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Edge Computing Use Cases in Banking

by | Aug 13, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

financial services

The banking and financial services industry deals with enormous, highly sensitive datasets collected from remote sites like branches, ATMs, and mobile applications. Efficiently leveraging this data while avoiding regulatory, security, and reliability issues is extremely challenging when the hardware and software resources used to analyze that data reside in the cloud or a centralized data center.

Edge computing decentralizes computing resources and distributes them at the network’s “edges,” where most banking operations take place. Running applications and leveraging data at the edge enables real-time analysis and insights, mitigates many security and compliance concerns, and ensures that systems remain operational even if Internet access is disrupted. This blog describes four edge computing use cases in banking, lists the benefits of edge computing for the financial services industry, and provides advice for ensuring the resilience, scalability, and efficiency of edge computing deployments.

4 Edge computing use cases in banking

1. AI-powered video surveillance

PCI DSS requires banks to monitor key locations with video surveillance, review and correlate surveillance data on a regular basis, and retain videos for at least 90 days. Constantly monitoring video surveillance feeds from bank branches and ATMs with maximum vigilance is nearly impossible for humans, but machines excel at it. Financial institutions are beginning to adopt artificial intelligence solutions that can analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security personnel.

When these AI-powered surveillance solutions are deployed at the edge, they can analyze video feeds in real time, potentially catching a crime as it occurs. Edge computing also keeps surveillance data on-site, reducing bandwidth costs and network latency while mitigating the security and compliance risks involved with storing videos in the cloud.

2. Branch customer insights

Banks collect a lot of customer data from branches, web and mobile apps, and self-service ATMs. Feeding this data into AI/ML-powered data analytics software can provide insights into how to improve the customer experience and generate more revenue. By running analytics at the edge rather than from the cloud or centralized data center, banks can get these insights in real-time, allowing them to improve customer interactions while they’re happening.

For example, edge-AI/ML software can help banks provide fast, personalized investment advice on the spot by analyzing a customer’s financial history, risk preferences, and retirement goals and recommending the best options. It can also use video surveillance data to analyze traffic patterns in real-time and ensure tellers are in the right places during peak hours to reduce wait times.

3. On-site data processing

Because the financial services industry is so highly regulated, banks must follow strict security and privacy protocols to protect consumer data from malicious third parties. Transmitting sensitive financial data to the cloud or data center for processing increases the risk of interception and makes it more challenging to meet compliance requirements for data access logging and security controls.

Edge computing allows financial institutions to leverage more data on-site, within the network security perimeter. For example, loan applications contain a lot of sensitive and personally identifiable information (PII). Processing these applications on-site significantly reduces the risk of third-party interception and allows banks to maintain strict control over who accesses data and why, which is more difficult in cloud and colocation data center environments.

4. Enhanced AIOps capabilities

Financial institutions use AIOps (artificial intelligence for IT operations) to analyze monitoring data from IT devices, network infrastructure, and security solutions and get automated incident management, root-cause analysis (RCA), and simple issue remediation. Deploying AIOps at the edge provides real-time issue detection and response, significantly shortening the duration of outages and other technology disruptions. It also ensures continuous operation even if an ISP outage or network failure cuts a branch off from the cloud or data center, further helping to reduce disruptions and remote sites.

Additionally, AIOps and other artificial intelligence technology tend to use GPUs (graphics processing units), which are more expensive than CPUs (central processing units), especially in the cloud. Deploying AIOps on small, decentralized, multi-functional edge computing devices can help reduce costs without sacrificing functionality. For example, deploying an array of Nvidia A100 GPUs to handle AIOps workloads costs at least $10k per unit; comparable AWS GPU instances can cost between $2 and $3 per unit per hour. By comparison, a Nodegrid Gate SR costs under $5k and also includes remote serial console management, OOB, cellular failover, gateway routing, and much more.

