Edge Computing – How to Get Help

Edge computing is cutting-edge technology that is transforming the way we process data in digitally networked ecosystems. But exactly what is edge computing and why is edge computing important? Edge computing brings processing power closer to the source of data, rather than sending it to a centralized location. As the world becomes more connected and the amount of data generated continues to grow, edge computing is poised to play an increasingly important role in shaping the future of technology. This blog provides an introduction to edge computing, the benefits of edge computing and some examples on industry applications of edge computing.

Devices on the network’s edge help organizations process data and transactions quickly on site and can cut down on redundant network traffic and the latency that comes with it. There are challenges with managing so many devices and all the data they process, though, so edge computing is still changing and improving.

Edge computing brings data processing closer to where the data is collected. It’s often used in concert with IoT devices, big data sets, and real-time data. Edge is not a product but an architectural decision. Edge doesn’t replace cloud computing but complements it. Get advice from your peers and edge experts on edge computing strategy, trends, and more.

Edge computing is a type of information technology (IT) architecture that allows companies and organizations to extract reliable and secure service from their cloud computing applications and solutions and distribute those applications or solutions to multiple locations.By 2025, according to Gartner, 75% of enterprises’ data will be created outside their physical data centers. In addition, if we consider the growing need to move sensitive information without interruption or delay, edge computing is emerging as a method that provides proper and secure data flow.

Table of Contents

What is edge computing?

Edge computing refers to the practice of processing data closer to where it is generated, rather than sending it to a central location for processing. This means that data is processed in real time, without the need for a high-speed internet connection. Increasingly, the proliferation of connected, Internet of Things (IoT) devices is why edge computing is important. With so much data being generated by these devices, it’s not practical to send all the data to a central location for processing. Edge computing allows for faster, more efficient processing of data, which in turn can lead to better decision-making and improved performance.

Edge computing works by using mainly small, low-power devices called “edge devices.” These edge devices are located close to data sources and connected to a network, which allows them to communicate with each other and with the cloud. When data is generated, it’s processed by an edge device, which can then send the processed data to the cloud for further analysis. This process is much faster than sending all of the data to the cloud for processing, which can take a long time and require a lot of bandwidth. Edge computing will not replace cloud computing. Rather, edge computing aims to push some of the cloud’s processing power and storage capacity closer to devices that produce and consume the data. Therefore it extends today’s cloud capabilities to a seamless hybrid computing environment.

As we explore the possibilities that edge computing offers the world by bringing data storage and processing power closer to data source devices, one possibility includes multi-access edge computing (MEC). Multi-access edge computing is a specific implementation of edge computing for wireless communication networks, such as 4G or 5G. Multi-access edge computing focuses on deploying edge computing capabilities at base stations or access points, enhancing network performance and delivering low-latency services to wireless communication users. Such implementation concepts, like the MEC for telecommunications, play more and more a significant role in various industries, including healthcare, transportation and smart cities. Thereby it enables new edge-based services and applications, such as video analytics for real-time surveillance, low-latency analytic services or intelligent autonomous logistics.

Edge computing is technology operated at the users’ physical location, either at or near to where data processing occurs, for the purpose of providing fast and reliable digital services and solutions. Thanks to this kind of technology, companies in almost any line of business can use and distribute their information through a hybrid cloud computing system and take advantage of a vast number of resources in different locations. In other words, it is a distributed computing framework that brings business applications closer to where the data is generated to provide timely decision-making and network availability, and various benefits at the operational level.

What are the benefits of edge computing?

One of the biggest benefits of edge computing is its ability to reduce latency. By processing data closer to the source, edge computing can significantly reduce the time it takes to transmit data to the cloud. With edge computing, data is processed locally on devices or servers at the edge of the network, rather than being sent to a central data center for processing. This means that data can be processed in real time, without the latency that comes with sending data back and forth to a central location. This is particularly important for businesses that rely on real-time data analysis, such as those in the healthcare and finance industries, as well as applications that require real-time data processing, such as autonomous vehicles and industrial automation.

