Indoor Positioning via BLE Beacons and Machine Learning
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Solution Overview
Problem
Satellite-based positioning systems like GPS degrade in indoor environments, adverse weather conditions, and areas with radio frequency emissions, leading to inaccurate location determination, particularly in multi-floor buildings and adverse outdoor conditions.
Innovation Solution
A positioning system using a network of BLE beacons and WiFi APs, combined with a trained machine learning model, to determine device location based on signal strength readings, providing accurate mapping and network connectivity even in dead zones by altering device states to access available network types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS satellite-based positioning systems are used, then location determination can be achieved in open outdoor environments, but positioning accuracy degrades significantly in indoor environments, adverse weather conditions, and areas with radio frequency emissions
Solution Approach 1:
The patent introduces BLE beacons and WiFi access points as intermediary positioning infrastructure to replace GPS in environments where satellite signals are blocked. These intermediaries create local positioning networks that provide accurate location determination indoors and in adverse conditions by using signal strength measurements from nearby beacons/APs rather than relying on satellite signals that cannot penetrate buildings or weather obstacles.
Solution Approach 2:
The patent replaces the GPS satellite-based electromagnetic positioning system with a ground-based beacon network using Bluetooth Low Energy and WiFi technologies. This substitution enables positioning in environments where satellite signals are blocked by buildings, weather, or RF interference, achieving accurate location determination through local signal strength measurements and fingerprinting techniques.
2Measurement precision
If GPS is used for positioning, then location information can be obtained in outdoor environments, but elevation measurements become inaccurate in multi-floor buildings and high-rise structures
Solution Approach 1:
The patent uses BLE beacons and WiFi access points positioned at known locations within buildings as intermediaries to determine vertical position. By measuring signal strength from multiple floors and comparing against fingerprint databases, the system can accurately identify which floor a device is on, providing reliable elevation information in multi-story structures where GPS satellite geometry becomes degraded.
Solution Approach 2:
The patent transitions from three-dimensional outdoor GPS positioning to a layered indoor positioning model where vertical position is determined by identifying the active floor level. This dimensional adaptation allows accurate location determination in multi-floor buildings by adding floor level identification to the horizontal position coordinates.
3Reliability
If traditional positioning systems are used, then location services can operate with basic infrastructure, but network connectivity is lost in dead zones requiring alternate connectivity solutions
Solution Approach 1:
The patent makes the positioning system universally applicable across both outdoor and indoor environments, as well as in areas with and without traditional network connectivity. By using BLE beacons and WiFi APs that can function independently of cellular networks, the system provides location services and can trigger alternate connectivity methods in dead zones, ensuring reliable operation regardless of environmental conditions or network availability.
Data Source
AI summary
Techniques are disclosed for determining a location of a mobile device within a dead zone. One example computing system includes a non-transitory computer-readable medium; and a processor in communication with the non-transitory computer-readable medium, the processor configured to execute instructions stored in the non-transitory computer-readable medium to: receive a service request from a communication device, the service request comprising one or more signal strength readings associated with a current position of the communication device; determine, using a trained machine learning model, the current position corresponds to a fingerprint within a location map based on the one or more signal strength readings; and output a control signal configured to cause the communication device to alter a state of the communication device based on location information associated with the fingerprint, wherein the state of the communication device comprises a source or type of network connectivity.


