Proximity Beacon Location Management via Crowd-Sourced Triangulation
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Solution Overview
Problem
Existing proximity beacon systems lack an efficient method to determine and manage the location of beacons, especially in indoor environments, leading to inaccuracies and increased administrative effort due to their inability to detect their own location and reliance on manual input, which becomes complex as the number and density of beacons increase.
Innovation Solution
A beacon data ecosystem that utilizes mobile devices in proximity to report their location and signal strength to an application server, which then triangulates the beacon's location and updates it iteratively, allowing for accurate mapping and management of beacon positions, including detection range areas and battery levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If proximity beacons lack GPS receivers to remain inexpensive and energy-efficient, then cost and energy consumption are reduced, but the ability to automatically detect beacon location is lost
Solution Approach 1:
The system enables beacons to automatically determine their own locations through crowd-sourced data from mobile devices. The application server collects location data and beacon detection data from multiple mobile devices, then automatically calculates and updates beacon locations without manual intervention, allowing beacons to serve themselves location-detection functions despite lacking GPS hardware.
Solution Approach 2:
Mobile devices act as intermediaries between the beacons and the location determination system. The mobile devices detect beacon signals and report their own GPS locations to the application server, which then uses this intermediary data to infer beacon locations, eliminating the need for direct GPS receivers in the beacons.
2Ease of operation
If administrators manually input beacon locations, then initial setup is simple, but accuracy and reliability deteriorate when beacons are moved or misplaced
Solution Approach 1:
The system continuously collects feedback data from mobile devices detecting beacons and uses this feedback to automatically update beacon locations. The application server processes ongoing detection data and adjusts beacon positions in real-time, creating a closed-loop system that maintains accuracy even when beacons are moved, eliminating the need for repeated manual repositioning.
3Area of stationary object
If the number and density of proximity beacons increase, then coverage improves, but administrative complexity and time required to organize data increases
Solution Approach 1:
The application server automatically performs the complex task of organizing and correlating data from numerous beacons and mobile devices. The system self-manages the increasing complexity by automatically processing crowd-sourced data from multiple sources, calculating beacon locations, and maintaining the beacon database without requiring proportional increases in administrative effort.
4Device complexity
If proximity beacons rely on mobile devices to report their locations, then hardware requirements are reduced, but reliability deteriorates when few mobile devices are present
Solution Approach 1:
The system merges data from multiple mobile devices to determine beacon locations. By combining location reports from several different mobile devices that detect the same beacon, the system achieves reliable location determination even when individual device samples are sparse, as the aggregated data provides sufficient statistical confidence.
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
This solution enables reliable and automated determination of proximity beacon locations, reducing administrative complexity and improving data accuracy, while also detecting potential issues such as beacon movement or malfunction through iterative updates and crowd-sourced data.
Implementation Method 1
mobile devices in range of the proximity beacon to report to the administrator device that the proximity beacon was detected by the mobile device
Implementation Method 2
an application server determines a location of the proximity beacon based on location data from a plurality of mobile devices and a signal strength measurement value associated with one or more signals transmitted between the proximity beacon and each mobile device
Data Source
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Figure 3A
AI summary
Once a proximity beacon is installed within a venue, mobile devices receive signals transmitted by the proximity beacon. These mobile devices then report their own locations to an application server. These mobile devices also report a signal strength to the application server, the signal strength associated with the signal from the proximity beacon. The application server determines the location of the proximity beacon by triangulating based on the locations of the mobile devices and on the signal strengths from the mobile devices. The location of the proximity beacon is then transmitted to a front-end device that then displays the location of the proximity beacon on a map along with other locations of other proximity beacons. The map> may also display range areas and battery levels of the proximity beacon, and any other data that can be gathered by the application server from the mobile devices or the proximity beacon.