Another benefit of edge computing is its ability to enhance security, because sensitive data can be processed and stored locally instead of being sent to a central location where it may be more vulnerable to cyber attacks. By processing data locally, edge computing can reduce the risk of data breaches and cyber attacks.

Additionally, edge computing can help businesses reduce network congestion and bandwidth costs by reducing the amount of data that needs to be transmitted over the network and to the cloud.

Edge computing is a powerful technology and a compelling option for businesses looking to improve their data processing capabilities. Take advantage of three key benefits that edge computing offers:

Latency reduction
Enhanced security
Cost savings on bandwidth

Overall, edge computing is a useful technology for optimizing web apps and connected devices by securely minimizing bandwidth usage and latency. With that it has a big impact on both security and sustainability requirements of new IT/OT system landscapes.

How do companies use this technology? As you’ve probably already realized, edge computing is more than a solution; it is an IoT (Internet of Things) strategy that lets data centers extend to different places in order to support operations. In the same way as a hybrid cloud model, edge computing provides accessibility to run different applications and production processes in a company’s own data centers or cloud environments at different locations. Currently, telecommunications, transport, or public-service companies take advantage of these functionalities to:

Perform local and immediate data processing
Avoid information transfer latency from one server to another
Connect the equipment to one centralized platform to receive standardized updates
Allow for higher-speed internet services

What are the advantages of edge computing? Now, if you still can’t figure out why edge computing is useful and advantageous technology in itself, even with all the potential of cloud computing on hand, keep reading!

Low latency: Information flow takes relatively little time in establishing connectivity from one point to another, allowing for immediate streamlining of operations.
Total management: By having its own infrastructure, edge computing lets you manage public cloud and private cloud applications consistently.
Risk control: It facilitates network administration and drastically reduces the failures that can occur in local subnets that are a part of cloud application flow protocol.
Distribution: It enables hosting points to be spread out and many different cloud solutions to be used.

Edge computing benefits for IT security

When it comes to edge computing, security is an important consideration that cannot be overlooked. As more and more devices are connected to the internet, the risk of cyber attacks increases and the importance of cyber security for businesses cannot be underestimated. Edge computing has the potential to reduce the strain on network infrastructure and improve security by keeping sensitive data closer to the source. Edge computing, which involves processing data closer to the source rather than in a centralized location, can help mitigate some of these risks by reducing the amount of data that needs to be transmitted over the internet.

However, to implement edge computing, it is important to ensure that edge devices and networks are secure through measures such as encryption, access controls and regular software and firmware updates. Additionally, it is important to have a plan in place for responding to security incidents, as well as to regularly test and evaluate the security of edge computing systems. By taking these steps, organizations can harness the powerful benefits of edge computing while minimizing cyber security risks.

Edge computing benefits for EDI

The benefits of edge computing technology even extend to Electronic Data Interchange (EDI). Since edge computing technology involves processing data closer to the source of data generation, several edge computing benefits for EDI include:

Data Transformation and Validation: Edge computing nodes can pre-process EDI data at the edge, transforming it into a standardized format and validating it against predefined rules. This ensures that only accurate and properly formatted data is transmitted to centralized systems, reducing the burden on backend servers.
Real-time processing: Edge computing enables real-time processing of EDI transactions closer to the data source. This allows for faster response times, enabling organizations to quickly react to incoming EDI messages, such as orders or inventory updates.
Local decision-making: Edge computing empowers edge nodes to make local decisions based on predefined rules or machine learning models without the need to send data to centralized servers. This could involve automated order processing, inventory management or supply chain optimization of data at the edge.
Offline operation: Edge computing enables EDI systems to continue operating even when connectivity to centralized servers is lost. Edge nodes can store and process EDI data locally, syncing with backend systems once connectivity is restored, ensuring business continuity.
Reduced network costs: By processing EDI transactions locally at the edge, organizations can reduce the volume of data transmitted over the network to centralized servers. This can lead to cost savings, especially in scenarios where network bandwidth is limited or expensive.
Edge analytics: Edge computing enables organizations to perform analytics and derive insights from EDI data closer to the data source. This allows for real-time monitoring of supply chain performance, customer behavior analysis, and predictive analytics at the edge.
Enhanced security: Edge computing can enhance the security of EDI systems by implementing security measures such as data encryption, access control, and anomaly detection at the edge. This helps protect sensitive EDI data from security threats and breaches.

