Industrial edge computing refers to the processing and analysis of data at or near the edge of an industrial network, where the data is generated rather than sent to a centralized cloud or data center. It involves deploying computing resources, such as servers, gateways, or edge devices, closer to the industrial machines, sensors, or equipment in manufacturing plants, oil rigs, power plants, and other industrial settings.
According to Gartner, currently, only about 10 percent of enterprise-generated data is created and processed outside a conventional data center or cloud; this figure is expected to rise to 75 percent by the year 2025.
This means making a potentially game-changing shift: away from the cloud towards edge computing.
Benefits of Industrial Edge Computing
The primary reason for adopting industrial edge computing is to address the challenges associated with latency, bandwidth limitations, and reliability in industrial environments. Here are a few reasons why industrial edge computing is important and why we should know about it:
Reduced latency
In time-sensitive industrial processes, such as automation, robotics, or real-time monitoring, latency (delay in data transmission) can have significant consequences. By processing data at the edge, closer to the source, industrial edge computing minimizes latency, enabling faster decision-making and improved responsiveness.
Bandwidth optimization
Industrial applications often generate large volumes of data, such as sensor readings, machine logs, or video feeds. Transferring all this data to a centralized cloud for processing can strain the available network bandwidth. With edge computing, data is filtered, aggregated, and processed locally, reducing the amount of data that needs to be transmitted to the cloud. This optimizes network bandwidth and reduces associated costs.
Enhanced Reliability
Industrial environments may experience intermittent or unreliable network connectivity. Using edge computing, critical operations can continue to function even during network outages. Edge devices can store and process data locally until connectivity is restored, ensuring continuous operation and minimizing downtime.
Improved Data Privacy and Security
Some industrial applications involve sensitive or confidential data that organizations prefer to keep within their premises. By processing data locally at the edge, companies can maintain better control over their data and reduce the risk of unauthorized access or data breaches associated with transmitting sensitive information to the cloud.
Real-time Insights and Decision-making
Real-time insights and decision-making become easier by analyzing and processing data at the edge. This is particularly valuable in scenarios requiring immediate action, such as predictive maintenance, anomaly detection, or quality control.
Scalability and cost-effectiveness
Edge computing can be easily scalable, allowing organizations to deploy additional edge devices or resources as needed without relying solely on cloud infrastructure. This flexibility and scalability help organizations adapt to evolving industry requirements and reduce the costs of transmitting and storing massive amounts of data in the cloud.
“With field-level interfaces connected to the industrial edge, access to real-time process data is possible. This is essential for use cases which require a lot of storage and processing power, such as powerful digital twin models,” said George Stoger, Director of Training and Consulting TTTech Industrial.
He further elaborates, “Not all data may be needed in the cloud; users could send only the data required for analysis or storage to the cloud, thus saving bandwidth and cost, while processing the full data set at the edge.”
Involved Technologies
Let me give you a brief introduction to the basic technologies and devices involved here.
Edge devices/gateways
These are specialized hardware devices deployed at the edge of industrial networks to collect, process, and transmit data. They often include computing resources, such as processors, memory, and storage, and can run edge applications. Examples of edge devices include industrial gateways, edge servers, or programmable logic controllers (PLCs).
Edge analytics software
Edge analytics software runs directly on edge devices and performs data processing, analysis, and decision-making at the edge. It allows for real-time insights and can be tailored to specific industrial applications. This software can filter, aggregate, and analyze data locally before transmitting only relevant information to the cloud or data center.
Machine learning at the edge
Edge computing enables the deployment of machine learning algorithms directly on edge devices. This allows for real-time data analysis and inference, facilitating predictive maintenance, anomaly detection, quality control, and other machine learning-driven industrial applications. Edge devices with dedicated AI accelerators or GPU capabilities can enhance the performance of machine learning algorithms.
Edge data storage
Edge computing involves storing and caching data locally on edge devices to reduce latency and improve responsiveness. Edge data storage can be in the form of solid-state drives (SSDs) or other local storage options. Data can be temporarily stored at the edge for immediate processing or forwarded to the cloud for long-term storage.
