The world of space utilization has evolved well past badge swipes and head counts. Advancements in Wi-Fi technology can certainly be credited with helping to usher in a new generation of metrics that offer more granular insights, like pathways, dwell times and team behavior. But today, a groundbreaking technology from InnerSpace is redefining Wi-Fi’s role in the workplace, offering accurate, actionable insights into how teams and individuals use their workspace – all without invasive sensors or the need for new hardware.
InnerSpace’s patented PHLF technology has solved the core issues of accuracy, cost, and usability with a scalable solution that works seamlessly with existing Wi-Fi infrastructure. It’s the perfect choice for large, complex environments where traditional systems would fail or be prohibitively expensive.
Instead of relying on raw signal strength, PHLF analyzes the ratios of signal strength between access points. This approach corrects for both hardware variability and any obstructions in noisy or complex environments.
PHLF continuously recalibrates itself using fixed reference points – Wi-Fi access points or static devices like printers and smart TVs. This dynamic adjustment ensures reliable accuracy even as spaces or devices change over time.
The InnerSpace platform achieves desk-level precision without invasive sensors. It integrates effortlessly with existing Wi-Fi setups from providers like Cisco, Aruba, Juniper, Arista and Ruckus. With no costly hardware upgrades needed, it’s an affordable and scalable solution.
To truly understand just how revolutionary this advancement is for space utilization, let’s compare the two old methods of Wi-Fi trilateration against what Wi-Fi with PHLF technology can do.
Trilateration using phonesPhones calculate distances to Wi-Fi access points based on signal strength. |
Access point listeningMultiple Wi-Fi access points “listen” for strength of device signals. |
PHLFAnalyzing the ratios of signal strength between Wi-Fi access points instead of relying on signal strength. |
Data accuracy |
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👎 Walls, furniture, and even body placement (e.g., phones in pockets) distort signal strength, leading to inaccurate data. |
👎 Same issues as trilateration using phones – obstructions in the physical environment and signal variations hinder accuracy. Duplication of data possible when individuals use multiple devices or multiple signals are emitted from the same device. Different phones emit signals at varying intensities, making standardization nearly impossible. |
👍 Ratio-based precision corrects for environmental obstructions, differences in signal strength and hardware variability, achieving superior accuracy in noisy or complex environments. Continuous recalibration using fixed reference points (e.g., access points or static devices like smart TVs) ensures reliable accuracy, even as spaces or devices change over time. Advanced algorithms ensure multiple signals from the same device are identified and unified into a single data point. |
Infrastructure requirements |
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👎 Requires app installations on phones, creating a significant adoption barrier. Users must opt in for the system to function effectively. |
👎 Advanced hardware systems like multi-antenna arrays (e.g., “angle of arrival” systems) are needed for precision, drastically increasing costs. |
👍 PHLF integrates effortlessly with existing Wi-Fi setups from providers like Cisco, Aruba, Juniper and other hardware vendors. No costly hardware upgrades are needed, making it an affordable and scalable choice. |
Gone are the days of simply collecting location data by using phones to calculate the distance to Wi-Fi access points, or vice versa, using access points to detect phone signals. As you can see above, these traditional systems were plagued with:
Traditional occupancy data, like badge swipes, door entry logs, or motion sensors, tell you if someone is in the building but offer little else in the way of insights. While helpful for tracking attendance, this basic data falls short of revealing how people interact with spaces or how activity flows across different areas – critical insights for optimizing the workplace. PHLF technology bridges this gap by delivering behavior-based data that uncovers how and why spaces are used with scalable, desk-level accuracy. Whole new types of data are accessible now, including:
With precise data on how spaces are used, InnerSpace helps IT leaders to support smarter decision-making for various departments across their organization.
Traditional Wi-Fi-based solutions fell short because they couldn’t overcome the challenges of accuracy, scalability, and usability. With InnerSpace’s PHLF technology, organizations can finally unlock the full potential of their existing infrastructure to gain actionable, precise insights into how spaces are truly used.
By turning promises into measurable outcomes, InnerSpace empowers IT leaders to make better decisions, optimize resources, and create workplaces that are as dynamic and efficient as the teams they support. The future of space utilization isn’t just accurate – it’s effortless, scalable, and ready for what’s next.