In 2025 I will be celebrating twenty five years in the Wi-Fi space. In this time, I have seen a lot happen, including many attempts to use Wi-Fi for location tracking. I have also seen how companies have used UWB, Zigbee and other technologies to attempt to improve the location tracking capability of their Wi-Fi infrastructure. A lot of folks are simply burned out because all these efforts failed, the cost of the deployment was too high, and no business logic was applied.
I was approached by InnerSpace about a month ago about what they are doing. They claimed to achieve high accuracy (<6’) location tracking using existing
Wi-Fi infrastructure, with no requirement for added density or additional sensors. Let me just say, I was highly skeptical! However, I have always approached technology with the idea that your mind should be like a parachute, it works best when open!
They have developed and recently patented a Wi-Fi-based locationing approach they call Predictive Hyperbolic Location Fingerprinting. (see the attached brief) Traditional triangulation methods using Wi-Fi struggle to locate people accurately due to multi-path interference caused by signal reflections on surfaces, non-line-of-sight conditions like walls and furniture that block signals and environmental variability caused by such things as changes to the layout or the movement of people affecting signal strength. Simply adding more access points is expensive and self-defeating as it just increases the noise floor. Adding sensors is another additive cost and not something the IT department wants to deal with.
However, if you are able to use the existing Wi-Fi infrastructure and existing Wi-Fi clients as your technical model, it can be a very cost effective and non-disruptive approach. It seems that InnerSpace is able to use the RSSI to dynamically update location fingerprints in a way that accounts for those changes in the environment. This is reflected in improved accuracy, stability in dynamic environments, and scalability. The team at InnerSpace proved this out this with a major enterprise Wi-Fi partner and have also replicated it in the field with their enterprise clients, achieving a 1.3m (~6’) level of accuracy 90% of the time.
I would also say the most important aspect is supplying business intelligence. A lot of RTLS systems have been deployed with no way to provide measured data that impacts business. The versatility of PHLF enables important business intelligence not achievable before without heavy investment in sensor infrastructures. Examples of this would be occupancy insights, utilization data, and how different group cohorts use space. This is important data that post COVID, can determine real estate costs, versus the mixed remote worker.
As I dig deeper into InnerSpace, I will create additional blogs, but for now the company's technology looks like there is finally a novel way of approaching location tracking that lowers cost, actually works, and provides measurable data to improve the business experience.
Stay tuned.
