Wi-Fi Technology for RTLS

Wi-Fi devices measure the Received Signal Strength Indicator (RSSI) from multiple access points. Using signal propagation models or fingerprinting techniques, the system estimates the device's position. This approach leverages existing Wi-Fi infrastructure but is susceptible to environmental changes.

Wi-Fi Round-Trip Time (RTT), also known as Fine Time Measurement (FTM), measures the time it takes for a signal to travel from a device to an access point and back. This time-based approach provides better accuracy than RSSI methods but requires compatible hardware supporting the 802.11mc protocol.

• Leverages existing Wi-Fi infrastructure
• Lower deployment cost compared to dedicated RTLS
• Wide coverage area
• Compatible with standard Wi-Fi devices
• Supports both asset tracking and people tracking
• Dual-purpose infrastructure (data + location)
• Mature technology with widespread adoption
• Continuous improvement with new Wi-Fi standards

• Lower accuracy compared to UWB or BLE AoA
• Susceptible to environmental changes
• Higher power consumption for mobile devices
• Signal interference in crowded Wi-Fi environments
• Requires multiple access points for reliable positioning
• Fingerprinting requires regular maintenance
• Limited update rate compared to dedicated RTLS
• Accuracy degrades in complex environments

A large public university with over 50,000 students implemented a Wi-Fi-based indoor positioning system across their 200-acre campus. The system leveraged existing wireless infrastructure to provide wayfinding services through a mobile app.

The implementation improved the student experience, particularly for new students and visitors, while providing valuable data on space utilization that helped optimize classroom scheduling and identify underutilized facilities. The university reported a 35% reduction in late arrivals to classes.

A 450-bed regional medical center implemented Wi-Fi RTLS to track 3,000 mobile medical devices across their facility. The system utilized the hospital's existing wireless network infrastructure, supplemented with additional access points for better coverage.

The implementation reduced equipment search time by 65% and improved utilization rates by 22%. The hospital achieved ROI within 18 months through reduced equipment purchases and improved staff productivity. Nurses saved an average of 30 minutes per shift previously spent searching for equipment.

• Wi-Fi access points (typically 1 per 1,000-2,000 sq ft)
• Wi-Fi tags for non-Wi-Fi assets
• Network infrastructure
• Location engine software
• Application platform
• Optional: calibration tools for fingerprinting

• Conduct RF site survey before installation
• Optimize access point placement for location
• Create and maintain fingerprinting database
• Implement proper security measures
• Balance location accuracy with network performance
• Regularly update calibration as environment changes

• Environmental changes affecting signal propagation
• Interference from other Wi-Fi networks
• Balancing network performance with location needs
• Power consumption for mobile devices
• Maintaining fingerprinting database
• Accuracy limitations compared to dedicated RTLS

• Wi-Fi 6E and Wi-Fi 7: Expanded spectrum and higher bandwidth improving positioning capabilities
• Enhanced RTT Support: Wider adoption of 802.11mc/FTM for improved accuracy
• AI-Enhanced Positioning: Machine learning algorithms to improve accuracy and adapt to environmental changes
• Sensor Fusion: Integration with other sensors (IMU, BLE) for improved accuracy and context awareness

• Integrated Solutions: Wi-Fi vendors incorporating location capabilities as standard features
• Hybrid Systems: Increasing integration of Wi-Fi with other technologies like BLE for comprehensive coverage
• Cloud-Based Processing: Shift toward cloud processing of location data for improved scalability
• Privacy-Focused Design: Enhanced security and privacy features to address growing concerns about location tracking