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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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Why Securing IT Means Replacing End-of-Life Console Servers

by | Jul 25, 2024 | Data Center Management, Data Center Resilience, Improve Network Security, Increase Productivity, Micro-segmentation, Minimize Impact of Disruptions, Network Automation, Out of Band Management, Remote Network Management, Serial Consoles, User Management, Vendor Neutral Platform, Zero Touch Provisioning (ZTP), Zero Trust Security

Rene Neumann – Why Securing IT Means Replacing End of Life Console Servers

 

The world as we know it is connected to IT, and IT relies on its underlying infrastructure. Organizations must prioritize maintaining this infrastructure; otherwise, any disruption or breach has a ripple effect that takes services offline for millions of users (take 2024’s CrowdStrike outage, for example). A big part of this maintenance is ensuring that all hardware components, including console servers, are up-to-date and secure. Most console servers reach end-of-life (EOL) and need to be replaced, but for many reasons, whether budgetary concerns or the “if it isn’t broken” mentality, IT teams often keep their EOL devices. Let’s look at the risks of using EOL console servers, and why replacing them goes hand-in-hand with securing IT.

The Risks of Using End-of-Life Console Servers

End-of-life console servers can undermine the security and functionality of IT systems. These risks include:

1. Lack of Security Features and Updates

Aging console servers lack adequate hardware and management security features, meaning they can’t support a zero trust approach. On top of this, once a console server reaches EOL, the manufacturer stops providing security patches and updates. The device then becomes vulnerable to newly discovered CVEs and complex cyberattacks (like the MOVEit and Ragnar Locker breaches). Cybercriminals often target outdated hardware because they know that these devices are no longer receiving updates, making them easy entry points for launching attacks.

2. Compliance Issues

Many industries have stringent regulatory requirements regarding data security and IT infrastructure. DORA, NIS2 (EU), NIST2 (US), PCI 4.0 (finance), and CER Directive are just a few of the updated regulations that are cracking down on how organizations architect IT, including the management layer. Using EOL hardware can lead to non-compliance, resulting in fines and legal repercussions. Regulatory bodies expect organizations to use up-to-date and secure equipment to protect sensitive information.

3. Prolonged Recovery

EOL console servers are prone to failures and inefficiencies. As these devices age, their performance deteriorates, leading to increased downtime and disruptions. Most console servers are Gen 2, meaning they offer basic remote troubleshooting (to address break/fix scenarios) and limited automation capabilities. When there is a severe disruption, such as a ransomware attack, hackers can easily access and encrypt these devices to lock out admin access. Organizations then must endure prolonged recovery (like the CrowdStrike outage, or 2023’s MGM attack) because they need to physically decommission and restore their infrastructure.

 

The Importance of Replacing EOL Console Servers

Here’s why replacing EOL console servers is essential to securing IT:

1. Modern Security Approach

Zero trust is an approach that uses segmentation across IT assets. This ensures that only authorized users can access resources necessary for their job function. This approach requires SAML, SSO, MFA/2FA, and role-based access controls, which are only supported by modern console servers. Modern devices additionally feature advanced security through encryption, signed OS, and tampering detection. This ensures a complete cyber and physical approach to security.

2. Protection Against New Threats

New CVEs and evolving threats can easily take advantage of EOL devices that no longer receive updates. Modern console servers benefit from ongoing support in the form of firmware upgrades and security patches. Upgrading with a security-focused device vendor can drastically shrink the attack surface, by addressing supply chain security risks, codebase integrity, and CVE patching.

3. Ease of Compliance

EOL devices lack modern security features, but this isn’t the only reason why they make it difficult or impossible to comply with regulations. They also lack the ability to isolate the control plane from the production network (see Diagram 1 below), meaning attackers can easily move between the two in order to launch ransomware and steal sensitive information. Watchdog agencies and new legislation are stipulating that organizations follow the latest best practice of separating the control plane from production, called Isolated Management Infrastructure (IMI). Modern console servers make this best practice simple to achieve by offering drop-in out-of-band that is completely isolated from production assets (see Diagram 2 below). This means that the organization is always in control of its IT assets and sensitive data.

A network diagram showing Gen 2 out-of-band is vulnerable to the internet

Diagram 1: Though an acceptable approach, Gen 2 out-of-band lacks isolation and leaves management interfaces vulnerable to the internet.

A network diagram showing how Gen 3 out-of-band secures network and management interfaces.

Diagram 2: Gen 3 out-of-band fully isolates the control plane to guarantee organizations retain control of their IT assets and sensitive info.

4. Faster Recovery

New console servers are designed to handle more workloads and functions, which eliminates single-purpose devices and shrinks the attack surface. They can also run VMs and Docker containers to host applications. This enables what Gartner calls the Isolated Recovery Environment (IRE) (see Diagram 3 below), which is becoming essential for faster recovery from ransomware. Since the IMI component prohibits attackers from accessing the control plane, admins retain control during an attack. They can use the IMI to deploy their IRE and the necessary applications — remotely — to decommission, cleanse, and restore their infected infrastructure. This means that they don’t have to roll trucks week after week when there’s an attack; they just need to log into their management infrastructure to begin assessing and responding immediately, which significantly reduces recovery times.

A diagram showing the components of an isolated recovery environment.

Diagram 3: The Isolated Recovery Environment allows for a comprehensive and rapid response to ransomware attacks.

Get a Walkthrough of IMI and IRE

Let’s cover what IMI and IRE would look like in your environment and walk through some outage recovery scenarios. Use the link below to set up a technical discussion.

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Meet Me at Cisco Live Amsterdam 2026

Visit booth C10 at Cisco Live Amsterdam to chat about IMI, IRE, and replacing end-of-life console servers. You can also catch my 10-minute presentation on Wednesday, February 11 at 1:50pm in the Speakers Corner. I’ll cover From Pilot Projects to Global Rollouts: Why Out-of-Band Management is Crucial for Scaling AI Infrastructure, with more concepts and network diagrams showing how to achieve true resilience. Visit our Cisco Live page below to let me know you’re coming. See you at the show!

