All-new LinkRunner 100G – professional-grade performance and precision tool network engineers need to troubleshoot and upgrade critical 100G fiber links. 

COLORADO SPRINGS, Colo., Sept. 9, 2026 – NetAlly today announced LinkRunner® 100G, a handheld tester built to validate and troubleshoot high-speed fiber links increasingly moving beyond data centers and into enterprise networks. With the press of a single button, LinkRunner 100G AutoTest tells a network technician whether a 100G link is healthy — and if it isn’t, exactly what’s wrong. At $8,999 plus an AllyCare Pro subscription, LinkRunner 100G delivers the professional-grade diagnostics network engineers expect from a 100G tester at a price built for enterprise IT.

“AI is driving a networking super-cycle, pushing higher-speed networks beyond the data center and into the enterprise,” said Jeff O’Mara, CEO of NetAlly. “Until today, the tools to support those networks were expensive and highly specialized for big telco providers. Enterprise IT is different. We built LinkRunner 100G to give enterprise network professionals the tool they need to deploy, fix, and upgrade these networks.”

As 25G, 40G, 50G, and 100G networks become more common in enterprise environments, IT teams are being asked to manage fiber networks that operate differently from the lower-speed connections they have traditionally supported. Troubleshooting networks running on twisted-pair copper up to 10G has been eased by rich automation specified by IEEE standards for auto-negotiation of speed, power, and polarity. None of this automation exists for 100G networks, resulting in brittle, hard-coded configurations. Vendors further complicate the space with proprietary and complex transceivers which modify encodings and bit rates to gain advantages in fiber efficiency and bandwidth.

LinkRunner 100G is designed specifically for these new challenges faced by network support teams, allowing technicians to run a full diagnostic at the push of a button, with no specialized fiber training required.

With LinkRunner 100G, network pros solve problems on 100G networks with simple, automated steps:

1. Catch configuration mismatches that can leave a Quad Small Form-factor Pluggable 28 (QSFP28) port down even when nothing is actually broken. QSFP Insights identifies the transceiver, fiber type, speed and Forward Error Correction (FEC) settings, helping uncover mismatches that can be difficult to spot through the switch alone.
2. Pinpoint the problem to make the fix, rather than guessing. NetAlly’s EZ Wired app validates the link speed, FEC, interfaces, network services and application connectivity as well as transceiver operation. Since each parameter must be set by the user on the switches, knowing the exact set of parameters provides the network pro with the recipe for success.
3. See where the traffic is actually getting stuck, rather than assuming the problem is the link being tested. Path Analysis traces the network path from the tester to its destination, hop by hop, revealing when a slowdown originates elsewhere, such as an overloaded switch several hops away.
4. Identify when a 100G link is trending in a bad direction and may be close to failing – before it does. Forward Error Correction (FEC), which enables higher-speed fiber connections by correcting damaged bits, can mask a degrading link while the network continues to operate. AutoTest catches failures before your users do and captures the information in Link-Live.
5. Verify that the network performs as specified and prove that you are getting the performance you paid for. Use Performance Test to validate newly deployed infrastructure, troubleshoot existing networks, and confirm expected throughput.

“Higher-speed fiber is quickly becoming part of the enterprise network, and IT teams need to be ready for it,” O’Mara said. “If you’re responsible for links above 10G, LinkRunner 100G belongs in your toolkit.”

LinkRunner 100G is handheld and battery-powered, so technicians can take it on any call, not just escalations. Results are translated into plain-language answers rather than error codes and can be uploaded to Link-Live, NetAlly’s collaboration and reporting platform, so a technician can share a full report instead of a screenshot.

Pricing and Availability

LinkRunner 100G is now available for $8,999, plus an AllyCare Pro subscription, through NetAlly’s network of authorized resellers. For full details, visit www.netally.com.

