computing are clear: faster decision-making and significant improvements in both cybersecurity and on-premises data privacy. Direct on-premises data analysis enables decisions to be made within milliseconds, which is essential for processes that cannot tolerate delays. Combined with private 5G networks, edge computing is transforming industries by providing stable and secure connections for smart applications, including edge AI in Belgian companies.

Faster decisions and greater control

the technology enables direct analyses with significantly lower latency, allowing immediate real-time action.

Perfect for new applications

edge computing provides the ideal foundation for industrial IoT, AI applications, and other use cases that rely on fast 5G connectivity. This is how edge computing enhances real-time analytics.

Improved cybersecurity and privacy

sensitive information remains securely on-site. This is a key aspect of edge computing cybersecurity and significantly strengthens data privacy in Belgium.

5G networks are significantly accelerating the adoption of edge computing. The high speeds and low latency of 5G make it possible to deliver data to edge applications even faster, enabling edge networks to respond to events almost instantly. Together with private 5G networks, edge computing is transforming industries by delivering fast, stable, and secure connectivity for smart applications such as edge AI. An effective edge computing and private 5G strategy can help businesses begin this transformation.

"Edge computing is not a replacement for the cloud, but a complement to it. Large data streams and complex AI model training often continue to take place in central data centers. Edge computing focuses on immediate response and local processing closer to the user, while collected data can later be used in the backend for deeper analysis. The real power lies in the collaboration between edge and cloud."

Thierry Van Nuffelen, Product Manager Cloud (Proximus NXT)

Edge computing versus cloud: a comparison

The question is not “edge computing vs cloud”, but rather: how can both be combined within an edge cloud architecture? Both technologies have unique strengths and complement each other perfectly in a hybrid model.

The best approach is to divide their roles as follows:

Characteristic Edge computing Cloud computing
Data processing Local, close to the data source (e.g. on a machine or sensor). Centralized, in large remote data centers
Latency (delay) Very low (a few milliseconds), ideal for real-time actions. Higher, depending on the distance to the data center and network quality
Bandwidth usage Low, because only relevant results or summaries are transmitted. High, because large volumes of raw data often need to be sent to the cloud
Connectivity Can operate autonomously, even with a poor or no Internet connection. Requires a stable and continuous Internet connection
Security Sensitive data remains on-site, improving privacy. However, each individual edge device must be secured. Centralized and robust security, but data leaves the physical location
Ideal for Industrial IoT, real-time quality control, augmented reality, autonomous vehicles. Big data analysis, large-scale data storage, website hosting, and business software (ERP/CRM)

Edge, cloud, or a hybrid model?

Cloud computing

Perfect for storing large volumes of data and performing analyses that are not required immediately. For general IT tasks, the cloud is often the most flexible and cost-effective solution.

Edge computing

Ideal for tasks that require an immediate response, such as controlling robots or analyzing live video footage. Its key strength is its extremely fast response time.

Hybrid model

The most effective approach is a hybrid model that combines both technologies within an edge cloud architecture. Data requiring immediate processing remains at the “edge”, while the remaining data is sent to the cloud for storage and deeper analysis.

The benefits of edge computing for real-time analytics

The benefits of edge computing for real-time analytics in Belgium are immediately apparent and support a wide range of use cases. These are the main advantages for businesses:

Minimal delay

Because data is analyzed on-site, decisions can be made within milliseconds. This is indispensable for processes where delays are unacceptable. Reducing latency with edge computing for IoT is a major advantage.

Higher reliability

Local systems continue operating even if the Internet connection to the cloud fails. This ensures your business continuity at all times.

Better privacy and security:

Sensitive data remains within the company. This is a key principle of edge computing data privacy. It strengthens control over company data and supports compliance with GDPR requirements in Belgium.

Lower data traffic costs

Less data needs to be transmitted to the cloud, reducing bandwidth usage and lowering operational costs.

How to implement edge computing?

Do you want to integrate edge computing into your existing IT infrastructure? This can be achieved through a clear step-by-step plan that provides insight into how to implement edge computing for real-time analytics.

The following steps help ensure a successful and controlled edge computing deployment:

"The biggest challenge lies in the way applications need to be developed. The traditional, centralized approach is no longer sufficient. Applications must be distributed across the edge network while still delivering a consistent user experience. This requires a different approach to application development, where feedback from the edge is immediately translated into actions, without losing sight of the bigger picture."

Thierry Van Nuffelen, Product Manager Cloud (Proximus NXT)

Data security and GDPR with edge computing

Edge computing introduces both new challenges and unique advantages in terms of cybersecurity and data privacy. Because data is processed locally, the potential attack surface increases, as each edge device can become a target. At the same time, edge computing creates unprecedented opportunities to better protect sensitive information. Understanding edge computing security risks is the first step.

Benefit

With edge computing, sensitive data remains at the location where it is generated and processed. This means less data is transmitted over public networks or stored externally. As a result, the risk of data breaches during transmission is significantly reduced, while compliance with the GDPR (General Data Protection Regulation) becomes easier. This helps companies, particularly in Belgium, comply with principles such as “privacy by design” and data minimization.

Point of attention

Each edge device or local server represents a potential entry point. A robust approach to securing data on edge devices in Belgium is therefore essential. This requires a layered security strategy that extends beyond the data center alone. Proximus NXT addresses these security risks through a layered approach: from end-to-end encryption and network segmentation (e.g. via private 5G networks) to physical hardware protection and continuous monitoring. Our edge computing infrastructure security protocols in Belgium are designed to provide optimal protection for your data and operations.

"As Proximus NXT, we position ourselves as a platform provider. Our goal is not necessarily to build specific edge applications, but rather to deliver the architecture and network that enable flexible application delivery to end users. We already have experience in this area, for example with road charging projects developed in collaboration with application partners, where we provided the platform."