The benefits of edge computing for banking

Edge computing can help the financial services industry:

  • Reduce losses, theft, and crime by leveraging artificial intelligence to analyze real-time video surveillance data.
  • Increase branch productivity and revenue with real-time insights from security systems, customer experience data, and network infrastructure.
  • Simplify regulatory compliance by keeping sensitive customer and financial data on-site within company-owned infrastructure.
  • Improve resilience with real-time AIOps capabilities like automated incident remediation that continues operating even if the site is cut off from the WAN or Internet
  • Reduce the operating costs of AI and machine learning applications by deploying them on small, multi-function edge computing devices. 
  • Mitigate the risk of interception by leveraging financial and IT data on the local network and distributing the attack surface.

Edge computing best practices

Isolating the management interfaces used to control network infrastructure is the best practice for ensuring the security, resilience, and efficiency of edge computing deployments. CISA and PCI DSS 4.0 recommend implementing isolated management infrastructure (IMI) because it prevents compromised accounts, ransomware, and other threats from laterally moving from production resources to the control plane.

IMI with Nodegrid(2)

Using vendor-neutral platforms to host, connect, and secure edge applications and workloads is the best practice for ensuring the scalability and flexibility of financial edge architectures. Moving away from dedicated device stacks and taking a “platformization” approach allows financial institutions to easily deploy, update, and swap out applications and capabilities on demand. Vendor-neutral platforms help reduce hardware overhead costs to deploy new branches and allow banks to explore different edge software capabilities without costly hardware upgrades.

Edge-Management-980×653

Additionally, using a centralized, cloud-based edge management and orchestration (EMO) platform is the best practice for ensuring remote teams have holistic oversight of the distributed edge computing architecture. This platform should be vendor-agnostic to ensure complete coverage over mixed and legacy architectures, and it should use out-of-band (OOB) management to provide continuous remote access to edge infrastructure even during a major service outage.

How Nodegrid streamlines edge computing for the banking industry

Nodegrid is a vendor-neutral edge networking platform that consolidates an entire edge tech stack into a single, cost-effective device. Nodegrid has a Linux-based OS that supports third-party VMs and Docker containers, allowing banks to run edge computing workloads, data analytics software, automation, security, and more. 

The Nodegrid Gate SR is available with an Nvidia Jetson Nano card that’s optimized for artificial intelligence workloads. This allows banks to run AI surveillance software, ML-powered recommendation engines, and AIOps at the edge alongside networking and infrastructure workloads rather than purchasing expensive, dedicated GPU resources. Plus, Nodegrid’s Gen 3 OOB management ensures continuous remote access and IMI for improved branch resilience.

Get Nodegrid for your edge computing use cases in banking

Nodegrid’s flexible, vendor-neutral platform adapts to any use case and deployment environment. Watch a demo to see Nodegrid’s financial network solutions in action.

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AI Orchestration: Solving Challenges to Improve AI Value

by | Aug 2, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

AI Orchestration(1)
Generative AI and other artificial intelligence technologies are still surging in popularity across every industry, with the recent McKinsey global survey finding that 72% of organizations had adopted AI in at least one business function. In the rush to capitalize on the potential productivity and financial gains promised by AI solution providers, technology leaders are facing new challenges relating to deploying, supporting, securing, and scaling AI workloads and infrastructure. These challenges are exacerbated by the fragmented nature of many enterprise IT environments, with administrators overseeing many disparate, vendor-specific solutions that interoperate poorly if at all.

The goal of AI orchestration is to provide a single, unified platform for teams to oversee and manage AI-related workflows across the entire organization. This post describes the ideal AI orchestration solution and the technologies that make it work, helping companies use artificial intelligence more efficiently.

AI challenges to overcome

The challenges an organization must overcome to use AI more cost-effectively and see faster returns can be broken down into three categories:

  1. Overseeing AI-led workflows to ensure models are behaving as expected and providing accurate results, when these workflows are spread across the enterprise in different geographic locations and vendor-specific applications.
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  2. Efficiently provisioning, maintaining, and scaling the vast infrastructure and computational resources required to run intensive AI workflows at remote data centers and edge computing sites.
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  3. Maintaining 24/7 availability and performance of remote AI workflows and infrastructure during security breaches, equipment failures, network outages, and natural disasters.