What AI Edge Means for Wireless Devices

A few days ago, I was sitting in a far corner of my home, upstairs, trying to build a small tool of my own using Claude. Nothing fancy — just me, a laptop, and an AI model doing the heavy lifting. Because I live in a multi-level house, I’ve got a mesh Wi-Fi system to avoid dead zones. I should’ve been fine, right?

Except I wasn’t. The model kept telling me it was about to give me a fast response. And then… nothing. Minutes went by before my query even landed on the system, and minutes more before the response came back.

Same story a few days later, uploading a video to my YouTube channel. I was excited to get it up, and instead I sat there watching a progress bar crawl. Twice, in two completely different situations, the thing standing between me and a good experience wasn’t the AI. It was my Wi-Fi.

That’s the thing nobody really addresses when they talk about “AI at the Edge.” Everyone’s excited about the models — how big they are, how smart they are, how fast they can reason. But almost nobody’s talking about the fact that all that intelligence has to travel through a wireless link before it ever reaches you. And that link is, frankly, the weakest part of the chain.

So, What Does “AI Edge” Actually Mean?

Let’s back up, because “AI at the edge” sounds like one of those terms that gets thrown around a lot without anyone really explaining it. Here’s the simple version: it means AI processing that happens on devices close to you — your smartphone, a smart hub in your home, or your Wi-Fi access point — instead of way off in some data center.

AI in the network is a different beast: models and processing happening deep inside network infrastructure, in places you never see. That side has plenty of processing power, with massive models and huge datasets, but all that horsepower comes with a tradeoff: latency. The further your request travels, the longer you wait.

Edge AI flips that. It’s built for speed, low latency, and quick answers. But here’s the catch, and it’s the whole point of this article: edge AI is only as fast as the wireless device carrying it. You can run the most advanced large language model in the world on your network. Doesn’t matter. If the last few feet — or the last few walls — between the AI and your device are a choke point, you’re going to feel it. And you’re going to experience it as “AI is slow,” even though the AI did its job in milliseconds.

That’s exactly what happened to me upstairs. Mesh system and all, the wireless portion of my network was the bottleneck dictating how “fast” my AI felt. You can validate a model’s accuracy all day in a lab with a clean connection, but if you don’t test how the RF performance holds up as conditions vary — signal attenuation, degraded power levels, changing interference — you’re not testing the full picture of what the device can actually deliver.

My personal experience reinforces a larger point: wireless testing can’t be an afterthought in AI-edge deployment; it must be central to the conversation.

Enter Wi-Fi 8

This is exactly why the coming generation of wireless technologies — whether it’s Wi-Fi 8 in your home network, 5G on your phone, or 6G on the horizon — matters more now than ever. Past generations mostly chased peak throughput in the form of bigger numbers on a spec sheet. Wi-Fi 8 (the industry’s working name for IEEE 802.11bn) breaks that pattern. With an official designation of “ultra-high reliability,” or UHR, Wi-Fi 8 is built to support higher throughput in poor-signal conditions, lower worst-case latency, and fewer dropped connections. This is especially important as devices roam between access points (APs) via features like Multi-AP Coordination (MAPC), where nearby access points work together instead of competing for airtime.

The emphasis on UHR represents a larger and significant shift away from “how fast can Wi-Fi run under ideal conditions” and toward “how consistently fast is my Wi-Fi as attenuation, power, and interference levels shift.” That distinction matters enormously for AI at the edge.

AI-powered devices aren’t just streaming a video where a little buffering is annoying but survivable. These systems are often making real-time decisions while interacting with voice assistants, AR/VR devices, industrial sensors, and even smart cameras performing on-device inference. Any hiccup in the wireless link doesn’t just slow things down; it can break the entire interaction. Nobody wants to repeat themselves to a smart speaker or discover too late that their security camera is lagging behind reality.