Rene Neumann presents at Cisco Live Amsterdam 2026
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The CrowdStrike Outage: How to Recover Fast and Avoid the Next Outage

by | Jul 23, 2024 | Consolidation, Data Logging, Improve Network Security, Increase Productivity, Micro-segmentation, Minimize Impact of Disruptions, Network Automation, Out of Band Management, Remote Network Management, Serial Consoles, User Management, Virtualization, Zero Touch Provisioning (ZTP), Zero Trust Security

CrowdStrike Outage BSOD

 

On July 19, 2024, CrowdStrike, a leading cybersecurity firm renowned for its advanced endpoint protection and threat intelligence solutions, experienced a significant outage that disrupted operations for many of its clients. This outage, triggered by a software upgrade, resulted in crashes for Windows PCs, creating a wave of operational challenges for banks, airports, enterprises, and organizations worldwide. This blog post explores what transpired during this incident, what caused the outage, and the broader implications for the cybersecurity industry.

What happened?

The incident began on the morning of July 19, 2024, when numerous CrowdStrike customers started reporting issues with their Windows PCs. Users experienced the BSOD (blue screen of death), which is when Windows crashes and renders devices unusable. As the day went on, it became evident that the problem was widespread and directly linked to a recent software upgrade deployed by CrowdStrike.

Timeline of Events

  1. Initial Reports: Early in the day, airports, hospitals, and critical infrastructure operators began experiencing unexplained crashes on their Windows PCs. The issue was quickly reported to CrowdStrike’s support team.
  2. Incident Acknowledgement: CrowdStrike acknowledged the issue via their social media channels and direct communications with affected clients, confirming that they were investigating the cause of the crashes.
  3. Root Cause Analysis: CrowdStrike’s engineering team worked diligently to identify the root cause of the problem. They soon determined that a software upgrade released the previous night was responsible for the crashes.
  4. Mitigation Efforts: Upon isolating the faulty software update, CrowdStrike issued guidance on how to roll back the update and provided patches to fix the issue.

What caused the CrowdStrike outage?

The root cause of the outage was a software upgrade intended to enhance the functionality and security of CrowdStrike’s Falcon sensor endpoint protection platform. However, this upgrade contained a bug that conflicted with certain configurations of Windows PCs, leading to system crashes. Several factors contributed to the incident:

  1. Insufficient Testing: The software update did not undergo adequate testing across all possible configurations of Windows PCs. This oversight meant that the bug was not detected before the update was deployed to customers.
  2. Complex Interdependencies: The incident highlights the complex interdependencies between software components and operating systems. Even minor changes can have unforeseen impacts on system stability.
  3. Rapid Deployment: In the cybersecurity industry, quick responses to emerging threats are crucial. However, the pressure to deploy updates rapidly can sometimes lead to insufficient testing and quality assurance processes.

We need to remember one important fact: whether software is written by humans or AI, there will be mistakes in coding and testing. When an issue slips through the cracks, the customer lab is the last resort to catch it. Usually, this can be done with a controlled rollout, where the IT team first upgrades their lab equipment, performs further testing, puts in place a rollback plan, and pushes the update to a less critical site. But in a cloud-connected SaaS world, the customer is no longer in control. That’s why they sign waivers stating that if such an incident occurs, the company that caused the problem is not liable. Experts are saying the only way to address this challenge is to have an infrastructure that’s designed, deployed, and operated for resilience. We discuss this architecture further down in this article.

How to recover from the CrowdStrike outage

CrowdStrike gives two options for recovering:

  • Option 1: Reboot in Safe Mode – Reboot the affected device in Safe Mode, locate and delete the file “C-00000291*.sys”, and then restart the device.
  • Option 2: Re-image – Download and configure the recovery utility to create a new Windows image, add this image to a USB drive, and then insert this USB drive into the target device. The utility will automatically find and delete the file that’s causing the crash.

The biggest obstacle that is costing organizations a lot of time and money is that with either of these recovery methods, IT staff need to be physically present to work on each affected device. They need to go one by one manually remediating via Safe Mode or physically inserting the USB drive. What makes this more difficult is that many organizations use physical and software/management security controls to limit access. Locked device cabinets slow down physical access to devices, and things like role-based access policies and disk encryption can make Safe Mode unusable. Because this outage is affecting more than 8.5 million computers, this kind of work won’t scale efficiently. That’s why organizations are turning to Isolated Management Infrastructure (IMI) and the Isolated Recovery Environment (IRE).

How IMI and IRE help you recover faster

IMI is a dedicated control plane network that’s meant for administration and recovery of IT systems, including Windows PCs affected by the CrowdStrike outage. It uses the concept of out-of-band management, where you deploy a management device that is connected to dedicated management ports of your IT infrastructure (e.g., serial ports, IPMI ports, and other ethernet management ports). IMI also allows you to deploy recovery services for your digital estate that is immutable and near-line when recovery needs to take place.

IMI does not rely at all on the production assets, as it has its own dedicated remote access via WAN links like 4G/5G, and can contain and encrypt recovery keys and tools with zero trust.

IMI gives teams remote, low-level access to devices so they can recover their systems remotely without the need to visit sites. Organizations that employ IMI are able to revert back to a golden image through automation, or deploy bootable tools to all the computers at the site to rescue them without data loss.

The dedicated out-of-band access to serial/IPMI and management ports gives automation software the same abilities as if a physical crash cart was pulled up to the servers. ZPE Systems’ Nodegrid (now a brand of Legrand) enables this architecture as explained next. Using Nodegrid and ZPE Cloud, teams can use either option to recover from the CrowdStrike outage:

  • Option 1: Reboot in Pre-Execution Environment Software – Nodegrid gives low-level network access to connected Windows as if teams were sitting directly in front of the affected device. This means they can remote-in, reboot to a network image, remote into the booted image, delete the faulty file, and restart the system.
  • Option 2: Re-image – ZPE Cloud serves as a file repository and orchestration engine. Teams can upload their working Windows image, and then automatically push this across their global fleet of affected devices. This option speeds up recovery times exponentially.
  • Option 3: – Run Windows Deployment server on the IMI device at the location and re-image servers and workstations if a good backup of the data has been located. This backup can be made available through the IMI after the initial image has been deployed. The IMI can provide dedicated secure access to the InTune services in your M365 cloud, and the backups do not have to transit the entire internet for all workstations at the time, speeding up recovery many times over.

All of these options can be performed at scale or even automated. Server recovery with large backups, although it may take a couple of hours, can be delivered locally and tracked for performance and consistency.

But what about the risk of making mistakes when you have to repeat these tasks? Won’t this cause more damage and data loss?

