Edge Device Location Using Cellular Network Data Instead of GPS
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Solution Overview
Problem
Existing methods for determining the location of mobile edge devices are resource-intensive, power-consuming, and unreliable due to reliance on GPS, which can fail in adverse conditions or when obstructed, leading to increased costs and inefficiencies for telecommunications network providers.
Innovation Solution
A device edge controller uses a machine learning model to process edge parameters, geographic data, and real-time metadata to calculate the location of mobile edge devices, eliminating the need for GPS by leveraging cellular radio access network and cellular core network data, and discarding unreliable locations to determine the most confident actual location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used to determine device location, then location accuracy is improved, but power consumption and device complexity increase
Solution Approach 1:
The patent extracts the location determination function from the mobile edge device itself and relocates it to the network side (core network or cloud platform). The device only provides basic identification data (IMEI, cell ID), while the network performs complex location calculation using machine learning models and multi-source data fusion, thereby eliminating the need for power-intensive GPS hardware in the device.
Solution Approach 2:
The patent introduces a network-side location server as an intermediary between the mobile edge device and the location determination system. This server collects data from multiple sources (cellular network data, WiFi data, sensor data), applies machine learning models, and calculates the final location, shielding the device from the complexity of location determination while providing accurate results.
2Reliability
If GPS hardware is installed in mobile edge devices, then location determination capability is improved, but device cost and complexity increase
Solution Approach 1:
The patent removes the location determination functionality from the mobile edge device and implements it in the network. The device only retains basic communication and identification functions, while the network handles complex location calculation using machine learning models that process multiple data sources, thereby simplifying the device architecture and reducing costs.
Solution Approach 2:
The network-side location system serves multiple functions: it determines location using various methods (cellular triangulation, WiFi fingerprinting, sensor-based positioning), provides location services to multiple devices simultaneously, and offers fallback mechanisms when one method fails, making the system universally applicable and robust.
3Measurement precision
If GPS is used for location determination, then location accuracy is improved, but system reliability deteriorates in adverse conditions
Solution Approach 1:
The patent changes the fundamental parameter of location determination from satellite-based (GPS) to network-based (cellular, WiFi, sensor data). This parameter change enables the system to operate reliably in GPS-denied areas such as urban canyons, indoors, and dense forests by using alternative measurement parameters like signal strength, timing information, and device sensor data.
Solution Approach 2:
The patent implements multiple location determination methods and fallback mechanisms in advance. When GPS is unavailable or inaccurate, the system automatically switches to alternative methods (cellular network triangulation, WiFi-based positioning, or sensor fusion), ensuring continuous location service reliability without requiring GPS hardware in the device.
4Measurement precision
If multiple data sources are processed by machine learning model, then location accuracy is improved, but computing resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing location data, cell information, and machine learning models in the network before actual location determination is needed. When a location request arrives, the system quickly queries pre-processed data and applies the trained machine learning model, avoiding the need for real-time complex calculations and significantly reducing processing time.
Data Source
AI summary
A device may receive edge parameters, geographic data, traffic data, and real-time metadata associated with an approximate location of a mobile edge device and a device edge. The device may receive a request for an actual location of the mobile edge device and the device edge, and may process the edge parameters, the geographic data, the traffic data, and the real-time metadata, with a machine learning model, to calculate multiple locations. The device may discard locations that fail to fit the edge parameters, the geographic data, the traffic data, and the real-time metadata, to generate a set of locations, and may select, from the set of locations, a location with a greatest location confidence determination as the actual location of the mobile edge device and the device edge.