Accurate Data. Anonymous Tracking. No Sensors.

Traditional triangulation methods for indoor localization often fall short in complex indoor environments due to their reliance on line-of-sight signals and the inherent variability of radio frequency propagation indoors.

InnerSpace introduces an innovative approach to indoor localization through Predictive Hyperbolic Location Fingerprinting (pHLF), a method that not only addresses the limitations of triangulation but also sets a new standard for accuracy, stability, and scalability in indoor positioning systems.

This method employs a predictive model based on the Received Signal Strength Indicator (RSSI) from multiple fixedly-positioned sensor devices in a 2D environment. Key components of this approach include:

• RSSI Value Collection: Gathering RSSI values from a network of stationary sensors at known locations.

• Fingerprint Generation: Creating both raw and log-ratio RSSI fingerprints for precise location mapping.

• Normalization and Fusion: Enhancing accuracy through the normalization and combination of fingerprints into a comprehensive, predictive model.

• Dynamic Adaptation: Continuously updating fingerprints to reflect changes in the environment, ensuring high accuracy over time

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• Data anonymization with one-way hashing and client-specific salts.

• No PII is stored; data is available only in aggregate form.

Because we use devices as proxies for “people” there is a concern that each device would be counted. The good news is that InnerSpace’s platform is able to deduplicate devices and ensure that multiple devices are counted appropriately as one person.

Our system learns over time that certain devices belong to one individual. We are able to differentiate as the devices separate, the one actively moving reflects the behavioral pathway whereas the stationary device is passively occupying space.

InnerSpace’s patented technology dramatically enhances location accuracy with Predictive Hyperbolic Location Fingerprinting (pHLF), making it the most accurate Wi-Fi-based location technology on the market today.

Traditional triangulation methods struggle due to signal obstructions and environmental variability. PHLF is a method to leverage a Received Signal Strength Indicator (RSSI) from fixed office equipment providing superior accuracy. pHLF significantly advances indoor location and offers a more reliable, accurate, and scalable solution for spatial intelligence achieving a location accuracy of within 4ft.

InnerSpace adds no additional clients or load to the existing Wi-Fi network, instead using data that is already collected by AP’s and forwarded to InnerSpace via existing 3rd-party integration paths through a location service webhook.

The amount of data forwarded to InnerSpace depends on the number of Wi-Fi AP’s and number of clients on the network, but for a standard 25k sq ft office floor plate, this is typically less than 100k per minute (~150MB per day).

InnerSpace prioritizes privacy and security at every step:

• No PII Stored: We do not capture or store names, user IDs, or any Personally Identifiable Information (PII). The only device data processed is the MAC address, which is immediately anonymized using one-way hashing with client-specific salts. The original MAC is discarded and cannot be reconstructed.

• Aggregated Data Only: All data is anonymized and aggregated to prevent identity inference. Individual behaviors, such as movement patterns, are obfuscated to ensure privacy.

• GDPR-Compliant: We adhere to GDPR regulations and industry best practices to protect personal data and minimize breach risks.

• Secure Infrastructure: Data is processed in a secure virtual private cloud, inaccessible from the outside. Regular security assessments ensure robust protections.

• ISO27001 Certified: Our information security practices meet globally recognized ISO27001 standards, validated through independent audits.

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