These challenges have a few common causes. One is that artificial intelligence and the underlying infrastructure that supports it are highly complex, making it difficult for human engineers to keep up. Two is that many IT environments are highly fragmented due to closed vendor solutions that integrate poorly and require administrators to manage too many disparate systems, allowing coverage gaps to form. Three is that many AI-related workloads occur off-site at data centers and edge computing sites, so it’s harder for IT teams to repair and recover AI systems that go down due to a networking outage, equipment failure, or other disruptive event.

How AI orchestration streamlines AI/ML in an enterprise environment

The ideal AI orchestration platform solves these problems by automating repetitive and data-heavy tasks, unifying workflows with a vendor-neutral platform, and using out-of-band (OOB) serial console management to provide continuous remote access even during major outages.

Automation

Automation is crucial for teams to keep up with the pace and scale of artificial intelligence. Organizations use automation to provision and install AI data center infrastructure, manage storage for AI training and inference data, monitor inputs and outputs for toxicity, perform root-cause analyses when systems fail, and much more. However, tracking and troubleshooting so many automated workflows can get very complicated, creating more work for administrators rather than making them more productive. An AI orchestration platform should provide a centralized interface for teams to deploy and oversee automated workflows across applications, infrastructure, and business sites.

Unification

The best way to improve AI operational efficiency is to integrate all of the complicated monitoring, management, automation, security, and remediation workflows. This can be accomplished by choosing solutions and vendors that interoperate or, even better, are completely vendor-agnostic (a.k.a., vendor-neutral). For example, using open, common platforms to run AI workloads, manage AI infrastructure, and host AI-related security software can help bring everything together where administrators have easy access. An AI orchestration platform should be vendor-neutral to facilitate workload unification and streamline integrations.

Resilience

AI models, workloads, and infrastructure are highly complex and interconnected, so an issue with one component could compromise interdependencies in ways that are difficult to predict and troubleshoot. AI systems are also attractive targets for cybercriminals due to their vast, valuable data sets and because of how difficult they are to secure, with HiddenLayer’s 2024 AI Threat Landscape Report finding that 77% of businesses have experienced AI-related breaches in the last year. An AI orchestration platform should help improve resilience, or the ability to continue operating during adverse events like tech failures, breaches, and natural disasters.

Gen 3 out-of-band management technology is a crucial component of AI and network resilience. A vendor-neutral OOB solution like the Nodegrid Serial Console Plus (NSCP) uses alternative network connections to provide continuous management access to remote data center, branch, and edge infrastructure even when the ISP, WAN, or LAN connection goes down. This gives administrators a lifeline to troubleshoot and recover AI infrastructure without costly and time-consuming site visits. The NSCP allows teams to remotely monitor power consumption and cooling for AI infrastructure. It also provides 5G/4G LTE cellular failover so organizations can continue delivering critical services while the production network is repaired.

A diagram showing isolated management infrastructure with the Nodegrid Serial Console Plus.

Gen 3 OOB also helps organizations implement isolated management infrastructure (IMI), a.k.a, control plane/data plane separation. This is a cybersecurity best practice recommended by the CISA as well as regulations like PCI DSS 4.0, DORA, NIS2, and the CER Directive. IMI prevents malicious actors from being able to laterally move from a compromised production system to the management interfaces used to control AI systems and other infrastructure. It also provides a safe recovery environment where teams can rebuild and restore systems during a ransomware attack or other breach without risking reinfection.

Getting the most out of your AI investment

An AI orchestration platform should streamline workflows with automation, provide a unified platform to oversee and control AI-related applications and systems for maximum efficiency and coverage, and use Gen 3 OOB to improve resilience and minimize disruptions. Reducing management complexity, risk, and repair costs can help companies see greater productivity and financial returns from their AI investments.

The vendor-neutral Nodegrid platform from ZPE Systems provides highly scalable Gen 3 OOB management for up to 96 devices with a single, 1RU serial console. The open Nodegrid OS also supports VMs and Docker containers for third-party applications, so you can run AI, automation, security, and management workflows all from the same device for ultimate operational efficiency.