Any team can make a mistake repeating these recovery tasks over a large footprint, and cause further damage or loss of data, slowing the recovery further. Automated recovery through the IMI addresses this, and can provide reliable recording and reporting to ensure that the restoration is complete and trusted. 

What does IMI look like?

Here’s a simplified view of Isolated Management Infrastructure. You can see that ZPE’s Nodegrid device is needed, which sits beside production infrastructure and provides the platform for hosting all the tools necessary for fast recovery.

A diagram showing how to use Nodegrid Gen 3 OOB to enable IMI.

What you need to deploy IMI for recovery:

  1. Out-of-band appliance with serial, USB, ethernet interfaces (e.g., ZPE’s Nodegrid Net SR)
  2. Switchable PDU: Legrand Server Tech or Raritan PDU
  3. Windows PXE Boot image

Here’s the order of operations for a faster CrowdStrike outage recovery:

  • Option 1 – Recover
    1. IMI deployed with a ZPE Nodegrid device that will start Pre-Execution Environment (PXE) which are Windows boot images that the Nodegrid will push to the computers when they boot up
    2. Send recovery keys from Intune to IMI remote storage over ZPE Cloud’s zero trust platform easily available in cloud or air-gapped through Nodegrid Manager
    3. Enable PXE service (automated across entire enterprise) and define the PXE recovery image
    4. Use serial or IP control of power to the computers, or if possible Intel vPro or IPMI capable machines, to reboot all machines
    5. All machines will boot and check in to a control tower for PXE, or be made available to remote into using stored passwords on the PXE environment, Windows AD, or other Privileged Access Management (PAM)
    6. Delete Files
    7. Reboot

 

  • Option 2 – Lean re-image
    1. IMI deployed with a Windows Pre-Execution boot image running PXE service
    2. Enable access to cloud and Azure Intune to the IMI remote storage for the local image for the PC
    3. Enable PXE service (automated across entire enterprise) and define the PXE recovery image
    4. Use serial or IP control of power to the computers, or if possible, Intel vPro or IPMI capable machines, to reboot all machines
    5. Machines will boot and check in to Intune either through the IMI or through normal Internet access and finish imaging
    6. Once the machine completes the InTune tasks, InTune will signal backups to come down to the machines. If these backups are offsite, they can be staged on the IMI through backup software running on a virtual machine located on the IMI appliance to speed up recovery and not impede the Internet connection at the remote site
    7. Pre-stage backups onto local storage, push recovery from the virtual machine on the IMI

 

  • Option 3 – Windows controlled re-image
    1. Windows Deployment Server (WDS) installed as a virtual machine running on the IMI appliance (offline to prevent issues or online but under a slowed deployment cycle in case there was an issue) 
    2. Send recovery keys from Intune to IMI remote storage over a zero trust interface in cloud or air-gapped
    3. Use serial or IP control of power to the computers, or if possible, Intel vPro or IPMI capable machines, to reboot all machines
    4. Machines will boot and check in to the WDS for re-imaging
    5. Machines will boot and check in to Intune either through the IMI or through normal Internet access and finish imaging
    6. Once the machine completes the InTune tasks, InTune will signal backups to come down to the machines. If these backups are offsite, they can be staged on the IMI through backup software running on a virtual machine located on the IMI appliance to speed up recovery and not impede the Internet connection at the remote site
    7. Pre-stage backups onto local storage, push recovery from the virtual machine on the IMI

Deploy IMI to avoid the next outage

Get in touch for help choosing the right size IMI deployment for your organization. Nodegrid and ZPE Cloud are the drop-in solution to recovering from outages, with plenty of device options to fit any budget and environment size. Contact ZPE Sales now or download the blueprint to help you begin implementing IMI.

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Comparing Edge Security Solutions

by | Jul 10, 2024 | Application Hosting, EdgeOps, Improve Network Security, Modernize Legacy Environments, Network Automation, Out of Band Management, Remote Network Management, SD-Branch, Secure Access Service Edge (SASE), Security Service Edge (SSE), Streamline Deployments, Vendor Neutral Platform

A user at an edge site with a virtual overlay of SASE and related edge security concepts
The continuing trend of enterprise network decentralization to support Internet of Things (IoT) deployments, automation, and edge computing is resulting in rapid growth for the edge security market. Recent research predicts it will reach $82.4 billion by 2031 at a compound annual growth rate (CAGR) of 19.7% from 2024.

Edge security solutions decentralize the enterprise security stack, delivering key firewall capabilities to the network’s edges. This prevents companies from funneling all edge traffic through a centralized data center firewall, reducing latency and improving overall performance.

This guide compares the most popular edge security solutions and offers recommendations for choosing the right vendor for your use case.

Executive summary

There are six single-vendor SASE solutions offering the best combination of features and capabilities for their targeted use cases.
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Single-Vendor SASE Product

Key Takeaways

Palo Alto Prisma SASE

Prisma SASE’s advanced feature set, high price tag, and granular controls make it well-suited to larger enterprises with highly distributed networks, complex edge operations, and personnel with previous SSE and SD-WAN experience.

Zscaler Zero Trust SASE

Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and feature roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology (OT) deployments.

Netskope ONE

Netskope ONE’s flexible options allow mid-sized companies to take advantage of advanced SASE features without paying a premium for the services they don’t need, though the learning curve may be a bit steep for inexperienced teams.

Cisco

Cisco Secure Connect makes SASE more accessible to smaller, less experienced IT teams, though its high price tag could be prohibitive to these companies. Cisco’s unmanaged SASE solutions integrate easily with existing Cisco infrastructures, but they offer less flexibility in the choice of features than other options on this list.

Forcepoint ONE

Forcepoint’s data-focused platform and deep visibility make it well-suited for organizations with complicated data protection needs, such as those operating in the heavily regulated healthcare, finance, and defense industries. However, Forcepoint ONE has a steep learning curve, and integrating other services can be challenging. 

Fortinet FortiSASE

FortiSASE provides comprehensive edge security functionality for large enterprises hoping to consolidate their security operations with a single platform. However, the speed of some dashboards and features – particularly those associated with the FortiMonitor DEM software – could be improved for a better administrative experience.