Edge networking technologies
Industrial edge computing relies on robust networking technologies to establish connectivity and enable communication between edge devices, cloud infrastructure, and other parts of the industrial network. Examples include industrial Ethernet protocols (e.g., PROFINET, EtherCAT), wireless technologies (e.g., Wi-Fi, Bluetooth, LoRaWAN), and cellular networks (e.g., 4G/5G) for remote edge deployments.
Edge management and orchestration
Industrial edge computing often involves managing and orchestrating multiple edge devices across a distributed network. Edge management platforms provide centralized control, monitoring, and configuration of edge devices, enabling efficient deployment, software updates, security management, and remote troubleshooting.
Edge security
Industrial edge computing requires robust security measures to protect data, devices, and the overall industrial infrastructure. Edge security technologies include authentication mechanisms, data encryption, access controls, intrusion detection and prevention systems (IDPS), and secure communication protocols to ensure the integrity, confidentiality, and availability of data and applications at the edge.
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Basic Architecture
In the following paragraphs, let’s demystify the three layers in the edge computing architecture.
Source : FSP Group
Cloud Layer
Although edge computing was introduced to address network congestion and latency problems commonly found in cloud computing, cloud computing in fact still plays an important role in the entire edge computing architecture. We can say that cloud computing and edge computing complement one another. Through the edge layer described next, the entire system determines if data needs to be processed in the cloud layer. If so, edge servers will pass data to the cloud layer for complex processing. On the other hand, edge servers will also pass a part of critical data to the cloud layer for storage and comprehensive analysis. This also demonstrates the integration between both the cloud and edge layers.
Edge Layer
This layer mainly consists of edge servers, and when compared to the cloud layer, the edge layer contains edge servers that are larger in quantity and more vastly deployed. Therefore, the edge layer can process data that is closer to the data source and address latency problems found in cloud computing. The edge layer can be considered the core of the entire edge computing architecture. After data from the device layer is analyzed and processed in the edge layer, data is transmitted to the cloud layer for subsequent processing and analysis. Data which cannot be processed in the edge layer can be sent to and analyzed in the cloud layer to ensure data integrity.
Device Layer
Amongst the three layers, the device layer contains the most devices. Ranging from devices as small as our mobile phones or computers to ones as large as buses and factories, these devices are all examples of components in the device layer. Through their sensors, devices in the device layer collect and capture data used to help products achieve the purposes they are designed for. Equipment in a hospital collecting vital signs of patients and autonomous vehicles capturing data from other nearby vehicles are all such examples. Although components in the cloud and edge layers possess better computing power, the devices in the device layer can still perform data analyses, processing and storage tasks which require negligible computing power, and process data closest to the data source in almost real‑time.
Potential Tradeoffs and Challenges to Consider Before Making the Jump
There are many trade-offs to consider when moving to edge computing:
- The complexity of managing different edge computing devices.
- Requirements for processing the data
- Additional expenses to operate and maintain those edge computing devices
Cold Start problem
David Linthicum, a renowned author of several cloud computing books, believes there is a limit to how much you can push data processing and storage towards the edge. After a certain point, the performance degradation is very obvious. The main reason he describes is the ‘cold start problem’ that may occur on the edge device.
If the code running on that device has not been used for some time, then it won’t be present in its cache memory and will be slow to launch in the beginning. This problem will start piling up if you have hundreds of edge devices that act on processes and produce data as requested at irregular times. This can lead to a 2-to 4- second cold start delays, which would be unacceptable for many users especially compared to consistent sub-second response times from cloud-based systems even with the network latency. Of course, your performance will depend on the speed of the network and the number of hops.
System architects may suggest deploying edge devices with larger caches and more capable edge computing systems. However, the solution’s economic viability is threatened if these fixes are applied to all those 500+ devices.
Infrastructural Problems
Industrial edge computing can face several challenges due to the harsh industrial environment. Environmental conditions, like hot and humid temperatures, power variations, air quality degradations, and physical security challenges, are some of the major areas of concern.
Companies involved
Several companies are actively involved in the industrial edge computing sector, providing solutions and technologies to support industrial edge deployments. Here are some notable firms working in this field:
Intel Corporation
Intel offers a range of edge computing solutions, including processors, accelerators, and software frameworks optimized for industrial edge computing. Their hardware and software platforms enable real-time analytics, machine learning, and edge connectivity.