Where Testing Moves the Needle

Performance isn’t just about the client device in your hand — it affects the access point, too. Both ends of the link have to hold up. And here’s the part that’s easy to miss: you don’t have much control over the link itself. You can’t fix the walls in someone’s house, the neighbor’s Wi-Fi network, or a crowded RF band. What you can control is how well your device’s Tx/Rx chain performs when it’s handed a bad link. If you’re not testing that, you’re relying on chance. Designing hardware for a target performance range is one thing. Proving the software actually drives that hardware to hit the target, consistently, is another.

That leads to the next consequential question, which is what does testing actually look like under these new conditions? Broadly, it splits into three categories:

Non-signaling testing measures RF performance in isolation, away from live communication, so problems get pinned to hardware. Considerations here include:

• Power level and power spectral density (PSD) validation for 6-GHz devices in a dRU configuration. This confirms that a device hits its specified power gain without exceeding regulatory limits, which impacts how much uplink range it can deliver.
• Spectrum mask testing, which catches energy leaking into adjacent channels, especially where tones are spread non-contiguously across a wider bandwidth as in case of dRU.
• Receiver sensitivity testing, which measures the weakest signal a device can still decode, for ELR-PPDU specifically. This is the number that proves the “long range” claim.

Mission-mode testing, by contrast, deliberately changes channel conditions while the device is running, to see how it adapts in real time. This includes:

• Per-stream rate-adaptation validation, which introduces path loss on a single MIMO link and confirms only that link’s MCS steps down.
• Checking that the rate-vs-range curve (MCS versus power) degrades smoothly as conditions worsen, rather than falling off a cliff at some threshold validating newer code rates introduced in Wi-Fi 8.
• Packet Error Rate vs Signal to Noise Ratio (PER-vs-SNR) sweeps confirm a feature like Long low density parity check (LLDPC) codewords genuinely lowers the error rate at poor SNR.

Co-existence testing puts the device in a shared, contested RF environment — alongside Bluetooth and neighboring Wi-Fi networks — to see how it holds up on signal power and adjacent-channel levels when it’s not the only radio in the room.

None of this is about recreating every real-world scenario. It’s about knowing, with precision and a great degree of certainty, where a device’s hardware and software start to fall short of spec — and before a user finds out first.

The Bottom Line

Here’s what I keep coming back to: we can build the smartest edge AI in the world, but if we don’t rigorously test the wireless network carrying it, we’re leaving performance — and user trust — on the table. My AI tool wasn’t slow because Claude was slow. My video wasn’t slow to upload because YouTube was slow. In both cases, it was the wireless hop that quietly decided how good my experience was going to be.

That makes the case for treating wireless test as a first-class citizen in AI edge deployment. And it supports the need for taking Wi-Fi, 5G and 6G seriously — not as a marketing spec bump. Wireless communications is the connective tissue that determines whether all that edge AI investment will actually pay off where it matters most: in someone’s hands, in their home, in the moment they need it to just work.

With 3GPP NR-NTN, satellite connectivity is opening new possibilities for smartphones, cars, and IoT devices worldwide to stay connected. MediaTek’s James Chen explains how the company is helping make scalable, seamless global satellite connectivity a reality.

How hotels, restaurants, and entertainment venues can use guest WiFi to create stronger engagement, better follow-up, and more measurable business outcomes.

For many venues, guest WiFi is still treated as a background service. It works, guests get online, and the interaction ends there.

But for venue teams focused on growth, that connection can do much more. It can support first-party data, more relevant guest engagement, better venue intelligence, and stronger business outcomes over time.

The statements and opinions by each Wi-Fi Alliance member and those providing comments are theirs alone, and do not reflect the opinions or views of Wi-Fi Alliance or any other member. Wi-Fi Alliance is not responsible for the accuracy of any of the information provided by any member in posting to or commenting on this blog. Concerns should be directed to [email protected].

As user expectations and connectivity demands continue to grow, Butler University shares lessons learned on wireless design, operational visibility, and building a network foundation ready for what’s next.

Butler University’s lessons on preparing campus infrastructure for the future

Campus networks are under more pressure than ever. Universities are supporting video collaboration, cloud applications, AI-driven tools, and thousands of connected devices, often with infrastructure that was never designed for those demands.