The best edge security solution for Gen 3 out-of-band (OOB) management, which is critical for infrastructure isolation, resilience, and operational efficiency, is Nodegrid from ZPE Systems. Nodegrid provides secure hardware and software to host other vendors’ tools on a secure, Gen 3 OOB network. It creates a control plane for edge infrastructure that’s completely isolated from breaches on the production network and consolidates an entire edge networking stack into a single solution. Disclaimer: This comparison was written by a third party in collaboration with ZPE Systems using publicly available information gathered from data sheets, admin guides, and customer reviews on sites like Gartner Peer Insights, as of 6/09/2024. Please email us if you have corrections or edits, or want to review additional attributes, at matrix@zpesystems.com.

What are edge security solutions?

Edge security solutions primarily fall into one (or both) of two categories:

  • Security Service Edge (SSE) solutions deliver core security features as a managed service. SSE does not come with any networking capabilities, so companies still need a way to securely route edge traffic through the (often cloud-based) security stack. This usually involves software-defined wide area networking (SD-WAN), which was traditionally a separate service that had to be integrated with the SSE stack.
  • Secure Access Service Edge (SASE) solutions package SSE together with SD-WAN, preventing companies from needing to deploy and manage multiple vendor solutions.

All the top SSE providers now offer fully integrated SASE solutions with SD-WAN. SASE’s main tech stack is in the cloud, but organizations must install SD-WAN appliances at each branch or edge data center. SASE also typically uses software agents deployed at each site and, in some cases, on all edge devices. Some SASE vendors also sell physical appliances, while others only provide software licenses for virtualized SD-WAN solutions. A third category of edge security solutions offers a secure platform to run other vendors’ SD-WAN and SASE software. These solutions also provide an important edge security capability: management network isolation. This feature ensures that ransomware, viruses, and malicious actors can’t jump from compromised IoT devices to the management interfaces used to control vital edge infrastructure.

Comparing edge security solutions

Palo Alto Prisma SASE

A screenshot from the Palo Alto Prisma SASE solution. Palo Alto Prisma was named a Leader in Gartner’s 2023 SSE Magic Quadrant for its ability to deliver best-in-class security features. Prisma SASE is a cloud-native, AI-powered solution with the industry’s first native Autonomous Digital Experience Management (ADEM) service. Prisma’s ADEM has built-in AIOps for automatic incident detection, diagnosis, and remediation, as well as self-guided remediation to streamline the end-user experience. Prisma SASE’s advanced feature set, high price tag, and granular controls make it well-suited to larger enterprises with highly distributed networks, complex edge operations, and personnel with previous SSE and SD-WAN experience.

Palo Alto Prisma SASE Capabilities:

  • Zero Trust Network Access (ZTNA) 2.0 – Automated app discovery, fine-grained access controls, continuous trust verification, and deep security inspection.
  • Cloud Secure Web Gateway (SWG) – Inline visibility and control of web and SaaS traffic.
  • Next-Gen Cloud Access Security Broker (CASB) – Inline and API-based security controls and contextual policies.
  • Remote Browser Isolation (RBI) – Creates a secure isolation channel between users and remote browsers to prevent web threats from executing on their devices.
  • App acceleration – Application-aware routing to improve “first-mile” connection performance.
  • Prisma Access Browser – Policy management for edge devices.
  • Firewall as a Service (FWaaS) – Advanced threat protection, URL filtering, DNS security, and other next-generation firewall (NGFW) features.
  • Prisma SD-WAN – Elastic networks, app-defined fabric, and Zero Trust security.

Zscaler Zero Trust SASE

Zscaler is another 2023 SSE Magic Quadrant Leader offering a robust single-vendor SASE solution based on its Zero Trust ExchangeTM platform. Zscaler SASE uses artificial intelligence to boost its SWG, firewall, and DEM capabilities. It also offers IoT device management and OT privileged access management, allowing companies to secure unmanaged devices and provide secure remote access to industrial automation systems and other operational technology. Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and future roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology deployments.

Zscaler Zero Trust SASE Capabilities:

  • Zscaler Internet AccessTM (ZIA) – SWG cyberthreat protection and zero-trust access to SaaS apps and the web.
  • Zscaler Private AccessTM (ZPA) – ZTNA connectivity to private apps and OT devices.
  • Zscaler Digital ExperienceTM (ZDX) –  DEM with Microsoft Copilot AI to streamline incident management.
  • Zscaler Data Protection – CASB/DLP secures edge data across platforms.
  • IoT device visibility – IoT device, server, and unmanaged user device discovery, monitoring, and management.
  • Privileged OT access – Secure access management for third-party vendors and remote user connectivity to OT systems.
  • Zero Trust SD-WAN – Works with the Zscaler Zero Trust Exchange platform to secure edge and branch traffic.

Netskope ONE

Netskope is the only 2023 SSE Magic Quadrant Leader to offer a single-vendor SASE targeted to mid-market companies with smaller budgets as well as larger enterprises. The Netskope ONE platform provides a variety of security features tailored to different deployment sizes and requirements, from standard SASE offerings like ZTNA and CASB to more advanced capabilities such as AI-powered threat detection and user and entity behavior analytics (UEBA). Netskope ONE’s flexible options allow mid-sized companies to take advantage of advanced SASE features without paying a premium for the services they don’t need, though the learning curve may be a bit steep for inexperienced teams.

Netskope ONE Capabilities:

  • Next-Gen SWG – Protection for cloud services, applications, websites, and data.
  • CASB – Security for both managed and unmanaged cloud applications.
  • ZTNA Next –  ZTNA with integrated software-only endpoint SD-WAN.
  • Netskope Cloud Firewall (NCF) – Outbound network traffic security across all ports and protocols.
  • RBI – Isolation for uncategorized and risky websites.
  • SkopeAI – AI-powered threat detection, UEBA, and DLP
  • Public Cloud Security – Visibility, control, and compliance for multi-cloud environments.
  • Advanced analytics – 360-degree risk analysis.
  • Cloud Exchange – Multi-cloud integration tools.
  • DLP – Sensitive data discovery, monitoring, and protection.
  • Device intelligence – Zero trust device discovery, risk assessment, and management.
  • Proactive DEM – End-to-end visibility and real-time insights.
  • SaaS security posture management – Continuous monitoring and enforcement of SaaS security settings, policies, and best practices.
  • Borderless SD-WAN – Zero trust connectivity for edge, branch, cloud, remote users, and IoT devices.