Microsoft Corporation
Microsoft’s Azure IoT Edge is a platform that supports edge computing in industrial settings. It allows organizations to deploy and manage edge applications, run analytics locally, and integrate with Azure cloud services for seamless data processing and analysis.
Schneider Electric
Schneider Electric offers industrial edge computing solutions under its EcoStruxure platform. Their edge control and analytics capabilities enable real-time data processing, optimization, and predictive maintenance in various industrial applications.
Dell Technologies
Dell Technologies provides edge computing solutions through its Dell EMC Edge portfolio. It includes ruggedized edge servers, gateways, and edge analytics software for industrial deployments, enabling data processing and analytics closer to the source.
HPE (Hewlett Packard Enterprise)
HPE offers edge computing solutions through its Edgeline Converged Edge Systems. These systems combine computing, storage, and networking capabilities to support real-time analytics and data processing at the edge in industrial environments.
Cisco Systems
Cisco offers industrial edge computing solutions through its Industrial Compute Gateway and IOx platform. These solutions enable edge computing capabilities, such as data processing, control, and connectivity, in industrial networks.
NVIDIA Corporation
NVIDIA provides edge computing solutions focusing on AI and machine learning at the edge. Their GPUs and AI platforms enable real-time inference and advanced analytics at the edge, empowering industrial applications with AI capabilities.
Bosch
Bosch offers industrial edge computing solutions through its Rexroth IoT Gateway and Edge analytics software. Their solutions enable edge data processing, analysis, and integration with cloud platforms for industrial automation and optimization.
ABB
ABB provides industrial edge computing solutions through its Ability Edge platform. It enables real-time data processing, analytics, and control at the edge, supporting various industrial applications, including power grids, manufacturing, and transportation.
These are just a few examples of companies working in the industrial edge computing sector. Many other technology vendors, system integrators, and industrial automation companies are also actively developing edge computing solutions tailored to industrial environments.
Applications of Industrial Edge Computing
Telecommunications industry
Industrial-grade edge computing plays a crucial role in the telecommunications industry, enabling various applications and services that require low latency, high bandwidth, and reliable connectivity. Here are some examples of how industrial-grade edge computing is used in the telecommunications sector:
Mobile Edge Computing (MEC)
MEC brings computing capabilities and services closer to the mobile network edge, enabling low-latency processing and real-time data analysis for mobile network applications. MEC facilitates services like content delivery, video caching, augmented reality, and virtual reality, improving user experience and reducing network congestion by processing data at the edge instead of sending it back to centralized data centers.
Network Function Virtualization (NFV)
NFV is a technology that virtualizes traditional network functions, such as firewalls, load balancers, and routers, and runs them on industry-standard servers at the network edge. By deploying virtualized network functions at the edge, telecommunications providers can improve network efficiency, scalability, and agility while reducing infrastructure costs.
Edge Data Centers
Telecommunications companies often deploy edge data centers at strategic locations to bring computing resources closer to the end-users and devices. These edge data centers process and store data locally, reducing latency and improving application performance. They are used for video streaming, gaming, IoT data processing, and real-time analytics.
5G Network Edge Computing
With the advent of 5G networks, edge computing has become even more critical in the telecommunications industry. The low-latency and high-bandwidth capabilities of 5G make deploying edge computing infrastructure closer to the access network possible. This enables ultra-low-latency services, real-time analytics, and distributed edge applications, supporting use cases such as autonomous vehicles, smart cities, and industrial IoT.
Edge Content Delivery Networks (CDN)
Telecommunications providers leverage edge CDN services to deliver high-bandwidth content, such as videos, images, and software updates, to end-users with low latency. Edge CDNs cache content at edge locations, reducing the distance data needs to travel and improving delivery speeds, particularly for popular or time-sensitive content.
IoT Connectivity and Management
Industrial-grade edge computing enable efficient management, connectivity, and processing of data from IoT devices in the telecommunications industry. Edge devices and gateways collect and preprocess data from a large number of IoT devices, reducing the need for transmitting vast amounts of data to central servers. Edge analytics can also enable real-time decision-making and automation for IoT applications.
Network Monitoring and Security
Edge computing is used to monitor and analyze network traffic and security events in real-time. By deploying edge-based monitoring and security applications, telecommunications providers can quickly identify and mitigate network issues, intrusions, and threats, enhancing overall network reliability and security.