Streamline AI orchestration with Nodegrid

Contact ZPE Systems today to learn more about using a Nodegrid serial console as the foundation for your AI orchestration platform. Contact Us

Edge Computing Use Cases in Telecom

by | Jul 31, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

This blog describes four edge computing use cases in telecom before describing the benefits and best practices for the telecommunications industry.
Telecommunications networks are vast and extremely distributed, with critical network infrastructure deployed at core sites like Internet exchanges and data centers, business and residential customer premises, and access sites like towers, street cabinets, and cell site shelters. This distributed nature lends itself well to edge computing, which involves deploying computing resources like CPUs and storage to the edges of the network where the most valuable telecom data is generated. Edge computing allows telecom companies to leverage data from CPE, networking devices, and users themselves in real-time, creating many opportunities to improve service delivery, operational efficiency, and resilience.

This blog describes four edge computing use cases in telecom before describing the benefits and best practices for edge computing in the telecommunications industry.

4 Edge computing use cases in telecom

1. Enhancing the customer experience with real-time analytics

Each customer interaction, from sales calls to repair requests and service complaints, is a chance to collect and leverage data to improve the experience in the future. Transferring that data from customer sites, regional branches, and customer service centers to a centralized data analysis application takes time, creates network latency, and can make it more difficult to get localized and context-specific insights. Edge computing allows telecom companies to analyze valuable customer experience data, such as network speed, uptime (or downtime) count, and number of support contacts in real-time, providing better opportunities to identify and correct issues before they go on to affect future interactions.

2. Streamlining remote infrastructure management and recovery with AIOps

AIOps helps telecom companies manage complex, distributed network infrastructure more efficiently. AIOps (artificial intelligence for IT operations) uses advanced machine learning algorithms to analyze infrastructure monitoring data and provide maintenance recommendations, automated incident management, and simple issue remediation. Deploying AIOps on edge computing devices at each telecom site enables real-time analysis, detection, and response, helping to reduce the duration of service disruptions. For example, AIOps can perform automated root-cause analysis (RCA) to help identify the source of a regional outage before technicians arrive on-site, allowing them to dive right into the repair. Edge AIOps solutions can also continue functioning even if the site is cut off from the WAN or Internet, potentially self-healing downed networks without the need to deploy repair techs on-site.

3. Preventing environmental conditions from damaging remote equipment

Telecommunications equipment is often deployed in less-than-ideal operating conditions, such as unventilated closets and remote cell site shelters. Heat, humidity, and air particulates can shorten the lifespan of critical equipment or cause expensive service failures, which is why it’s recommended to use environmental monitoring sensors to detect and alert remote technicians to problems. Edge computing applications can analyze environmental monitoring data in real-time and send alerts to nearby personnel much faster than cloud- or data center-based solutions, ensuring major fluctuations are corrected before they damage critical equipment.

4. Improving operational efficiency with network virtualization and consolidation

Another way to reduce management complexity – as well as overhead and operating expenses – is through virtualization and consolidation. Network functions virtualization (NFV) virtualizes networking equipment like load balancers, firewalls, routers, and WAN gateways, turning them into software that can be deployed anywhere – including edge computing devices. This significantly reduces the physical tech stack at each site, consolidating once-complicated network infrastructure into, in some cases, a single device. For example, the Nodegrid Gate SR provides a vendor-neutral edge computing platform that supports third-party NFVs while also including critical edge networking functionality like out-of-band (OOB) serial console management and 5G/4G cellular failover.

Edge computing in telecom: Benefits and best practices

Edge computing can help telecommunications companies:

  • Get actionable insights that can be leveraged in real-time to improve network performance, service reliability, and the support experience.
  • Reduce network latency by processing more data at each site instead of transmitting it to the cloud or data center for analysis.
  • Lower CAPEX and OPEX at each site by consolidating the tech stack and automating management workflows with AIOps.
  • Prevent downtime with real-time analysis of environmental and equipment monitoring data to catch problems before they escalate.
  • Accelerate recovery with real-time, AIOps root-cause analysis and simple incident remediation that continues functioning even if the site is cut off from the WAN or Internet.

Management infrastructure isolation, which is recommended by CISA and required by regulations like DORA, is the best practice for improving edge resilience and ensuring a speedy recovery from failures and breaches. Isolated management infrastructure (IMI) prevents compromised accounts, ransomware, and other threats from moving laterally from production resources to the interfaces used to control critical network infrastructure.