As institutions plan for the future, simply replacing aging equipment is no longer enough. We spoke with Nick Northcutt, Network and Security Engineer at Butler University, about lessons learned from Butler’s network modernization journey and what higher education IT teams should consider when building a future-ready campus network.

Many universities are grappling with aging infrastructure and growing technology demands. What prompted Butler University to think beyond simply replacing existing equipment and take a more strategic approach to network modernization?

The video meeting boom during the pandemic dramatically raised expectations for our wireless network. Suddenly, people were noticing roaming issues, poor signal quality, and other performance challenges that weren’t as obvious when most users were simply using email and web browsing. We had a clear problem to solve, so we started by identifying our biggest trouble spots and understanding where the wireless design was falling short.

We found outdated designs in residential and academic buildings, including access points placed in hallways instead of the spaces where people live, study, and gather. We also identified dead spots and density issues in classrooms.

By redesigning these environments, increasing access point coverage, and improving support for high-density spaces, we saw a significant drop in support cases. That work marked the beginning of a broader effort to re-evaluate wireless across campus.

It also prompted us to look ahead at how emerging wireless technologies could further improve the campus experience. We applied the same thinking to Hinkle Fieldhouse’s basketball arena, where a new wireless design is helping lay the foundation for future fan engagement initiatives.

When you think about a “future-ready” campus network, what does that look like, and what capabilities are most important for supporting the university’s long-term needs?

To me, a future-ready campus network is one that can scale to meet growing traffic demands, move beyond an over-reliance on wired connections, and deliver highly reliable wireless connectivity while still protecting users and data. Ideally, it should accomplish all of this while operating invisibly in the background, so the experience feels seamless for teaching staff, students, etc.

Looking back on Butler’s network modernization journey, what lessons or insights were most valuable, and were there any outcomes that surprised the team?

The sudden, exponential increase in video calls over Wi-Fi during the pandemic caught us by surprise. Knowing what we know now, being able to respond to that new demand right at the onset would have been very beneficial. We’ve also learned that Wi-Fi design is never a one-time exercise; it is an iterative process because there are so many variables, including the campus environment, changing user behavior, and budget constraints. Even today, we’re continually looking for ways to improve performance and deliver better experience for teaching staff, students and others.

I think making Wi-Fi design and troubleshooting professional development a priority for staff would be high on the list—and something to do sooner rather than later. It would help take a lot of the guesswork out of Wi-Fi troubleshooting and support.

On the operational side, I was pleasantly surprised by how seamless the access point (AP) replacement process proved to be. Our facilities team began installations at 3:00 a.m. and could finish an entire building before classes started. We repeated this process across campus, replacing more than 1,700 access points with virtually no disruption to students, faculty, or staff before the 8:00am classes start. The ability to deploy at that scale with virtually no user disruption was extremely impressive.

For higher education IT teams planning a similar transformation, what advice would you offer based on your experience?

The best Wi-Fi hardware in the world won’t fix poor AP placement and design. Find the problem areas on your campus and invest in industry best-practice standards. Survey and build simulations using wireless planning and design tools to support redesigns and new builds. Invest in wireless professional development for your staff (CWNA, ECSE, etc.).

A great way to improve Wi-Fi case resolution is to invest in historical wireless troubleshooting and analytics tools, and establish a standard process for the Help Desk to gather useful information for escalation, such as the date and time of the incident, the device’s Wi-Fi MAC address, room location, and a brief description of the issue.

When it is time to consider your AP replacement cycle, think holistically about what your campus will need to support in the future. Consider technologies such as 6 GHz, WPA3 device compatibility, and emerging Wi-Fi 7 capabilities. Also think about AI-assisted operational tools and flexible network access designs that can accommodate the growing number of IoT and consumer devices connecting to campus networks, particularly in residential environments. Finally, evaluate how different vendors approach performance, manageability, security, and future scalability when making long-term infrastructure decisions.