Manufacturing
Industrial-grade edge computing is extensively used in the manufacturing sector to enhance operational efficiency, enable real-time decision-making, and drive automation. Here are some examples of how industrial-grade edge computing is employed in manufacturing:
Real-time Process Monitoring and Control:
Edge computing enables real-time monitoring and control of manufacturing processes. Edge devices placed on the factory floor collect data from sensors, machines, and production lines, and process it locally. Real-time analytics and control algorithms running at the edge help optimize process parameters, detect anomalies, and enable predictive maintenance, ensuring efficient and uninterrupted production.
Quality Control and Defect Detection:
Industrial-grade edge computing enables real-time quality control and defect detection in manufacturing. Edge devices equipped with vision systems, sensors, and machine learning algorithms perform on-the-spot analysis of product quality, identifying defects, deviations, or non-conformities. This enables immediate corrective actions, reducing scrap, improving yield, and enhancing overall product quality.
Predictive Maintenance:
Edge computing plays a crucial role in predictive maintenance strategies for manufacturing equipment. Edge devices collect data from sensors embedded in machines, such as temperature, vibration, or energy consumption. Local analysis of this data enables early detection of equipment anomalies or deterioration, triggering maintenance alerts and preventing costly unplanned downtime.
Edge Robotics and Automation:
Industrial robots and automation systems increasingly leverage edge computing capabilities. By deploying edge devices alongside robots, real-time data processing enables quick and precise decision-making, reducing the need for round-trip communication with centralized systems. Edge robotics enhances flexibility, responsiveness, and autonomy, enabling tasks such as collaborative robots, automated guided vehicles (AGVs), or pick-and-place operations.
Edge-based Inventory Management:
Edge computing facilitates real-time inventory management and optimization in manufacturing. Edge devices equipped with RFID or barcode scanners can track inventory levels, monitor stock movements, and trigger automated replenishment processes. This minimizes stockouts, reduces inventory carrying costs, and streamlines supply chain operations.
Edge-based Energy Management:
It is utilized for real-time energy management in manufacturing facilities. Edge devices monitor and analyze energy consumption, identify energy inefficiencies, and optimize energy usage. This helps manufacturers reduce energy costs, improve sustainability, and comply with energy efficiency regulations.
Healthcare
Industrial-grade edge computing can be deployed in the healthcare industry to improve patient care, enhance operational efficiency, and enable advanced healthcare applications. Here are some potential use cases and benefits of deploying industrial-grade edge computing in healthcare:
Real-time Patient Monitoring:
Edge devices placed at the bedside or on wearable devices can collect and process real-time patient data, such as vital signs, electrocardiograms (ECGs), or oxygen saturation levels. Edge analytics can enable early detection of abnormalities, triggering immediate alerts to healthcare providers for timely interventions and improving patient outcomes.
Telemedicine and Remote Care:
Edge computing can support telemedicine and remote care initiatives by enabling real-time video consultations, remote diagnostics, and virtual patient monitoring. Edge devices with audiovisual capabilities and edge analytics can facilitate seamless and secure communication between healthcare professionals and patients, regardless of their physical locations.
Healthcare IoT Applications:
Industrial-grade edge computing can handle the large volume of data generated by healthcare IoT devices. Edge devices at hospitals, clinics, or even in patients’ homes can preprocess and filter data from various IoT sensors, wearables, and medical devices. This reduces the need for transmitting all data to centralized servers and enables timely data analysis and actionable insights at the edge.
Edge-enabled Imaging and Diagnostics:
Edge computing can enhance imaging and diagnostic processes in healthcare. Edge devices with advanced image processing capabilities can preprocess medical imaging data, perform edge-based image analysis, and generate preliminary reports for radiologists or pathologists. This reduces the time needed for diagnosis and enables faster treatment decisions.
Edge Security and Privacy:
Healthcare data is highly sensitive and requires stringent security measures. Industrial-grade edge computing can provide enhanced security and privacy by encrypting data at the edge, implementing access controls, and minimizing data transmission to external systems. This helps protect patient information while still enabling efficient data processing and analysis.
