RF Device Tracking via RSSI Averaging and Periodic Scanning
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Solution Overview
Problem
Current crowd-source location systems for tracking devices face challenges in widespread adoption, reliability of information, accuracy of scanning, and resource utilization, particularly due to user privacy concerns and battery drain from continuous Bluetooth scanning.
Innovation Solution
A system comprising software applications on locator devices and personal communication devices, with a server, that registers trackable devices, detects their presence using radio-frequency scanning, determines new locations by averaging received signal strength indicator (RSSI) values, and communicates geographical locations, incorporating a peripheral scan cache and configuration parameters to optimize scanning frequency and notification criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous Bluetooth scanning is performed to track devices, then location accuracy is improved, but battery consumption increases
Solution Approach 1:
The system implements periodic scanning with variable intervals instead of continuous scanning. The scan interval dynamically adjusts based on device priority, movement detection, and battery status, allowing the system to maintain location tracking accuracy while significantly reducing power consumption during idle periods.
Solution Approach 2:
The system performs preliminary actions by pre-calculating scan schedules, pre-loading device information, and using machine learning models to predict device locations before actual scanning occurs. This reduces the frequency and intensity of actual scanning operations, thereby saving battery power.
2Reliability
If crowd-source location is enabled to track lost devices, then device tracking capability is improved, but user privacy concerns increase
Solution Approach 1:
The system introduces a trusted intermediary layer (the application server) that mediates all data exchanges between users. Personal identifiers are never shared directly between users; instead, the server uses token-based anonymous identification and location relaying, where the server acts as an intermediary that protects user privacy while enabling crowd-source tracking.
Solution Approach 2:
The system creates anonymous copies of user information for crowd-source matching. Instead of sharing real user identities, the system generates pseudonymous device identifiers and anonymized location data that can be matched across users without revealing personal information, thereby enabling tracking while protecting privacy.
3Adaptability or versatility
If multiple tracker devices are tracked simultaneously, then tracking versatility is improved, but information reliability decreases
Solution Approach 1:
The system segments tracking information by creating separate logical containers for each tracked device, with dedicated data structures and processing pipelines. Each tracker's data is handled independently with device-specific parameters, reducing cross-contamination of information and maintaining reliability even when tracking multiple devices simultaneously.
4Difficulty of detecting and measuring
If scanning range is increased to detect more devices, then detection capability is improved, but false detections increase
Solution Approach 1:
The system implements feedback mechanisms where detection results are continuously validated against multiple criteria including signal strength trends, device response patterns, and historical data. The system adjusts scanning parameters based on feedback from previous detections, reducing false positives by learning from patterns and confirming detections through multiple verification steps.
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
Enhances the reliability and accuracy of tracking devices out of range, reduces battery consumption, and incentivizes user participation through a finder's fee mode, while smoothing out RSSI variations for precise location determination.
Implementation Method 1
a radio-frequency scanning component... detecting the registered trackable device by the radio-frequency scanning component
Implementation Method 2
determine when the sighting meets criteria defining the sighting as a new location of that registered trackable device, the criteria including a received signal strength indicator (RSSI) value
Data Source
AI summary
System and methods for locating radio-frequency-emitting devices. The method may comprise registering, on a downloadable software application on a first locator device, a trackable device, thereby creating a registered trackable device, which emits a radio frequency signal, then detecting, via a radio frequency scan conducted by a second locator device, a sighting of the registered trackable device; determining, at the second locator device, whether the sighting of the registered trackable device meets criteria defining the sighting as a new location of that registered trackable device, the criteria including a received signal strength indicator (RSSI) value; communicating, when the sighting of the registered trackable device meets the criteria, a report of the new location to a server, the report including a geographical location of the registered trackable device; and sending a notification from the server to the first locator device that a new location of the registered trackable device has occurred.