How can universities balance meeting current needs while ensuring their network infrastructure remains adaptable for future technologies and demands?

I think the key is making sure your network can support increased traffic over the next five years. Beyond that, it’s about designing the network with enough flexibility to handle the things we can’t predict yet. That means investing in products that support multi-gigabit switching, automation, AI workflows, cloud integration, and strong analytics.

How do you see the technology requirements of higher education institutions evolving over the next five years?

I expect network throughput requirements to keep climbing, especially as AI, streaming, video conferencing, gaming, and research demands continue to grow. Wi-Fi is now the primary way most users connect, so expectations for wireless performance will only increase. At the same time, the growth of wireless IoT on campus means we have to keep finding ways to make these devices work as easily as they do at home, while still keeping them secure. Ultimately, the network should never be the bottleneck.

As universities prepare for AI-enabled applications and other emerging technologies, what infrastructure investments and foundational capabilities should institutions prioritize today to support innovation over the next decade?

As adoption grows, universities should focus on building a network infrastructure that can support significantly higher data volumes, lower latency requirements, and increasingly distributed workloads. Security should also be a major consideration.

I would prioritize connectivity, security, and visibility. That means investing in infrastructure that can support future wireless and multi-gig needs, along with strong monitoring, analytics, and security capabilities. As cloud services continue to grow, universities need networks that can scale, adapt, and support a Zero Trust mindset while enabling long-term growth, flexibility, and innovation.

Based on Butler University’s modernization journey, what’s the single most important piece of advice you’d share with university IT leaders looking to build a future-ready campus network?

My advice would be to think beyond simply replacing aging equipment. Understand where your problem areas are, invest in good design, and make sure the infrastructure you choose can grow with the needs of your institution. Technology will continue to change, but a strong foundation, skilled staff, and a clear understanding of your users’ needs will always matter.

If you’re attending EDUCAUSE, I’d welcome the opportunity to continue the conversation and share more details during our poster session.

The data center industry is transforming faster than at any point in its history. For decades, the story of the data center was a story of steady, predictable growth with more racks, more servers, and more power each year. That era is over. Artificial intelligence (AI) workloads are driving infrastructure demands to unprecedented levels.

Behind that surge lies an equally urgent challenge: the unseen supporting infrastructure that connects, secures, and keeps the AI systems operational. It is not glamorous, yet it quietly determines whether an AI data center stays online, efficient, and secure.

Anatomy of the Modern AI Data Center

The modern AI data center is composed of two distinct but deeply connected worlds: the white space and the gray space.

The white space is what most people imagine when they think of a data center: servers, network gear, and the high-speed IT fabric that stitches them together into a single compute core. It is the domain where AI models are trained and served, and as such, draws the most attention and investments.

Conversely, the gray space is easy to overlook precisely because it is designed to be invisible. This domain includes the industrial facilities that surround and sustain the compute engine. There are four groups that make up the gray space:

1. Power and distribution
   The lifeblood of the AI data center, these systems manage and deliver electricity to the entire data center. Here, resilient and redundant connectivity is everything. It keeps power telemetry flowing continuously and allows subtle warning signs, like abnormal power surges, to surface before they become emergencies.
2. Cooling and thermal management
   Systems like cooling towers where the network must be redundant and fast converging to ensure vital telemetry data is transmitted to control systems in real-time to prevent thermal runaway which can hamper uptime and create safety incidents.
3. Building management
   Building Management Systems (BMS) are responsible for monitoring and coordinating HVAC, environmental sensors, lighting, alarms, and other facility systems. It gives operators centralized visibility and control to maintain safe conditions, respond quickly to issues, and operate the data center efficiently.
4. Physical security and access controls
   Since AI data centers concentrate high-value compute, sensitive data, and critical digital services, unauthorized access or security disruption are especially consequential. Perimeter cameras and access control systems require reliable PoE and high-bandwidth connectivity to maintain continuous surveillance and protect the facility and the people in it.