Shipping
The shipping industry harnesses industrial edge computing to improve operational efficiency, optimize vessel performance, enhance safety, and enable data-driven decision-making. Here are some ways in which industrial edge computing is being utilized in the shipping industry:
Vessel Performance Optimization:
It enables real-time monitoring and analysis of vessel performance parameters, such as fuel consumption, engine performance, and emissions. Edge devices installed onboard ships collect data from sensors, engines, and navigation systems. Localized edge analytics and algorithms process this data to optimize fuel efficiency, track engine health, and identify potential maintenance needs, resulting in cost savings and improved environmental sustainability.
Predictive Maintenance:
Edge computing plays a critical role in predictive maintenance strategies for ships. Edge devices collect data from various onboard sensors and systems, such as vibration, temperature, and oil condition monitoring systems. Localized edge analytics and machine learning algorithms analyze this data to detect anomalies, predict equipment failures, and trigger maintenance alerts. Shipping companies can proactively address maintenance needs, reduce unplanned downtime, optimize maintenance schedules, and enhance overall vessel reliability.
Safety and Security Enhancements:
It improves safety and security in the shipping industry. Edge devices equipped with video analytics, thermal imaging, and intrusion detection capabilities can monitor vessel perimeters, detect potential safety hazards, and identify security breaches in real-time. This enables timely responses, mitigates risks, and ensures the safety of crew, cargo, and the vessel itself.
Cargo Monitoring and Optimization:
It enables real-time monitoring and optimization of cargo conditions during shipping. Edge devices equipped with sensors can track parameters like temperature, humidity, and location, ensuring optimal storage conditions and preventing damage to sensitive cargo. Edge analytics can process this data locally, providing insights into cargo conditions, potential issues, and optimization opportunities.
Edge-based Decision Support Systems:
Industrial edge computing facilitates onboard decision support systems for ship operators. Edge devices with localized computing capabilities can process data from various sources, including weather forecasts, navigational data, and operational parameters. Edge-based decision support systems provide real-time insights and recommendations to crew members, assisting with route planning, fuel optimization, and compliance with regulations.
Data Aggregation and Integration:
Lastly, Industrial edge computing facilitates the aggregation and integration of data from multiple vessels and maritime systems. Edge devices preprocess and filter data, performing initial analysis and data fusion at the edge. This reduces the volume of data transmitted to central systems and enhances data quality for further analysis, reporting, and decision-making.
Streaming Content Providers
Streaming content producers like Netflix and HBO can leverage industrial edge computing to enhance the delivery of their content, improve user experiences, and optimize their operations. Here are some ways in which they can make use of industrial edge computing to their advantage:
Content Caching and Delivery:
It enables content caching and delivery at the edge of the network, closer to the end-users. By deploying edge servers in distributed locations, streaming content producers can cache popular or frequently accessed content locally. This reduces latency, improves streaming quality, and enhances user experiences by delivering content more quickly and reliably.
Edge-based Content Optimization:
Edge devices can preprocess and transcode video content, adapting it to different devices, screen resolutions, or network conditions. This reduces the need for centralized transcoding, improves scalability, and ensures optimal content delivery to a wide range of user devices.
Content Recommendation and Personalization:
Edge computing facilitates localized content recommendation and personalization algorithms. Edge devices can collect user data, including viewing habits, preferences, and interactions, and process it locally to generate personalized content recommendations. This enables real-time personalization, reduces reliance on centralized recommendation engines, and enhances the user experience by delivering tailored content suggestions.
Quality of Service (QoS) Monitoring:
Industrial edge computing enables real-time monitoring of streaming quality and network performance. Edge devices equipped with monitoring capabilities can assess network conditions, video playback quality, and user experience metrics. Localized analytics and algorithms can detect issues such as buffering, latency, or quality degradation, enabling rapid troubleshooting and ensuring a consistent high-quality streaming experience.
Edge-enabled Analytics and Insights:
It allows streaming content producers to perform real-time analytics and gain actionable insights. Edge devices can process streaming data, user behavior data, and performance metrics locally, generating real-time analytics and insights on user engagement, content popularity, or network performance. This enables quick decision-making, content optimization, and business intelligence for streaming service providers.
Edge-based Content Security:
This technology strengthens content security measures for streaming platforms. Edge devices can enforce digital rights management (DRM) protocols, perform encryption and decryption, and detect potential security threats at the edge. This helps protect copyrighted content, prevent unauthorized access, and enhance content security throughout the streaming delivery chain.