Cisco

Cisco is one of the only edge security vendors to offer SASE as a managed service for companies with lean IT operations and a lack of edge networking experience. Cisco Secure Connect SASE-as-a-service includes all the usual SSE capabilities, such as ZTNA, SWG, and CASB, as well as native Meraki SD-WAN integration and a generative AI assistant. Cisco also provides traditional SASE by combining Cisco Secure Access SSE – which includes the Cisco Umbrella Secure Internet Gateway (SIG) – with Catalyst SD-WAN. Cisco Secure Connect makes SASE more accessible to smaller, less experienced IT teams, though its high price tag could be prohibitive to these companies. Cisco’s unmanaged SASE solutions integrate easily with existing Cisco infrastructures, but they offer less flexibility in the choice of features than other options on this list.

Cisco Secure Connect SASE-as-a-Service Capabilities:

  • Clientless ZTNA
  • Client-based Cisco AnyConnect secure remote access
  • SWG
  • Cloud-delivered firewall
  • DNS-layer security
  • CASB
  • DLP
  • SAML user authentication
  • Generative AI assistant
  • Network interconnect intelligent routing
  • Native Meraki SD-WAN integration
  • Unified management

Cisco Secure Access SASE Capabilities

  • ZTNA 
  • SWG
  • CASB
  • DLP
  • FWaaS
  • DNS-layer security
  • Malware protection
  • RBI
  • Catalyst SD-WAN

Forcepoint ONE

A screenshot from the Forcepoint ONE SASE solution. Forcepoint ONE is a cloud-native single-vendor SASE solution placing a heavy emphasis on edge and multi-cloud visibility. Forcepoint ONE aggregates live telemetry from all Forcepoint security solutions and provides visualizations, executive summaries, and deep insights to help companies improve their security posture. Forcepoint also offers what they call data-first SASE, focusing on protecting data across edge and cloud environments while enabling seamless access for authorized users from anywhere in the world. Forcepoint’s data-focused platform and deep visibility make it well-suited for organizations with complicated data protection needs, such as those operating in the heavily regulated healthcare, finance, and defense industries. However, Forcepoint ONE has a steep learning curve, and integrating other services can be challenging.

Forcepoint ONE Capabilities:

  • CASB – Access control and data security for over 800,000 cloud apps on managed and unmanaged devices.
  • ZTNA – Secure remote access to private web apps.
  • SWG – Includes RBI, content disarm & reconstruction (CDR), and a cloud firewall.
  • Data Security – A cloud-native DLP to help enforce compliance across clouds, apps, emails, and endpoints.
  • Insights – Real-time analysis of live telemetry data from Forcepoint ONE security products.
  • FlexEdge SD-WAN – Secure access for branches and remote edge sites.

Fortinet FortiSASE

Fortinet’s FortiSASE platform combines feature-rich, AI-powered NGFW security functionality with SSE, digital experience monitoring, and a secure SD-WAN solution. Fortinet’s SASE offering includes the FortiGate NGFW delivered as a service, providing access to FortiGuard AI-powered security services like antivirus, application control, OT security, and anti-botnet protection. FortiSASE also integrates with the FortiMonitor DEM SaaS platform to help organizations optimize endpoint application performance. FortiSASE provides comprehensive edge security functionality for large enterprises hoping to consolidate their security operations with a single platform. However, the speed of some dashboards and features – particularly those associated with the FortiMonitor DEM software – could be improved for a better administrative experience.

Fortinet FortiSASE Capabilities:

  • Antivirus – Protection from the latest polymorphic attacks, ransomware, viruses, and other threats.
  • DLP – Prevention of intentional and accidental data leaks.
  • AntiSpam – Multi-layered spam email filtering.
  • Application Control – Policy creation and management for enterprise and cloud-based applications.
  • Attack Surface Security – Security Fabric infrastructure assessments based on major security and compliance frameworks.
  • CASB – Inline and API-based cloud application security.
  • DNS Security – DNS traffic visibility and filtering.
  • IPS – Deep packet inspection (DPI) and SSL inspection of network traffic.
  • OT Security – IPS for OT systems including ICS and SCADA protocols.
  • AI-Based Inline Malware Prevention – Real-time protection against zero-day exploits and sophisticated, novel threats.
  • URL Filtering – AI-powered behavior analysis and correlation to block malicious URLs.
  • Anti-Botnet and C2 – Prevention of unauthorized communication attempts from compromised remote servers.
  • FortiMonitor DEM – SaaS-based digital experience monitoring.
  • Secure SD-WAN – On-premises and cloud-based SD-WAN integrated into the same OS as the SSE security solutions.

Edge isolation and security with ZPE Nodegrid

The Nodegrid platform from ZPE Systems is a different type of edge security solution, providing secure hardware and software to host other vendors’ tools on a secure, Gen 3 out-of-band (OOB) management network. Nodegrid integrated branch services routers use alternative network interfaces (including 5G/4G LTE) and serial console technology to create a control plane for edge infrastructure that’s completely isolated from breaches on the production network. It uses hardware security features like secure boot and geofencing to prevent physical tampering, and it supports strong authentication methods and SAML integrations to protect the management network. A screenshot from the Forcepoint ONE SASE solution. Nodegrid’s OOB also ensures remote teams have 24/7 access to manage, troubleshoot, and recover edge deployments even during a major network outage or ransomware infection. Plus, Nodegrid’s ability to host Guest OS, including Docker containers and VNFs, allows companies to consolidate an entire edge networking stack in a single platform. Nodegrid devices like the Gate SR with Nvidia Jetson Nano can even run edge computing and AI/ML workloads alongside SASE. .

ZPE Nodegrid Edge Security Capabilities

  • Vendor-neutral platform – Hosting for third-party applications and services, including Docker containers and virtualized network functions.
  • Gen 3 OOB – Management interface isolation and 24/7 remote access during outages and breaches.
  • Branch networking – Routing and switching, VNFs, and software-defined branch networking (SD-Branch).
  • Secure boot – Password-protected BIO/Grub and signed software.
  • Latest kernel & cryptographic modules – 64-bit OS with current encryption and frequent security patches.
  • SSO with SAML, 2FA, & remote authentication – Support for Duo, Okta, Ping, and ADFS.
  • Geofencing – GPS tracking with perimeter crossing detection.
  • Fine-grain authorization – Role-based access control.
  • Firewall – Native IPSec & Fail2Ban intrusion prevention and third-party extensibility.
  • Tampering protection – Configuration checksum and change detection with a configuration ‘reset’ button.
  • TPM encrypted storage – Software encryption for SSD hardware storage.

