Edge Computing

What Is Edge Computing? Everything You Need to Know

Edge computing is the latest in computing. It is changing how we view and process data.

Data shows that edge computing is becoming more popular. Many people still have questions about it. What is edge computing? How does it work? Why is it important? How does it compare to cloud computing?

This article will explain how edge computing works and why it matters.

What is Edge Computing?

Edge computing processes data closer to the source, minimising latency and reducing the need for a direct connection to cloud services. Enterprise applications are positioned near data sources like local edge servers and IoT devices. This setup allows organisations to gain insights faster and speed up response times.

How Does Edge Computing Work?

Edge computing is about location. In traditional enterprise computing, data is produced at a client endpoint, like a user’s computer. This data travels across a WAN and through the corporate LAN to be stored and processed by an enterprise application. The results are then sent back to the client.

This approach works for many business applications but struggles with the growing number of internet-connected devices and the data they produce. Gartner predicts that by 2025, 75% of enterprise data will be created outside centralised data centres. Moving such large amounts of data stresses the global internet, which can be congested and disrupted.

IT architects now focus on moving storage and computing resources to the data’s location. If data can’t be brought closer to the data centre, bring the data centre closer to the data. This concept, known as edge computing, involves placing storage and servers at the data source. Often, only a partial gear rack is needed to process data locally on the remote LAN.

Edge computing devices are often housed in shielded enclosures to protect them from environmental conditions. They process and analyse data streams locally, sending only the results back to the main data centre.

Business intelligence from edge computing can vary. For example, retail environments might use video surveillance and sales data to understand product demand. Predictive analytics can guide equipment maintenance and repair before failures occur. Utilities use edge computing to ensure proper equipment function and maintain output quality.

Why Is Edge Computing Important?

Edge computing supports distributed computing by placing compute and storage resources closer to the data source. Distributed computing models aren’t new; remote offices, data centres, and cloud computing have been around for a while.

Decentralisation can be challenging. It requires a fast and responsive network. Edge computing solves three main network issues: bandwidth, latency, and congestion.


Bandwidth is the data a network can carry over time, usually in bits per second. All networks have bandwidth limits, especially wireless ones. Increasing bandwidth is expensive and doesn’t fix all issues.


Latency is the time to send data between two points on a network. Large distances and network congestion can delay data movement, affecting real-time responses. For self-driving cars, this delay can be critical.


Congestion happens when too much data overwhelms the internet, causing delays and requiring data retransmissions. Network outages can worsen congestion and cut off communication, rendering the Internet of Things useless during these times.

Difference Between Edge Computing, Cloud Computing and Fog Computing

Edge computing is linked to cloud and fog computing. While they are related, they are not the same and should not be used interchangeably. Edge, cloud, and fog computing all involve distributed computing. They focus on where compute and storage resources are located in relation to data production. The key difference lies in the location of these resources.


Edge computing involves placing computing and storage resources where data is produced. This setup puts compute and storage at the same point as the data source at the network edge. For example, several servers and some storage might be installed on a wind turbine to collect and process data from sensors within the turbine.

Similarly, a railway station might have some computing and storage to handle track and rail traffic sensor data. The processed results can then be sent to another data centre for review, archiving, and broader analytics.


Cloud computing is a large, scalable deployment of compute and storage resources at distributed global locations. Cloud providers offer pre-packaged services for IoT operations, making the cloud a preferred platform for IoT deployments. Cloud computing has enough resources and services for complex analytics, but the nearest regional cloud facility can still be far from the data collection point.

Connections depend on the same internet connectivity as traditional data centres. In practice, cloud computing is an alternative or complement to traditional data centres. It can bring centralised computing closer to a data source but not at the network edge.


The choice of computing and storage deployment isn’t limited to the cloud or the edge. A cloud data centre might be too far away, while edge deployment might be too resource-limited or scattered. In this case, fog computing can help. Fog computing places computing and storage resources near the data but not necessarily at the data.