Edge-based Ad Insertion and Targeting:
Industrial-grade edge computing facilitates localized ad insertion and targeting capabilities. Edge devices can detect ad cues or triggers within the streaming content and dynamically insert targeted advertisements based on user demographics, preferences, or viewing history. This enables personalized ad experiences, improves ad relevance, and enhances monetization opportunities for streaming content producers.
Governments
City governments can leverage industrial edge computing to enhance service delivery, improve efficiency, and create smarter and more sustainable cities. Here are some ways in which governments can make use of industrial edge computing for service delivery:
Smart Infrastructure Management:
Industrial-grade edge computing enables real-time monitoring and management of city infrastructure. Edge devices installed in critical infrastructure, such as transportation systems, utilities, or waste management facilities, can collect data on performance, energy usage, or maintenance needs. Localized edge analytics and control algorithms can optimize resource allocation, enable predictive maintenance, and improve the overall efficiency and reliability of infrastructure systems.
Intelligent Traffic Management:
Edge computing can enhance traffic management and optimize transportation systems within cities. Edge devices equipped with sensors and video analytics can collect data on traffic flow, congestion, and accidents in real-time. Edge-based algorithms can process this data locally to optimize traffic signal timing, reroute vehicles, and provide real-time traffic information to commuters, reducing congestion and improving traffic flow.
Environmental Monitoring and Sustainability:
It enables real-time environmental monitoring in cities. Edge devices equipped with sensors can measure air quality, noise levels, temperature, and other environmental parameters. Localized edge analytics can process this data, providing insights into environmental conditions and enabling timely interventions to mitigate pollution or address sustainability challenges.
Citizen Engagement and Services:
Industrial-grade edge computing facilitates localized citizen engagement and service delivery. Edge devices equipped with communication capabilities can provide interactive touchpoints for citizens to access city services, report issues, or engage with local government. Edge-based applications and services can deliver personalized information, alerts, or recommendations to citizens based on their location or preferences, enhancing citizen satisfaction and participation.
Public Safety and Security:
It enhances public safety and security measures in cities. Edge devices with video analytics and surveillance capabilities can detect security threats, monitor public spaces, and facilitate emergency response. Localized edge analytics can process video feeds and sensor data, enabling real-time threat detection, reducing response times, and enhancing overall public safety.
Data-driven Decision Making:
It enables localized data processing and analytics, empowering city governments to make data-driven decisions in real-time. Edge devices can preprocess and analyze data from various sources, such as sensors, IoT devices, or social media feeds, providing insights into citizen behavior, service demand, or infrastructure performance. This enables evidence-based decision-making, policy formulation, and resource allocation for effective city management.
By leveraging industrial edge computing, city governments can optimize service delivery, improve efficiency, enhance sustainability, and foster citizen engagement. The localized processing and data analysis at the edge enable real-time decision-making, reduced latency, and improved responsiveness to citizen needs in building smarter and more livable cities.
Top Voices in the Edge Computing Space
Jeff Barr
He is the Vice President and Chief Evangelist at Amazon Web Services. He frequently talks about cloud and edge computing and has written over 3000 blog posts on the AWS blog.
David Linthicum
He is one of the most widely-known technology influencers when it comes to cloud and edge computing. He works as Chief Cloud Strategy Officer at Deloitte. Previously he has worked for companies like Ernest & Young, AT&T, and IBM. He consistently writes about technology at Infoworld.
Mr. Speed is the head of edge computing technologies at Ampere and works on IoT, automobiles, robotics and 5G technologies. Previously he worked with IBM as Co-founder and Product Owner of IBM AutoLab Munich co-development centre for IoT-connected cars, AI cockpits, and AVs.
Kilton Hopkins
Mr Hopkins is the CEO of Edgeworx. Previously, he was the IoT program director at Northeastern University, San Francisco.
Jason Shepherd
He is the CEO at Nubix and regularly speaks and writes about edge computing. Previously, he was VP of Ecosystem at the edge orchestration startup ZEDEDA. Before that, he served as Dell Technologies’ CTO for IoT and Edge Computing. He also served as board chair for Linux Foundation Edge from 2021-2022 and sits on The Channel Company’s IoT Advisory Board.
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