Deploy edge security solutions on the vendor-neutral Nodegrid OOB platform

Nodegrid’s secure hardware and vendor-neutral OS make it the perfect platform for hosting other vendors’ SSE, SD-WAN, and SASE solutions. Reach out today to schedule a free demo.

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Applications of Edge Computing

by | Jul 9, 2024 | Application Hosting, Edge Computing, Increase Productivity, Modernize Legacy Environments, Network Automation, Network Edge Orchestration, Out of Band Management, Remote Network Management, SD-Branch, SD-WAN, Streamline Deployments, Vendor Neutral Platform, Zero Touch Provisioning (ZTP)

A healthcare worker presents various edge computing concepts to highlight some of the applications of edge computing

The edge computing market is huge and continuing to grow. A recent study projected that spending on edge computing will reach $232 billion in 2024. Organizations across nearly every industry are taking advantage of edge computing’s real-time data processing capabilities to get immediate business insights, respond to issues at remote sites before they impact operations, and much more. This blog discusses some of the applications of edge computing for industries like finance, retail, and manufacturing, and provides advice on how to get started.

What is edge computing?

Edge computing involves decentralizing computing capabilities and moving them to the network’s edges. Doing so reduces the number of network hops between data sources and the applications that process and use that data, which mitigates latency, bandwidth, and security concerns compared to cloud or on-premises computing.

Learn more about edge computing vs cloud computing or edge computing vs on-premises computing.

Edge computing often uses edge-native applications that are built from the ground up to harness edge computing’s unique capabilities and overcome its limitations. Edge-native applications leverage some cloud-native principles, such as containers, microservices, and CI/CD. However, unlike cloud-native apps, they’re designed to process transient, ephemeral data in real time with limited computational resources. Edge-native applications integrate seamlessly with the cloud, upstream resources, remote management, and centralized orchestration, but can also operate independently as needed.
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Applications of edge computing

Industry

Applications

Financial services

  • Mitigate security and compliance risks of off-site data transmission

  • Gain real-time customer and productivity insights

  • Analyze surveillance footage in real-time

Industrial manufacturing

  • Monitor and respond to OT equipment issues in real-time

  • Create more efficient maintenance schedules

  • Prevent network outages from impacting production

Retail operations

  • Enhance the in-store customer experience

  • Improve inventory management and ordering

  • Aid loss prevention with live surveillance analysis

Healthcare

  • Monitor and respond to patient health issues in real-time

  • Mitigate security and compliance risks by keeping data on-site

  • Reduce networking requirements for wearable sensors

Oil, gas, & mining

  • Ensure continuous monitoring even during network disruptions

  • Gain real-time safety, maintenance, and production recommendations

  • Enable remote troubleshooting and recovery of IT systems

AI & machine learning

  • Reduce the costs and risks of high-volume data transmissions

  • Unlock near-instantaneous AI insights at the edge

  • Improve AIOps efficiency and resilience at branches

Financial services

The financial services industry collects a lot of edge data from bank branches, web and mobile apps, self-service ATMs, and surveillance systems. Many firms feed this data into AI/ML-powered data analytics software to gain insights into how to improve their services and generate more revenue. Some also use AI-powered video surveillance systems to analyze video feeds and detect suspicious activity. However, there are enormous security, regulatory, and reputational risks involved in transmitting this sensitive data to the cloud or an off-site data center.

Financial institutions can use edge computing to move data analytics applications to branches and remote PoPs (points of presence) to help mitigate the risks of transmitting data off-site. Additionally, edge computing enables real-time data analysis for more immediate and targeted insights into customer behavior, branch productivity, and security. For example, AI surveillance software deployed at the edge can analyze live video feeds and alert on-site security personnel about potential crimes in progress.

Industrial manufacturing

Many industrial manufacturing processes are mostly (if not completely) automated and overseen by operational technology (OT), such as supervisory control and data acquisition systems (SCADA). Logs from automated machinery and control systems are analyzed by software to monitor equipment health, track production costs, schedule preventative maintenance, and perform quality assurance (QA) on components and products. However, transferring that data to the cloud or centralized data center increases latency and creates security risks.

Manufacturers can use edge computing to analyze OT data in real time, gaining faster insights and catching potential issues before they affect product quality or delivery schedules. Edge computing also allows industrial automation and monitoring processes to continue uninterrupted even if the site loses Internet access due to an ISP outage, natural disaster, or other adverse event in the region. Edge resilience can be further improved by deploying an out-of-band (OOB) management solution like Nodegrid that enables control plane/data plane isolation (also known as isolated management infrastructure), as this will give remote teams a lifeline to access and recover OT systems.

Retail operations

In the age of one-click online shopping, the retail industry has been innovating with technology to enhance the in-store experience, improve employee productivity, and keep operating costs down. Retailers have a brief window of time to meet a customer’s needs before they look elsewhere, and edge computing’s ability to leverage data in real time is helping address that challenge. For example, some stores place QR codes on shelves that customers can scan if a product is out of stock, alerting a nearby representative to provide immediate assistance.

Another retail application of edge computing is enhanced inventory management. An edge computing solution can make ordering recommendations based on continuous analysis of purchasing patterns over time combined with real-time updates as products are purchased or returned. Retail companies, like financial institutions, can also use edge AI/ML solutions to analyze surveillance data and aid in loss prevention.

Healthcare

The healthcare industry processes massive amounts of data generated by medical equipment like insulin pumps, pacemakers, and imaging devices. Patient health data can’t be transferred over the open Internet, so getting it to the cloud or data center for analysis requires funneling it through a central firewall via MPLS (for hospitals, clinics, and other physical sites), overlay networks, or SD-WAN (for wearable sensors and mobile EMS devices). This increases the number of network hops and creates a traffic bottleneck that prevents real-time patient monitoring and delays responses to potential health crises.

Edge computing for healthcare allows organizations to process medical data on the same local network, or even the same onboard chip, as the sensors and devices that generate most of the data. This significantly reduces latency and mitigates many of the security and compliance challenges involved in transmitting regulated health data offsite. For example, an edge-native application running on an implanted heart-rate monitor can operate without a network connection much of the time, providing the patient with real-time alerts so they can modify their behavior as needed to stay healthy. If the app detects any concerning activity, it can use multiple cellular and ATT FirstNet connections to alert the cardiologist without exposing any private patient data.