Thierry Van Nuffelen, Product Manager Cloud (Proximus NXT)

Why choose the expertise of Proximus NXT for your edge computing project?

When you choose Proximus NXT as your edge computing partner, you benefit from expertise that brings applications and computing power closer to your users. We are among the leading edge computing solution providers, combining our expertise in connectivity, such as private 5G, with powerful local processing capabilities. The result is faster processes, improved security, and complete control over your data.

Integrated end-to-end solution

We support Belgian companies through the entire process: from strategic advice and design to implementation, management, and support. You benefit from a single point of contact for your complete edge computing infrastructure in Belgium.

Combined expertise and connectivity

As specialists in connectivity solutions (fiber, private 5G networks, LoRaWAN) and ICT integration, we combine the best network infrastructure with the right computing capacity. This guarantees seamless and secure data transfer for all your edge applications.

Local expertise and support

Unlike pure hyperscalers, we understand the specific requirements and regulations of the Belgian market. Our local teams provide personal support and ensure seamless integration of edge computing with existing ERP systems and cloud strategies.

Future-proof and scalable solutions

We deliver secure, scalable, and future-oriented edge solutions tailored to your company's objectives, from pilot projects to full-scale deployments.

"With edge components, companies can run complex services through local data processing exactly where they need it. The benefits of edge computing for real-time analytics are therefore becoming tangible across more and more sectors."

Thierry Van Nuffelen, Product Manager Cloud (Proximus NXT)

Frequently asked questions

The combination of edge computing and private 5G networks has a major impact on industry. Enabled by the rollout of 5G in Belgium, this combination offers significant advantages for edge computing and private 5G implementation:

  • Highly reliable connectivity: A secure private 5G network provides a stable, fast, and secure wireless connection within a defined area, such as a factory or port.
  • The ideal foundation for edge: This robust connectivity forms the ideal basis for deploying 5G private network edge solutions, enabling direct and wireless data processing.

By connecting edge servers to a private 5G network, businesses can deploy various low-latency applications throughout Belgium.

Examples include:

  • Self-driving vehicles in warehouses or port areas.
  • Predictive maintenance through real-time analysis of sensor data.
  • Live quality control using high-resolution cameras on production lines.

This powerful combination is particularly effective in the manufacturing sector, where it drives major innovation and efficiency gains.

Yes, but not as a replacement for the cloud. Instead, edge computing is becoming an essential complement to it. The future of data management and AI in Belgium is undeniably hybrid. The debate between edge and cloud computing is not a competition, but a strategic partnership in which both technologies fulfil distinct and complementary roles.

  • The role of the cloud: The cloud remains the backbone for large-scale storage, central administration, training complex AI models (cloud AI), and performing extensive analyses that are not immediately time-sensitive.
  • The role of edge computing: Edge computing becomes the engine for real-time actions, immediate insights, and autonomous processes, directly at the data source. This makes the benefits of edge computing for real-time analytics tangible in Belgium.

As industrial IoT and edge AI continue to evolve and the demand for low-latency applications increases, the combination of edge and cloud becomes increasingly important. Edge computing is a fundamental component of future digital infrastructure and is critical for companies that want to compete on speed, efficiency, and intelligence within the Belgian market. Market figures confirm this strategic importance, predicting exponential growth for the global edge computing market.

Edge computing can reduce the overall environmental footprint of data processing. By analyzing and filtering data locally, less data needs to be sent to energy-intensive cloud data centers. This reduces bandwidth usage and transmission-related energy, helping Belgian companies achieve their sustainability objectives while addressing concerns related to edge computing energy consumption.

Absolutely. Integrating edge computing with existing ERP systems can lead to major improvements. Edge computing processes operational data locally in real time (e.g. machine performance or inventory levels), generates immediate insights, and sends only relevant, summarized information to the ERP system. This results in more accurate and up-to-date information for planning, inventory management, and decision-making.

Local data processing through edge computing improves data privacy because sensitive information does not need to leave the physical business location (and therefore remains within the Belgian jurisdiction) for primary analysis. This minimizes the risk of data breaches during transmission over external networks and simplifies compliance with GDPR requirements for Belgian organizations.

The combination of edge computing and artificial intelligence (AI) is known as edge AI. In this model, the complex calculations and decision-making processes of AI models take place at the “edge”, close to the data source itself, rather than in a central cloud environment. The distinction between edge AI and cloud AI primarily concerns location and latency. Edge AI enables devices to learn from and respond to new data immediately, without delay. Examples include inspections in factories where cameras instantly detect defects and stop production lines, or smart retail cameras that anonymously analyze customer behavior and adapt the shopping experience in real time. Because processing takes place locally, analyses are not only faster, but also improve data privacy and cybersecurity, as sensitive information does not need to leave the local environment.

How does edge computing enable local AI? By running AI models locally, decisions related to quality control can be made immediately, enabling efficient on-premises data analysis without first transmitting data to the cloud.

Edge cloud is a concept that extends the capabilities of cloud services to the edge of the network. While edge computing focuses on local data processing, edge cloud expands this model by physically bringing cloud functionalities, such as compute, storage, and networking, closer to end-users or data sources. This is not a replacement for traditional cloud infrastructure, but rather a strategic extension of it within a hybrid architecture. Edge cloud combines the scalability and flexibility of cloud services with the low latency and bandwidth efficiency required for critical applications such as real-time on-premises data analysis, advanced industrial IoT solutions, and private 5G networks.

Four years ago, “edge” primarily referred to endpoint devices, such as laptops and connected devices interacting with the cloud. Today, edge computing also includes the “mist” layer: contact points where edge devices converge, located closer to users than traditional data centers.