Leveraging edge computing technology for EDI offers benefits such as improved responsiveness, reduced network overhead, enhanced security and the ability to operate offline, making edge computing technology an option for modernizing electronic transaction processing systems.

What’s the relationship between edge computing and intelligent applications? At this point, it’s important to mention the applications that, together with edge computing, can achieve truly meaningful levels of efficiency in businesses. Data analysis Analytics tools are a crucial aspect of many work processes. To this end, edge computing can achieve high standards of speed and immediacy in analyzing data and processes. Now, cloud computing in many ways allows you to study substantial volumes of data that edge computing simply cannot, so if agility is not a key element, then cloud computing may be the better option. Artificial intelligence Artificial intelligence (AI) has proven to be a great ally in production processes, especially those where repetitive tasks are executed cyclically. Edge computing together with artificial intelligence can become a powerful implement for decision making and for carrying out productivity-boosting initiatives. However, cloud computing can provide a much broader framework in terms of the capabilities that AI algorithms have for recognizing images and studying the context due to their broad and near unlimited access to data. Machine learning Like with machine learning, edge computing provides the information necessary for essential data combinations to infer conditions and provides valuable forecasts for decision-making. But, in this respect, cloud computing is capable of applying event management, processing, classification, and combination techniques to infer scenarios with much greater capacity given the use of relevant information. Edge computing can, in many cases, complement a cloud model, especially considering the immediacy and low latency of data analysis. However, cloud models offer a wider range of functionality.

Edge computing benefits with an integration platform

A key benefit of edge computing technology, when it is integrated with an integration platform, is streamlined data management and analysis processes. This enables seamless communication and data exchange between edge devices and centralized systems, facilitating swift decision-making based on comprehensive and up-to-date information. With IoT devices generating large amounts of data at the edge, organizations face the challenge of efficiently collecting, processing and analyzing this data. Integration platforms provide capabilities that allow organizations to derive valuable insights by orchestrating data flows, aggregating data from diverse sources and applying analytics and AI algorithms to their data management and analysis processes.

Integration with edge computing technology enhances scalability and flexibility

Edge computing allows organizations to distribute computational tasks across a network of edge devices, reducing the burden on centralized servers and improving scalability. When coupled with an integration platform, this distributed architecture becomes even more agile, enabling organizations to adapt quickly to changing business requirements and scale their operations seamlessly. Whether deploying new edge devices, adding functionalities or integrating with third-party services, the combined capabilities of edge computing and an integration platform empower organizations to utilize edge-generated data, unlocking new opportunities for innovation and competitive advantage.

What are the industry applications of edge computing?

Bringing the processing power closer to data sources reduces the need for data to be sent to a centralized location for processing or control. This makes the industry applications of edge computing far-reaching, with the potential to further transform many industries such as automotive, healthcare and manufacturing.

Automotive industry applications of edge computing

In the automotive industry, edge computing is enhancing various aspects of vehicle functionality and enabling innovative applications for faster response times and increased safety.