IMI with Nodegrid(2)
To ensure the scalability and flexibility of edge architectures, the best practice is to use vendor-neutral platforms to host, connect, and secure edge applications and workloads. Moving away from dedicated device stacks and taking a “platformization” approach allows organizations to easily deploy, update, and swap out functions and services on demand. For example, Nodegrid edge networking solutions have a Linux-based OS that supports third-party VMs, Docker containers, and NFVs. Telecom companies can use Nodegrid to run edge computing workloads as well as asset management software, customer experience analytics, AIOps, and edge security solutions like SASE.

Vendor-neutral platforms help reduce hardware overhead costs to deploy new edge sites, make it easy to spin-up new NFVs to meet increased demand, and allow telecom organizations to explore different edge software capabilities without costly hardware upgrades. For example, the Nodegrid Gate SR is available with an Nvidia Jetson Nano card that’s optimized for AI workloads, so companies can run innovative artificial intelligence at the edge alongside networking and infrastructure management workloads rather than purchasing expensive, dedicated GPU resources.

Edge-Management-980×653
Finally, to ensure teams have holistic oversight of the distributed edge computing architecture, the best practice is to use a centralized, cloud-based edge management and orchestration (EMO) platform. This platform should also be vendor-neutral to ensure complete coverage and should use out-of-band management to provide continuous management access to edge infrastructure even during a major service outage.

Streamlined, cost-effective edge computing with Nodegrid

Nodegrid’s flexible, vendor-neutral platform adapts to all edge computing use cases in telecom. Watch a demo to see Nodegrid’s telecom solutions in action.

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Edge Computing Use Cases in Retail

by | Jul 25, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Industry Use Cases, Monitoring & Reporting, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, Simplify Branch Infrastructure, Streamline Deployments, User Management, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

Automated transportation robots move boxes in a warehouse, one of many edge computing use cases in retail
Retail organizations must constantly adapt to meet changing customer expectations, mitigate external economic forces, and stay ahead of the competition. Technologies like the Internet of Things (IoT), artificial intelligence (AI), and other forms of automation help companies improve the customer experience and deliver products at the pace demanded in the age of one-click shopping and two-day shipping. However, connecting individual retail locations to applications in the cloud or centralized data center increases network latency, security risks, and bandwidth utilization costs.

Edge computing mitigates many of these challenges by decentralizing cloud and data center resources and distributing them at the network’s “edges,” where most retail operations take place. Running applications and processing data at the edge enables real-time analysis and insights and ensures that systems remain operational even if Internet access is disrupted by an ISP outage or natural disaster. This blog describes five potential edge computing use cases in retail and provides more information about the benefits of edge computing for the retail industry.

5 Edge computing use cases in retail

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1. Security video analysis

Security cameras are crucial to loss prevention, but constantly monitoring video surveillance feeds is tedious and difficult for even the most experienced personnel. AI-powered video surveillance systems use machine learning to analyze video feeds and detect suspicious activity with greater vigilance and accuracy. Edge computing enhances AI surveillance by allowing solutions to analyze video feeds in real-time, potentially catching shoplifters in the act and preventing inventory shrinkage.

2. Localized, real-time insights

Retailers have a brief window to meet a customer’s needs before they get frustrated and look elsewhere, especially in a brick-and-mortar store. A retail store can use an edge computing application to learn about customer behavior and purchasing activity in real-time. For example, they can use this information to rotate the products featured on aisle endcaps to meet changing demand, or staff additional personnel in high-traffic departments at certain times of day. Stores can also place QR codes on shelves that customers scan if a product is out of stock, immediately alerting a nearby representative to provide assistance.

3. Enhanced inventory management

Effective inventory management is challenging even for the most experienced retail managers, but ordering too much or too little product can significantly affect sales. Edge computing applications can improve inventory efficiency by making ordering recommendations based on observed purchasing patterns combined with real-time stocking updates as products are purchased or returned. Retailers can use this information to reduce carrying costs for unsold merchandise while preventing out-of-stocks, improving overall profit margins.
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4. Building management

Using IoT devices to monitor and control building functions such as HVAC, lighting, doors, power, and security can help retail organizations reduce the need for on-site facilities personnel, and make more efficient use of their time. Data analysis software helps automatically optimize these systems for efficiency while ensuring a comfortable customer experience. Running this software at the edge allows automated processes to respond to changing conditions in real-time, for example, lowering the A/C temperature or routing more power to refrigerated cases during a heatwave.

