Tracking BLE Devices via Heuristic Address Resolution
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Solution Overview
Problem
Bluetooth Low Energy (BLE) devices employ address randomization to enhance user privacy, but this makes it challenging to track their presence accurately without compromising privacy, which is essential for applications like presence analytics, crowd estimation, and indoor navigation.
Innovation Solution
A system and method that utilize a network of Bluetooth access points to collect and analyze advertisement packets from BLE devices, employing heuristic rules and stochastic/probability equations to identify and track devices despite their changing Unique Identity Addresses (UIADDs) by leveraging fixed, persistent, behavioral, measured, and pattern data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If address randomization is employed to enhance user privacy, then privacy protection is improved, but device tracking capability deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing multiple data types (UIADD, manufacturer data, device name, RSSI, location) before address randomization occurs. This preliminary data collection enables later identification and tracking of devices even when their UIADD changes, resolving the contradiction between privacy protection and tracking capability.
Solution Approach 2:
The patent introduces intermediary data elements (manufacturer data, device name, RSSI patterns, location information) that serve as mediators between the randomized UIADD and the tracking system. These intermediaries maintain device identity continuity despite address changes, allowing tracking while preserving privacy.
2Measurement precision
If multiple data types are collected and analyzed to track devices through UIADD changes, then device tracking capability is improved, but system complexity increases
Solution Approach 1:
The system segments the tracking problem into distinct data types (UIADD, manufacturer data, device name, RSSI, location) and processes each segment separately using specialized algorithms. This segmentation manages complexity by breaking down the overall tracking task into manageable, independent analysis components.
Solution Approach 2:
The system implements feedback mechanisms where collected data from multiple sources continuously refines device identification accuracy. The analysis results feed back into the tracking process, improving device tracking capability while managing system complexity through iterative refinement rather than complex one-time processing.
Data Source
AI summary
Systems and methods for method for resolving Bluetooth device identity regardless of changes in MAC (Media Access Control) addresses are disclosed. For example, a processing system is disclosed that can establish a set of recognized Bluetooth devices based on a first set of data, receive a second set of data measured during a second time period subsequent to the first time period, and in an instance in which a particular UIADD from the second set of data is not found in the first set of data, determine whether a candidate Bluetooth device that transmitted the particular UIADD is a specific recognized Bluetooth device of the set of recognized Bluetooth devices established based on the first set of data.