Mobile Device Proximity Detection Without Manual BLE Recalibration
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Solution Overview
Problem
Existing proximity determination methods for mobile devices using Bluetooth Low Energy (BLE) beacons are labor-intensive and require frequent recalibration due to changes in the radio environment, leading to accuracy issues.
Innovation Solution
A method and system that generates a two-dimensional graph based on beacon measurements to determine the most probable position of a mobile device using a probability indicator, calculated from signal strength and distance, allowing for robust proximity determination without manual recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprinting is used to increase positioning precision, then measurement precision is improved, but device complexity increases due to labor-intensive setup and calibration
Solution Approach 1:
The system performs automatic environmental mapping and fingerprint generation without requiring manual intervention. The mobile device autonomously collects signal strength measurements from multiple beacons, builds the fingerprint database, and updates it dynamically, eliminating the need for labor-intensive manual setup and calibration while maintaining high positioning precision
Solution Approach 2:
The system pre-generates fingerprints for all possible device orientations and positions by simulating signal strength measurements in advance. This preliminary action creates a comprehensive fingerprint database that covers various scenarios, eliminating the need for repeated manual calibration when environmental changes occur
2Device complexity
If traditional RSSI threshold comparison is used for proximity determination, then device complexity is reduced, but measurement precision deteriorates due to great variation in RSSI measurements
Solution Approach 1:
Instead of using a single RSSI threshold, the system transforms the approach by using multiple parameters: signal strength measurements from multiple beacons, device orientation data, and pre-generated fingerprints for different orientations. This multi-parameter approach significantly improves proximity determination accuracy while keeping the device complexity manageable through automated processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and reliable proximity detection between a mobile device and a beacon device, independent of physical map calibration, reducing the need for manual adjustments and improving positional accuracy.
Implementation Method 1
Proximity determination of a mobile device can be used for many purposes. For instance, when mobile devices used as electronic keys are based on a wireless technology with a range of more than a few centimetres, e.g. for Bluetooth Low Energy (BLE)
Implementation Method 2
obtaining, from the mobile device, device measurements of signal strength, received in the mobile device, of beacons from the beacon devices
Data Source
Figure 1~2
Figure 3~4
Figure 5A~7
AI summary
It is provided a method for enabling determination of proximity of a mobile device (2a, 2b) to a beacon device (3a). The method is performed by a proximity determiner (1) and comprises the steps of: determining a set of beacon devices (3a-e) being detectable from the mobile device; obtaining beacon measurements of one or two beacon devices in each pair; generating a two-dimensional graph based on the beacon measurements; obtaining device measurements of signal strength of beacons from the beacon devices in the set of beacon devices; finding an optimum in a space defined by the two- dimensional graph; and determining the most probable position of the mobile device in the graph based on the optimum.