5. Warehouse automation

The retail industry uses warehouse automation systems to improve the speed and efficiency at which goods are delivered to stores or directly to users. These systems include automated storage and retrieval systems, robotic pickers and transporters, and automated sortation systems. Companies can use edge computing applications to monitor, control, and maintain warehouse automation systems with minimal latency. These applications also remain operational even if the site loses internet access, improving resilience.

The benefits of edge computing for retail

The benefits of edge computing in a retail setting include:
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Edge computing benefits

Description

Reduced latency

Edge computing decreases the number of network hops between devices and the applications they rely on, reducing latency and improving the speed and reliability of retail technology at the edge.

Real-time insights

Edge computing can analyze data in real-time and provide actionable insights to improve the customer experience before a sale is lost or reduce waste before monthly targets are missed.

Improved resilience

Edge computing applications can continue functioning even if the site loses Internet or WAN access, enabling continuous operations and reducing the costs of network downtime.

Risk mitigation

Keeping sensitive internal data like personnel records, sales numbers, and customer loyalty information on the local network mitigates the risk of interception and distributes the attack surface.
Edge computing can also help retail companies lower their operational costs at each site by reducing bandwidth utilization on expensive MPLS links and decreasing expenses for cloud data storage and computing. Another way to lower costs is by using consolidated, vendor-neutral solutions to run, connect, and secure edge applications and workloads.

For example, the Nodegrid Gate SR integrated branch services router delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. The open, Linux-based Nodegrid OS supports VMs and Docker containers for third-party edge computing applications, security solutions, and more. The Gate SR is also available with an Nvidia Jetson Nano card that’s optimized for AI workloads to help retail organizations reduce the hardware overhead costs of deploying artificial intelligence at the edge.

Consolidated edge computing with Nodegrid

Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in retail. Watch a demo to see Nodegrid’s retail network solutions in action.

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Edge Computing Use Cases in Healthcare

by | Jul 23, 2024 | Actionable Data, Application Hosting, Consolidation, Edge Computing, EdgeOps, Improve Network Security, Increase Productivity, Micro-segmentation, Monitoring & Reporting, Network Edge Orchestration, Out of Band Management, Remote Network Management, Vendor Neutral Platform, Virtualization, Zero Trust Security

A closeup of an IoT pulse oximeter, one of many edge computing use cases in healthcare
The healthcare industry enthusiastically adopted Internet of Things (IoT) technology to improve diagnostics, health monitoring, and overall patient outcomes. The data generated by healthcare IoT devices is processed and used by sophisticated data analytics and artificial intelligence applications, which traditionally live in the cloud or a centralized data center. Transmitting all this sensitive data back and forth is inefficient and increases the risk of interception or compliance violations.

Edge computing deploys data analytics applications and computing resources around the edges of the network, where much of the most valuable data is created. This significantly reduces latency and mitigates many security and compliance risks. In a healthcare setting, edge computing enables real-time medical insights and interventions while keeping HIPAA-regulated data within the local security perimeter. This blog describes six potential edge computing use cases in healthcare that take advantage of the speed and security of an edge computing architecture.

6 Edge computing use cases in healthcare

Edge computing use cases for EMS

Mobile emergency medical services (EMS) teams need to make split-second decisions regarding patient health without the benefit of a doctorate and, often, with spotty Internet connections preventing access to online drug interaction guides and other tools. Installing edge computing resources on cellular edge routers gives EMS units real-time health analysis capabilities as well as a reliable connection for research and communications. Potential use cases include:
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Use cases

Description

1. Real-time health analysis en route

Edge computing applications can analyze data from health monitors in real-time and access available medical records to help medics prevent allergic reactions and harmful medication interactions while administering treatment.

2. Prepping the ER with patient health insights

Some edge computing devices use 5G/4G cellular to livestream patient data to the receiving hospital, so ER staff can make the necessary arrangements and begin the proper treatment as soon as the patient arrives.