The discipline of tying these industrial subsystems together (e.g. networking, monitoring, and security on a single observable, standards-based system) is referred to as ‘gray space automation’. It is the practice of taking dozens of independent operational technology (OT) systems that historically ran in isolation and connecting them into one coherent whole. In the AI era, that connective tissue is as critical as the compute core it powers.

The criticality of gray space automation

The gray space brings together the physical systems that determine whether AI infrastructure stays available, secure, and efficient. Four interconnected challenges must be addressed together as weakness in any one facility can reduce compute capacity or take critical systems offline.

Continuous operations in demanding environments: Gray space systems extend into electrical rooms, pump areas, rooftops, and outdoor locations where heat, moisture, vibration, and electrical noise can challenge conventional networking. As AI densities rise, even a brief communication failure can create operational blind spots or interrupt critical coordination. Uptime Institute reports that one in ten data center outages still cause serious or severe disruption.

Growing, interconnected attack surface: Every connected controller, sensor, camera, and vendor-access pathway creates operational value, but it also creates new risk. Limited network visibility, flat communications, and fragmented security policies can allow a compromised device to threaten adjacent systems. Operators need to identify assets, restrict unnecessary communications, and detect threats without disrupting critical processes.

Complexity compounded by skills shortages: Gray-space domains are often built by different vendors and managed through separate tools, creating inconsistent architectures and heavy manual workloads. Nearly two-thirds of operators report difficulty finding or retaining qualified staff. Scaling requires familiar tools, repeatable designs, automation, and shared visibility across IT, security, and facilities teams.

Growing pressure to operate efficiently: Rising power and cooling demands make static operating models increasingly costly and unsustainable. Reliable telemetry from gray space systems is essential for preventive maintenance, capacity planning, and improvements in PUE and WUE. Without it, emerging failures go unnoticed and efficiency opportunities remain out of reach.

One Cisco foundation for the entire AI data center

Cisco brings the entire AI data center on one foundation, bridging the white space where AI workloads run and the gray space systems that continuously power, cool, monitor, and protect them. By extending one trusted networking and security foundation across both environments, Cisco helps operators reduce silos, apply consistent policies, and oversee the entire infrastructure supporting AI capacity.

It starts with Cisco Cloud Control, the AI native operations layer that brings inventory, topology, alerts, identity, and workflows from supported domains into one governed experience. It preserves the depth of specialized domain tools while connecting operations at the management layer: AI Canvas to bring teams and insights together, Nexus Dashboard and Intersight for deep control of white space networking, and Catalyst Center and Meraki Dashboard from Cisco Cloud Control to extend familiar operations into gray space. Together, they turn separate domains into one coordinated operating model, helping teams see more and do more in a collective effort to protect uptime, capacity, and efficiency.

Extending the Cisco foundation into the gray space domain
Cisco IIoT extends the one foundation from the compute core into the operational backbone of gray space systems that keep AI infrastructure running. Cisco Industrial Ethernet (IE) switches pair rugged, low power, fanless designs with lossless industrial resiliency protocols that keep critical systems connected even when something goes wrong. With Cisco Cyber Vision embedded directly into the switches, the network becomes the defense layer passively discovering every connected asset and creating segmentation policies that IE switches then enforce to isolate gray space systems from one another and from IT. And because IE switches integrate with the Meraki Dashboard or Catalyst Center, IT teams can comfortably deploy, onboard, and manage gray space facilities using tools and skills they already possess.

• Industrial-grade resilience for continuous AI through ruggedized networking engineered to thrive where standard networking fails to ensure constant, reliable connectivity
• Security embedded into the network that turns the network itself into the defense layer and establishes a security checkpoint at every connection across the gray space facilities
• Industrial networking simplified for IT ensures IT expertise transfers directly into the OT world to streamline onboarding, management, and maintenance of gray space facilities.
• Engineered for efficiency at every connection to help achieve new benchmarks in sustainability metrics and optimizing every system for peak performance.