Real-time vehicle monitoring: Edge computing enables automotive manufacturers to capture and analyze data from various vehicle sensors in real-time, allowing for proactive maintenance and improved performance. For example, edge devices can process data from engine sensors to identify potential issues and send alerts to drivers or maintenance teams, ensuring timely repairs and avoiding breakdowns.
Autonomous driving: When an autonomous vehicle drives down a road, it needs to collect and process data about traffic, pedestrians, street signs and stop lights. If the vehicle needs to quickly stop or swerve to avoid an accident, there just isn’t enough time to send data back and forth from the vehicle to the cloud. Edge computing brings cloud computing services closer to a vehicle, enabling the vehicle’s IoT sensors to process the data quickly enough in real time to avoid an accident. An autonomous vehicle can make split-second decisions without relying solely on cloud-based processing.
Intelligent traffic management: Edge computing can be utilized to analyze traffic patterns and optimize routes in real time. By collecting data from a network of edge devices installed in vehicles or roadside infrastructure, smart algorithms can process and analyze this data locally, providing drivers with real-time traffic information and suggesting alternative routes to avoid congestion and safety hazards. This improves overall traffic flow and reduces travel times for commuters.

Healthcare industry applications of edge computing

Edge computing has several new applications that are transforming the healthcare industry by enabling faster, more efficient and personalized care, including:

Remote patient monitoring: Edge computing allows for real-time monitoring of patients’ vital signs and health data at edge devices, such as wearable sensors or connected medical devices, so that doctors can respond quickly to any changes in a patient’s condition. This enables healthcare providers to receive timely updates on patients’ conditions and provide immediate interventions if necessary.
Telemedicine and telehealth: Edge computing can enhance telemedicine services by processing data closer to the source, reducing latency and enabling faster analysis of medical images or video consultations. This enables physicians to provide remote care with minimal delays, improving the patient experience and healthcare outcomes.
Emergency response systems: Edge computing can support emergency response systems by processing data from wearable devices or smart sensors in real time. This allows for quicker detection of changes in vital signs and quicker response to emergencies.

Manufacturing industry applications of edge computing

By leveraging edge computing, manufacturers can enhance their operational agility, reduce costs and improve the overall efficiency and quality of their manufacturing processes. Three examples of edge computing use cases in the manufacturing industry include:

Predictive maintenance: Edge computing enables manufacturers to implement real-time monitoring and analysis of machinery and equipment on the factory floor. By deploying edge devices and sensors, data can be collected and processed locally, allowing for quick detection of anomalies and potential failures. This helps manufacturers identify maintenance needs in advance and avoid any costly unplanned downtime, improving overall operational efficiency.
Quality control and inspection: Edge computing is used to perform real-time analysis of production data to ensure product quality and identify defects or deviations from set standards. High-speed cameras and sensors can capture images or measure parameters at high frequencies, allowing for immediate analysis and decision-making at the edge of the network. This enables manufacturers to intervene rapidly, reducing product waste and ensuring consistent quality.
Intralogistics optimization: Edge computing can be used to optimize inventory management and material flow. By deploying edge devices in warehouses and distribution centers, real-time data on inventory levels, demand patterns and transportation metrics can be analyzed locally and delivered autonomously to production lines via driverless transportation systems. In this way, manufacturers can improve order fulfillment and increase their ability to adapt and respond.

The possibilities of the industry applications of edge computing are becoming endless, but finally enable manufacturers to make quicker business decisions and enhance overall supply chain responsiveness and product visibility. As this continues to evolve we can expect to see even more exciting edge computing applications in the future. Indeed, the potential of edge computing to enable real-time responsiveness and faster decision-making positions it as a key technology for building smarter digital ecosystems with integrated smart services.

Leverage the power of edge computing through and for integration

Edge computing also challenges integration platform vendors to move their functions closer to the edge. Ensuring data governance when integrating data from multiple sources between edge devices and other endpoints involves several important aspects, including:

Connectivity: While edge computing devices process data locally, they can also connect to the cloud for additional analysis, storage and collaboration purposes. Integration platforms facilitate the secure and efficient integration of edge and cloud services, enabling hybrid computing environments.
Integration: Edge devices perform critical data processing tasks, such as real-time analytics, AI inference, or complex event processing. These localized processing functions also require efficient data integration within nodes.
Monitoring: Edge computing devices collect data from various sources, such as sensors, IoT devices, or on-premises applications. With pre-processed and filtered data, efficient monitoring can ensure data consistency and enable near real-time sharing of insights and information.