Edge computing use cases in hospitals & clinics

Hospitals and clinics use IoT devices to monitor vitals, dispense medications, perform diagnostic tests, and much more. Sending all this data to the cloud or data center takes time, delaying test results or preventing early intervention in a health crisis, especially in rural locations with slow or spotty Internet access. Deploying applications and computing resources on the same local network enables faster analysis and real-time alerts. Potential use cases include:
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Use cases

Description

3. AI-powered diagnostic analysis

Edge computing allows healthcare teams to use AI-powered tools to analyze imaging scans and other test results without latency or delays, even in remote clinics with limited Internet infrastructure.

4. Real-time patient monitoring alerts

Edge computing applications can analyze data from in-room monitoring devices like pulse oximeters and body thermometers in real-time, spotting early warning signs of medical stress and alerting staff before serious complications arise.

Edge computing use cases for wearable medical devices

Wearable medical devices give patients and their caregivers greater control over health outcomes. With edge computing, health data analysis software can run directly on the wearable device, providing real-time results even without an Internet connection. Potential use cases include:
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Use cases

Description

5. Continuous health monitoring

An edge-native application running on a system-on-chip (SoC) in a wearable insulin pump can analyze levels in real-time and provide recommendations on how to correct imbalances before they become dangerous.

6. Real-time emergency alerts

Edge computing software running on an implanted heart-rate monitor can give a patient real-time alerts when activity falls outside of an established baseline, and, in case of emergency, use cellular and ATT FirstNet connections to notify medical staff.

The benefits of edge computing for healthcare

Using edge computing in a healthcare setting as described in the use cases above can help organizations:

  • Improve patient care in remote settings, where a lack of infrastructure limits the ability to use cloud-based technology solutions.
  • Process and analyze patient health data faster and more reliably, leading to earlier interventions.
  • Increase efficiency by assisting understaffed medical teams with diagnostics, patient monitoring, and communications.
  • Mitigate security and compliance risks by keeping health data within the local security perimeter.

Edge computing can also help healthcare organizations lower their operational costs at the edge by reducing bandwidth utilization and cloud data storage expenses. Another way to reduce costs is by using consolidated, vendor-neutral solutions to host, connect, and secure edge applications and workloads.

For example, the Nodegrid Gate SR is an integrated branch services router that delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. Nodegrid’s open, Linux-based OS supports VMs and Docker containers for third-party edge applications, security solutions, and more. Plus, an onboard Nvidia Jetson Nano card is optimized for AI workloads at the edge, significantly reducing the hardware overhead costs of using artificial intelligence at remote healthcare sites. Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in healthcare, future-proofing your edge architecture.

Streamline your edge deployment with Nodegrid

The vendor-neutral Nodegrid platform consolidates an entire edge technology stack into a unified, streamlined solution. Watch a demo to see Nodegrid’s healthcare network solutions in action.

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Benefits of Edge Computing

by | Jul 18, 2024 | Application Hosting, Consolidation, Edge Computing, Improve Network Security, Increase Productivity, Micro-segmentation, Minimize Impact of Disruptions, Network Edge Orchestration, Remote Network Management, Secure Access Service Edge (SASE), Security Service Edge (SSE), Streamline Deployments, Vendor Neutral Platform, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

An illustration showing various use cases and benefits of edge computing

Edge computing delivers data processing and analysis capabilities to the network’s “edge,” at remote sites like branch offices, warehouses, retail stores, and manufacturing plants. It involves deploying computing resources and lightweight applications very near the devices that generate data, reducing the distance and number of network hops between them. In doing so, edge computing reduces latency and bandwidth costs while mitigating risk, enhancing edge resilience, and enabling real-time insights. This blog discusses the five biggest benefits of edge computing, providing examples and additional resources for companies beginning their edge journey.
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5 benefits of edge computing​

Edge Computing:

Description

Reduces latency

Leveraging data at the edge reduces network hops and latency to improve speed and performance.

Mitigates risk

Keeping data on-site at distributed edge locations reduces the chances of interception and limits the blast radius of breaches.

Lowers bandwidth costs

Reducing edge data transmissions over expensive MPLS lines helps keep branch costs low.

Enhances edge resilience

Analyzing data on-site ensures that edge operations can continue uninterrupted during ISP outages and natural disasters.