Where a multi-category vendor stitches together disparate products, Cisco treats the network itself as both the connectivity layer and an embedded security sensor. Cisco offers a single, battle-tested networking architecture that’s backed by a technology ecosystem that spans design, deployment, support, and lifecycle guidance for every data center.

Built the foundation for continuous AI

For over 20 years, Cisco has helped connect and secure industrial environments where downtime carries serious operational and safety consequences, from manufacturing floors and energy grids to transportation systems. Combined with decades of data center networking leadership, Cisco is positioned to unite the white space where AI runs with the gray space systems that keep it running. The result is a proven foundation that strengthens resilience, embeds security, simplifies operations, and improves efficiency across the entire AI data center.

Cisco is already helping some of the biggest names in AI bring their data center facilities onto a unified architecture. Connect with a Cisco Networking expert to learn about how we’re helping to unlock the future of AI data centers.

Fourth session in SPARK’s wireless technology webinar series will examine deterministic timing, ultra-low latency, ultra-low power consumption and high data throughput across demanding real-time applications

MONTREAL, Canada, September 1, 2026 – SPARK Microsystems, a Canadian fabless semiconductor company specializing in next-generation short-range wireless communications, will host the fourth session in its wireless technology webinar series on September 15, 2026, titled “From Gaming and Audio to Real-Time IoT Devices: Designing High-Performance Wireless with LE-UWB™.”

Wireless performance requirements are becoming increasingly demanding as connected devices move toward real-time operation. Gaming peripherals and high-quality audio systems require highly responsive wireless connectivity, low power consumption and increasing data performance, while emerging applications such as robotics and industrial IoT are pushing the limits of wireless communication in latency, power efficiency and reliable real-time communication.

During the webinar, SPARK Microsystems experts will examine how LE-UWB™ combines deterministic packet timing, ultra-low latency, ultra-low power consumption and high data throughput to support high-performance short-range wireless connectivity. The session will explore where the wireless requirements of gaming and audio are converging with those of a new generation of real-time IoT devices.

Attendees will also learn why low latency alone does not define real-time wireless performance, and how predictable timing can help maintain consistent operation in increasingly congested wireless environments. The presentation will conclude with a live Q&A with SPARK experts.

Webinar: From Gaming and Audio to Real-Time IoT Devices: Designing High-Performance Wireless with LE-UWB™
Date: September 15, 2026
Time: 11:00 a.m. ET
Registration: Register for the webinar

Building on SPARK’s Wireless Technology Webinar Series

The September session builds on three previous SPARK webinars examining the evolving requirements of short-range wireless connectivity.

The most recent session, “From Ranging to Real-Time IoT: Building Low-Power Location-Aware Systems with UWB,” presented in collaboration with Softgent, is available on demand. The webinar explores how LE-UWB can combine wireless communication with time-of-flight ranging and proximity sensing for low-power, location-aware IoT systems.

Earlier sessions examined how LE-UWB differs from Bluetooth, Wi-Fi and conventional UWB, as well as the wireless requirements emerging from Physical AI and always-on IoT devices.

Watch previous webinars: sparkmicro.com/webinars

About SPARK Microsystems

SPARK Microsystems is building next-generation short-range wireless communication devices for consumer, industrial and medical applications. Leveraging patented LE-UWB™ technology, SPARK delivers ultra-low-power, high-speed wireless connectivity with unmatched interference robustness and exceptionally low latency. LE-UWB™ technology is ideally suited for wearables, peripherals, tags, sensors, and other IoT-connected devices that require reliable, high-performance data communication, as well as ranging and positioning capabilities. SPARK makes it possible to minimize and ultimately eliminate wires and batteries across a wide range of AI and IoT applications. For more information, visit sparkmicro.com.