Fog computing can handle vast amounts of sensor or IoT data generated across large areas. Examples include smart buildings, smart cities, or smart utility grids. Fog computing and edge computing have similar definitions and architectures. The terms are sometimes used interchangeably, even among experts.

Benefits of Edge Computing

Edge computing tackles key infrastructure issues like bandwidth limitations, excess latency, and network congestion. It also offers other benefits that make it useful in different scenarios.


Edge computing is useful where connectivity is unreliable, or bandwidth is limited due to environmental factors. Examples include ships at sea, oil rigs, remote farms, deserts, and rainforests. It processes data on-site, sometimes on the edge device itself, like water quality sensors on purifiers in remote villages.

This reduces the amount of data sent and saves bandwidth by transmitting only when connectivity is available. Edge devices include sensors, actuators, endpoints, and IoT gateways.

Data sovereignty

Moving large amounts of data isn’t just a technical issue. Crossing national and regional boundaries can lead to data security, privacy, and legal problems. Edge computing keeps data close to its source and within local laws.

This law dictates how data should be stored, processed, and shared. Processing raw data locally can secure sensitive information before sending it to the cloud or primary data centre in other regions.

Cost Sensitivity

Companies using edge computing need less bandwidth and spend less on cloud services. This results in lower operating costs and improved cost efficiency.

Privacy and Security

Edge computing offers greater security and privacy by keeping data at the Edge instead of centralised servers.

Edge devices usually store minimal data, making them less attractive targets for hackers. The decentralised nature of Edge computing makes it harder for hackers to access all records. With data spread across many devices and locations, attackers must target multiple points, increasing the difficulty of a successful attack.

Remote Areas

Edge computing is essential in remote areas, where connectivity is often limited or nonexistent. It allows data to be processed locally without relying on distant cloud servers. This reduces latency and ensures vital information is available quickly.

Devices like sensors and IoT gateways can function independently, making edge computing a reliable solution for remote environments. It helps in agriculture, mining, and environmental monitoring, providing real-time data processing and on-site decision-making.

network connectivity

Edge Computing Challenges and Opportunities

Although edge computing has the potential to provide compelling benefits across a multitude of use cases, the technology is far from foolproof. Beyond the traditional problems of network limitations, several key considerations can affect the adoption of edge computing:


Edge computing overcomes typical network limitations. However, even basic edge deployments need some connectivity. It’s important to design an edge deployment that works with poor or erratic connectivity and plan for what happens when connectivity is lost. Autonomy, AI, and graceful failure planning are essential for successful edge computing.

Unrealised Business Value at the Edge

Organisations often struggle to grasp the full business value of edge solutions. Companies should look beyond quick returns. Invest in desirable, feasible, and viable edge computing experiences for sustained ROI.

Data Lifecycle

The main problem with today’s data overload is that much of it is unnecessary. For instance, a medical monitoring device only needs to keep problem data. Normal patient data over days is not useful.

Most real-time analytics data is short-term and not stored long-term. Businesses have to choose which data to keep and what to discard after analysis. Retained data must be protected according to business and regulatory policies.

Lack of Cloud Talent

Edge is not about retooling, especially for companies using the cloud. It extends those capabilities to the edge. With existing cloud talent, you can deploy at the edge easily. The hardware connection is straightforward.

Security Challenges

IoT devices are often insecure. It’s crucial to design an edge computing deployment with strong device management, implement policy-driven configuration enforcement, ensure security in computing and storage resources, include software patching and updates, and focus on data encryption both at rest and in transit. Major cloud providers offer secure communications for IoT services. However, this isn’t automatic when building an edge site from scratch.

Edge Computing Use Cases and Examples

Edge computing techniques gather, filter, process, and analyse data close to its source. This approach is useful when data cannot be moved to a central location due to high costs, technological constraints, or compliance issues like data sovereignty. This concept has led to many practical examples and use cases.