Enables real-time insights

Eliminating off-site processing allows companies to use and extract value from data as soon as it’s generated.

1. Reduces latency

Edge computing leverages data on the same local network as the devices that generate it, cutting down on edge data transmissions over the WAN or Internet. Reducing the number of network hops between devices and applications significantly decreases latency, improving the speed and performance of business intelligence apps, AIOps, equipment health analytics, and other solutions that use edge data.

Some edge applications run on the devices themselves, completely eliminating network hops and facilitating real-time, lag-free analysis. For example, an AI-powered surveillance application installed on an IoT security camera at a walk-up ATM can analyze video feeds in real-time and alert security personnel to suspicious activity as it occurs.​

 

Read more examples of how edge computing improves performance in our guide to the Applications of Edge Computing.

2. Mitigates risk

Edge computing mitigates security and compliance risks by distributing an organization’s sensitive data and reducing off-site transmission. Large, centralized data stores in the cloud or data center are prime targets for cybercriminals because the sheer volume of data involved increases the chances of finding something valuable. Decentralizing data in much smaller edge storage solutions makes it harder for hackers to find the most sensitive information and also limits how much data they can access at one time.

Keeping data at the edge also reduces the chances of interception in transit to cloud or data center storage. Plus, unlike in the cloud, an organization maintains complete control over who and what has access to sensitive data, aiding in compliance with regulations like the GDPR and PCI DSS 4.0.
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To learn how to protect edge data and computing resources, read Comparing Edge Security Solutions.

3. Lowers bandwidth costs

Many organizations use MPLS (multi-protocol label switching) links to securely connect edge sites to the enterprise network. MPLS bandwidth is much more expensive than regular Internet lines, which makes transmitting edge data to centralized data processing applications extremely costly. Plus, it can take months to provision MPLS at a new site, delaying launches and driving up overhead expenses.

Edge computing significantly reduces MPLS bandwidth utilization by running data-hungry applications on the local network, reserving the WAN for other essential traffic. Combining edge computing with SD-WAN (software-defined wide area networking) and SASE (secure access service edge) technologies can markedly decrease the reliance on MPLS links, allowing organizations to accelerate branch openings and see faster edge ROIs.
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Learn more about cost-effective edge deployments in our Edge Computing Architecture Guide.

4. Enhances edge resilience

Since edge computing applications run on the same LAN as the devices generating data, they can continue to function even if the site loses Internet access due to an ISP outage, natural disaster, or other adverse event. This also allows uninterrupted edge operations in locations with inconsistent (or no) Internet coverage, like offshore oil rigs, agricultural sites, and health clinics in isolated rural communities. Edge computing ensures that organizations don’t miss any vital health or safety alerts and facilitates technological innovation using AI and other data analytics tools in challenging environments..
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For more information on operational resilience, read Network Resilience: What is a Resilience System?

5. Enables real-time insights

Sending data from the edge to a cloud or on-premises data lake for processing, transformation, and ingestion by analytics or AI/ML tools takes time, preventing companies from acting on insights at the moment when they’re most useful. Edge computing applications start using data as soon as it’s generated, so organizations can extract value from it right away. For example, a retail store can use edge computing to gain actionable insights on purchasing activity and customer behavior in real-time, so they can move in-demand products to aisle endcaps or staff extra cashiers as needed.
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To learn more about the potential uses of edge computing technology, read Edge Computing Examples.

Simplify your edge computing deployment with Nodegrid

The best way to achieve the benefits of edge computing described above without increasing management complexity or hardware overhead is to use consolidated, vendor-neutral solutions to host, connect, and secure edge workloads. For example, the Nodegrid Gate SR from ZPE Systems delivers an entire stack of edge networking and infrastructure management technologies in a single, streamlined device. The open, Linux-based Nodegrid OS supports VMs and containers for third-party applications, with an Nvidia Jetson Nano card capable of running AI workloads alongside non-AI data analytics for ultimate efficiency.

Improve your edge computing deployment with Nodegrid

Nodegrid consolidates edge computing deployments to improve operational efficiency without sacrificing performance or functionality. Schedule a free demo to see Nodegrid in action.

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