Oil, gas, & mining

Oil, gas, and other mining operations use IoT sensors to monitor flow rates, detect leaks, and gather other critical information about equipment deployed in remote sites, drilling rigs, and offshore platforms all over the world. Drilling rigs are often located in extremely remote or even human-inaccessible locations, so ensuring reliable communications with monitoring applications in the cloud or data center can be difficult. Additionally, when networks or systems fail, it can be time-consuming and expensive – not to mention risky – to deploy IT teams to fix the issue on-site.

The energy and mining industries can use edge computing to analyze data in real time even in challenging deployment environments. For example, companies can deploy monitoring software on cellular-enabled edge computing devices to gain immediate insights into equipment status, well logs, borehole logs, and more. This software can help establish more effective maintenance schedules, uncover production inefficiencies, and identify potential safety issues or equipment failures before they cause larger problems. Edge solutions with OOB management also allow IT teams to fix many issues remotely, using alternative cellular interfaces to provide continuous access for troubleshooting and recovery.

AI & machine learning

Artificial intelligence (AI) and machine learning (ML) have broad applications across many industries and use cases, but they’re all powered by data. That data often originates at the network’s edges from IoT devices, equipment sensors, surveillance systems, and customer purchases. Securely transmitting, storing, and preparing edge data for AI/ML ingestion in the cloud or centralized data center is time-consuming, logistically challenging, and expensive. Decentralizing AI/ML’s computational resources and deploying them at the edge can significantly reduce these hurdles and unlock real-time capabilities.

For example, instead of deploying AI on a whole rack of GPUs (graphics processing units) in a central data center to analyze equipment monitoring data for all locations, a manufacturing company could use small edge computing devices to provide AI-powered analysis for each individual site. This would reduce bandwidth costs and network latency, enabling near-instant insights and providing an accelerated return on the investment into artificial intelligence technology.

AIOps can also be improved by edge computing. AIOps solutions analyze monitoring data from IT devices, network infrastructure, and security solutions and provide automated incident management, root-cause analysis, and simple issue remediation. Deploying AIOps on edge computing devices enables real-time issue detection and response. It also ensures continuous operation even if an ISP outage or network failure cuts off access to the cloud or central data center, helping to reduce business disruptions at vital branches and other remote sites.

Getting started with edge computing

The edge computing market has focused primarily on single-use-case solutions designed to solve specific business problems, forcing businesses to deploy many individual applications across the network. This piecemeal approach to edge computing increases management complexity and risk while decreasing operational efficiency.

The recommended approach is to use a centralized edge management and orchestration (EMO) platform to monitor and control edge computing operations. The EMO should be vendor-agnostic and interoperate with all the edge computing devices and edge-native applications in use across the organization. The easiest way to ensure interoperability is to use vendor-neutral edge computing platforms to run edge-native apps and AI/ML workflows.

For example, the Nodegrid platform from ZPE Systems provides the perfect vendor-neutral foundation for edge operations. Nodegrid integrated branch services routers like the Gate SR with integrated Nvidia Jetson Nano use the open, Linux-based Nodegrid OS, which can host Docker containers and edge-native applications for third-party AI, ML, data analytics, and more. These devices use out-of-band management to provide 24/7 remote visibility, management, and troubleshooting access to edge deployments, even in challenging environments like offshore oil rigs. Nodegrid’s cloud-based or on-premises software provides a single pane of glass to orchestrate operations at all edge computing sites.

Streamline your edge computing deployment with Nodegrid

The vendor-neutral Nodegrid platform can simplify all applications of edge computing with easy interoperability, reduced hardware overhead, and centralized edge management and orchestration. Schedule a Nodegrid demo to learn more.
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Edge Computing Examples

by | Jun 21, 2024 | Application Hosting, Edge Computing, Modernize Legacy Environments, Network Automation, Out of Band Management, Remote Network Management, SD-Branch, Security Service Edge (SSE), Simplify Branch Infrastructure, Streamline Deployments, Vendor Neutral Platform, Zero Touch Provisioning (ZTP)

Interlocking cogwheels containing icons of various edge computing examples are displayed in front of racks of servers

The edge computing market is growing fast, with experts predicting edge computing spending to reach almost $350 billion in 2027. Companies use edge computing to leverage data from Internet of Things (IoT) sensors and other devices at the periphery of the network in real-time, unlocking faster insights, accelerating ROIs for artificial intelligence and machine learning investments, and much more. This blog highlights 7 edge computing examples from across many different industries and provides tips and best practices for each use case.

What is edge computing?

Edge computing involves moving compute capabilities – processing units, RAM, storage, data analysis software, etc. – to the network’s edges. This allows companies to analyze or otherwise use edge data in real-time, without transmitting it to a central data center or the cloud.

Edge Computing Learning Center

Edge computing shortens the physical and logical distance between data-generating devices and the applications that use that data, which reduces bandwidth costs and network latency while simplifying many aspects of data security and compliance.

7 Edge computing examples

Below are 7 examples of how organizations use edge computing, along with best practices for overcoming the typical challenges involved in each use case. Click the links in the table for more information about each example.

Examples Best Practices
Monitoring inaccessible equipment in the oil & gas industry Use a vendor-neutral edge computing & networking platform to reduce the tech stack at each site.
Remotely managing and securing automated Smart buildings Isolate the management interfaces for automated building management systems from production to reduce risk.
Analyzing patient health data generated by mobile devices Protect patient privacy with strong hardware roots-of-trust, Zero Trust Edge integrations, and control plane/data plane separation.
Reducing latency for live streaming events and online gaming Use all-in-one, vendor-neutral devices to minimize hardware overhead and enable cost-effective scaling.
Improving performance and business outcomes for AI/ML Streamline operations by using a vendor-neutral platform to remotely monitor and orchestrate edge AI/ML deployments.
Enhancing remote surveillance capabilities at banks and ATMs Isolate the management interfaces for all surveillance systems using Gen 3 OOB to prevent compromise.
Extending data analysis to agriculture sites with limited Internet access Deploy edge gateway routers with environmental sensors to monitor operating conditions and prevent equipment failures.