1. Manufacturing

An industrial manufacturer used edge computing to monitor production, allowing real-time analytics and machine learning to detect errors and improve quality. They added environmental sensors throughout the plant to understand how each product component is assembled and stored and how long it remains in stock. Now, the manufacturer can make faster and more accurate decisions about the factory and operations.

2. Healthcare

In healthcare, edge computing is a distributed approach that collects vast amounts of patient data from devices, sensors, and medical equipment. This large volume of data needs edge computing to apply automation and machine learning. This helps to filter out normal data and identify problem data. Clinicians can then act quickly to help patients avoid health incidents in real-time.

3. Retail

Retail businesses generate large amounts of data from surveillance, stock tracking, and sales. These businesses benefit from data processing right at the edge of the network, enabling faster analysis and improved decision-making. Since retail stores can differ greatly, edge computing provides an effective solution for local processing.

4. Network Optimization

Edge computing improves network performance. It measures user performance across the internet and uses analytics to find the best low-latency path. This “steers” traffic for optimal performance.

5. Farming

Imagine a business that grows crops indoors without sunlight, soil, or pesticides. This process cuts growth times by over 60%. Sensors track water use and nutrient levels and determine the best harvest time. Data is collected and analysed to understand environmental effects. This helps improve crop-growing algorithms and ensures crops are harvested at their peak.

6. Transportation

Autonomous vehicles generate 5 TB to 20 TB of data daily. They collect information on location, speed, vehicle condition, road and traffic conditions, and other vehicles. This data must be processed in real time while the vehicle is moving. This requires powerful onboard computers, turning each vehicle into an “edge” device. The data also helps authorities and businesses manage vehicle fleets based on real conditions.

7. Workplace Safety

Edge computing helps businesses monitor workplace conditions and ensure safety protocols are followed by processing data from on-site cameras, employee safety devices, and sensors in real-time. This helps businesses monitor workplace conditions and ensure safety protocols are followed. It’s especially useful in remote or dangerous environments like construction sites or oil rigs.

Possibilities of Edge Computing, IoT and 5G

Edge computing is evolving with new technologies and practices that improve its performance. A significant trend is edge availability, with worldwide access expected by 2028. Currently, edge computing is often situation-specific, but it is projected to become more common and change internet usage.

This is evident in the increase in computing, storage, and network products designed for edge computing. More partnerships between vendors will enhance product compatibility and flexibility at the edge. An example is the collaboration between AWS and Verizon to improve edge connectivity.

Wireless technologies like 5G and Wi-Fi 6 will influence edge deployments and usage, enabling new virtualisation and automation capabilities. These technologies can improve vehicle autonomy and workload migrations to the edge while making wireless networks more adaptable and cost-effective.

Edge computing gained attention with the rise of IoT and the large amount of data such devices generate. As IoT technologies evolve, they will impact the future of edge computing. One future development is micro modular data centres (MMDCs), which are small, mobile data centres that can be deployed closer to the data source, improving computing efficiency without locating the edge directly at the data source.


Can edge computing work without the Internet?

Edge computing can work without internet access. It uses LAN connectivity to transmit and process data. The internet is only needed to send data to the cloud for storage and analysis.

What are the limitations of edge computing?

Edge computing has limited computing power compared to centralised cloud systems. This means it can only handle specific tasks. Deployment and maintenance can be complex and costly. Also, there is limited data storage capacity at the edge. Security measures must be robust to protect data locally. Network reliability and latency can also be challenging in some environments.

Can edge computing store data?

Yes, edge computing can store data, but it is limited in capacity. It usually stores data temporarily for quick access and processing. For long-term storage, data is often transferred to central cloud systems.

Which situation would benefit the most from using edge computing?

Edge computing is most beneficial in situations requiring real-time data processing. It is ideal for environments where quick decision-making is crucial, such as autonomous vehicles, healthcare monitoring, and industrial automation. Edge computing is also helpful in remote locations with limited internet connectivity, allowing local data handling and processing.

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