BLE Beacon Proximity Mapping Without Manual Recalibration
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Solution Overview
Problem
Existing proximity determination methods for mobile devices using Bluetooth Low Energy (BLE) beacons are labor-intensive to set up and recalibrate, and RSSI measurements are unreliable due to environmental changes and device positioning variability.
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 and labor intensity increase significantly
Solution Approach 1:
The system performs preliminary action by having mobile devices automatically collect and store beacon signal measurements (RSSI values) from multiple locations during normal operation. This pre-collected data forms a fingerprint database that enables future positioning without requiring manual calibration sessions, thus improving positioning precision while avoiding the labor-intensive setup of traditional fingerprinting systems.
Solution Approach 2:
The system implements self-service by automatically performing the entire fingerprinting process without human intervention. Mobile devices autonomously collect beacon measurements, the server automatically processes this data to create location profiles, and the system continuously updates the fingerprint database as new data becomes available. This eliminates the need for manual calibration sessions and reduces device complexity while maintaining high positioning precision.
2Ease of operation
If traditional RSSI threshold comparison is used for proximity determination, then ease of operation is improved, but reliability deteriorates due to signal strength variations
Solution Approach 1:
The system applies parameter changes by transitioning from using absolute RSSI threshold values to using relative RSSI differences and signal strength patterns. Instead of comparing a single RSSI value against a fixed threshold, the system analyzes the pattern of RSSI values from multiple beacons and compares them against stored location profiles. This approach maintains ease of operation while significantly improving reliability by compensating for signal variations caused by device positioning and environmental factors.
Solution Approach 2:
The system introduces an intermediary layer of probabilistic location estimation between the raw RSSI measurements and the final proximity determination. The server acts as an intermediary that processes RSSI data, compares it against the fingerprint database, and calculates the probability that the mobile device is at a specific location. This intermediary processing step filters out noise and variations in individual RSSI measurements, thereby improving reliability while keeping the overall system simple to operate.
3Measurement precision
If fingerprint re-calibration is performed to maintain accuracy, then measurement precision is improved, but productivity decreases due to labor-intensive requirements
Solution Approach 1:
The system implements dynamics by making the fingerprint database adaptive and continuously evolving. Instead of performing periodic manual re-calibration sessions, the system automatically incorporates new RSSI measurements collected from mobile devices into the fingerprint database in real-time. The location profiles are dynamically updated as new data becomes available, allowing the system to adapt to environmental changes, furniture movements, and beacon additions automatically. This maintains high positioning accuracy while eliminating the productivity loss associated with manual re-calibration efforts.
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, independent of physical map calibration, reducing setup and maintenance efforts while improving positioning precision.
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
Another way in which proximity between the mobile device and another device, such as an electronic lock, can be determined is by taking RSSI measurements in either direction and comparing the RSSI measurement to certain threshold values
Data Source
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.