1. Monitoring and managing inaccessible equipment in the oil and gas industry

The oil and gas industry uses IoT sensors to monitor flow rates, detect leaks, and gather other critical information about human-inaccessible equipment and operations. With drilling rigs located offshore and in extremely remote locations, ensuring reliable internet access to communicate with cloud-based or on-premises monitoring applications can be tricky. Dispatching IT teams to diagnose and repair issues is also costly, time-consuming, and risky. Edge computing allows oil and gas companies to process data on-site and in real-time, so safety issues and potential equipment failures are caught and remediated as soon as possible, even when Internet access is spotty.

Best practice: Use a vendor-neutral edge computing & networking platform like the Nodegrid Gate SR to reduce the tech stack at each site. The Gate SR can host other vendors’ software for SD-WAN, Secure Access Service Edge (SASE), equipment monitoring, and more. It also provides out-of-band (OOB) management and built-in cellular failover to improve network availability and resilience. Read this case study to learn more.

2. Remotely managing and securing fully automated Smart buildings

Smart buildings use IoT sensors to monitor and control building functions such as HVAC, lighting, power, and security. Property management companies and facilities departments use data analysis software to automatically determine optimal conditions, respond to issues, and alert technicians when emergencies occur. Edge computing allows these automated processes to respond to changing conditions in real-time, reducing the need for on-site personnel and improving operational efficiency.

Best practice: Keep the management interfaces for automated building management systems isolated from the production environment to reduce the risk of compromise or ransomware infection. Use edge computing platforms with Gen 3 out-of-band (OOB) management for control plane/data plane separation to improve resilience and ensure continuous remote access for troubleshooting and recovery. 

3. Analyzing patient health data generated by mobile devices in the healthcare industry

Healthcare organizations use data analysis software, including AI and machine learning, to analyze patient health data generated by insulin pumps, pacemakers, imaging devices, and other IoT medical technology. Keeping that data secure is critical for regulatory compliance, so it must be funneled through a firewall on its way to cloud-based or data center applications, increasing latency and preventing real-time response to potentially life-threatening health issues. Edge computing for healthcare moves patient monitoring and data analysis applications to the same local network (or even the same onboard chip) as the sensors generating most of the data, reducing security risks and latency. Some edge computing applications for healthcare can operate without a network connection most of the time, using built-in cellular interfaces and ATT FirstNet connections to send emergency alerts as needed without exposing any private patient data.

Best practice: Protect patient privacy by deploying healthcare edge computing solutions like Nodegrid with strong hardware roots-of-trust, Zero Trust Edge integrations, and control plane/data plane separation. Nodegrid secures management interfaces with the Trusted Platform Module 2.0 (TPM 2.0), multi-factor authentication (MFA), secure boot, built-in firewall intrusion prevention, and more.

4. Reducing latency for live streaming events and online gaming

Streaming live content requires low-latency processing for every user regardless of their geographic location, which is hard to deliver from a few large, strategically placed data centers. Edge computing decentralizes computing resources, using relatively small deployments in many different locations to bring services closer to audience members and gamers. Edge computing reduces latency for streaming sports games, concerts, and other live events, as well as online multiplayer games where real-time responses are critical to the customer experience.

Best practice: Use all-in-one, vendor-neutral devices like the Nodegrid Gate SR to combine SD-WAN, OOB management, edge security, service delivery, and more. Nodegrid services routers reduce the tech stack at each edge computing site, allowing companies to scale out as needed while minimizing hardware overhead.

5. Improving performance and business outcomes for artificial intelligence/machine learning

Artificial intelligence and machine learning applications provide enhanced data analysis capabilities for essentially any use case, but they must ingest vast amounts of data to do so. Securely transmitting and storing edge and IoT data and preparing it for ingestion in data lakes or data warehouses located in the cloud or data center takes significant time and effort, which may prevent companies from getting the most out of their AI investment. Edge computing for AI/ML eliminates transmission and storage concerns by processing data directly from the sources. Edge computing lets companies leverage their edge data for AI/ML much faster, enabling near-real-time insights, improving application performance, and providing accelerated business value from AI investments.

Best practice: Use a vendor-neutral OOB management platform like Nodegrid to remotely monitor and orchestrate edge AI/ML deployments. Nodegrid OOB ensures 24/7 remote management access to AI infrastructure even during network outages. It also supports third-party automation for mixed-vendor devices to help streamline edge operations. 

6. Enhancing remote surveillance capabilities at banks and ATMs

Constantly monitoring video surveillance feeds from banks and ATMs is very tedious for people, but machines excel at it. AI-powered video surveillance systems use advanced machine-learning algorithms to analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security teams. With edge computing, these solutions can analyze surveillance data in real-time, so they could potentially catch a crime as it’s occurring. Edge computing also keeps surveillance data on-site, reducing bandwidth costs, network latency, and the risk of interception.

Best practice: Isolate the management interfaces for all surveillance systems using a Gen 3 OOB solution like Nodegrid to keep malicious actors from hijacking the security feeds. OOB control plane/data plane separation also makes it easier to establish a secure environment for regulated financial data, simplifying PCI DSS 4.0 and DORA compliance.

7. Extending data analysis to agriculture sites with limited Internet access

The agricultural sector uses IoT technology to monitor growing conditions, equipment performance, crop yield, and much more. Many of these devices use cellular connections to transmit data to the cloud for analysis which, as we’ve already discussed ad nauseam, introduces latency, increases bandwidth costs, and creates security risks. Edge computing moves this data processing on-site to reduce delays in critical applications like livestock monitoring and irrigation control. It also allows farms to process data on a local network, reducing their reliance on cellular networks that aren’t always reliable in remote and rural areas.

Best practice: Deploy all-in-one edge gateway routers with environmental sensors, like the Nodegrid Mini SR, to monitor operating conditions where your critical infrastructure is deployed. Nodegrid’s environmental sensors alert remote teams when the temperature, humidity, or airflow falls outside of established baselines to prevent equipment failure. 

Edge computing for any use case

The potential uses for edge computing are nearly limitless. A shift toward distributed, real-time data analysis allows companies in any industry to get faster insights, reduce inefficiencies, and see more value from AI initiatives.

Simplify your edge deployment with Nodegrid

The Nodegrid line of integrated services routers delivers all-in-one edge networking, computing, security, and more. For more edge computing examples using Nodegrid, reach out to ZPE Systems today. Contact Us