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SPARK MICROSYSTEMS, LE-UWB and DETERMINISTIC WIRELESS are trademarks or registered trademarks of SPARK Microsystems International Inc. in Canada and/or other countries. All other trademarks are the property of their respective owners.

Identifying and closing gaps in Wi-Fi design and deployments

A conversation with Dennis Holmes, senior advisor for Mobility at Presidio. Presidio is a global digital solutions and services provider delivering software-defined cloud, collaboration and security solutions to customers of all sizes. Presidio designs modern networks for industries ranging from healthcare to manufacturing and retail to sports.

What makes 7SIGNAL’s sensors, agents and AI useful for pinpointing gaps in the design of enterprise networks?

I’ve been in the wireless business since the 1990s, and many vendors claim to do what 7SIGNAL does. But they don’t tie into an agent on the end user’s device. With 7SIGNAL, you get visibility into the end device and device drivers along with a huge database showing which driver works best. That’s the secret sauce. 7SIGNAL shows what devices (and users) experience on the network: roaming behavior, latency, RF interference, and connectivity failures.

Our SAs, or pre-sales guys, help design networks and work with our sales team. 7SIGNAL gives SAs the information they need to develop a better technical strategy and proposal.

What sort of customers or industries benefit from 7SIGNAL’s technology?

At a recent conference, dozens of my customers watched a demo showing how to pick the right device driver. But 7SIGNAL already delivers that very information. From professional sports teams with stadium POS to warehousing companies with handheld devices, customers want to know if the network is operating optimally or quickly pinpoint the problem. 7SIGNAL’s data saves hundreds of thousands of dollars when designing or troubleshooting an enterprise network. That’s the ROI.

Think about an automotive parts maker, which might have a dozen plants. Let’s say one of them makes brake pads and rotors, employs 6,000 people, and its network goes down. They may lose $20,000 per hour while they try to find the problem, fix it, and prevent it from happening again. With 7SIGNAL, as an SA, I can eliminate 90 percent of a customer’s perceived Wi-Fi problems.

I know a couple of warehousing companies that had problems controlling AGVs. They spent hundreds of thousands of dollars trying to figure out the issue. With 7SIGNAL, we figured out there was a radio problem with their AGVs. To fix it, we retrofitted each AGV with a ruggedized wireless access point with an external antenna. That solved the problem on day one with a roaming algorithm.

For sporting venues, 7SIGNAL’s sensors are important in areas that aren’t easily accessible for monitoring such as VIP suites and other revenue-generating areas. 7SIGNAL provides a detailed state of Wi-Fi in case there is an issue with lobbies, merchandise stores, or F&B areas. 7SIGNAL helps show that Wi-Fi is functioning, which is critical for the guest and revenue.

For a hospital or healthcare network, 7SIGNAL’s agents are important because nurses carry devices and use mobile workstations. Infusion pumps and clinical workstations depend on a well-designed and operating network. When Wi-Fi goes down, that interrupts caring for patients. Dropped connections mean healthcare providers have to stop what they’re doing. With 7SIGNAL’s agents, IT staff quickly figure out from the client side what’s going on with the devices or network.

How does 7SIGNAL help a solution architect?

To create and sell a network design to a manufacturing facility, an SA might engage with a plant manager. At a hospital, the SA is selling the chief nursing officer. Neither of these folks care about things like packet loss. So, the SA’s most valuable conversation is about ROI and uptime. To make sure the recommended solution delivers both those things for enterprise clients, the SA wants to know how the design will perform. That’s where 7SIGNAL comes in; it shows the architect what the client experience will be, something the infrastructure dashboard can’t do.

Any final thoughts?

7SIGNAL’s technology has been easy to use. Integrating 7SIGNAL’s solution with AI has made it a lot easier for our team and customers to ask questions in plain language. Network monitoring tools typically show the infrastructure, not what devices experience. Reconciling the two is how an SA can deliver a better proposal for a network design, one that achieves the most uptime. The customer gets a network that requires fewer support tickets and offers a better experience for users.