Telemetry Engine Location Prediction for Network Traffic Density
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Solution Overview
Problem
Current technologies face challenges in accurately predicting and monitoring wireless network traffic density, especially in indoor locations with poor signal penetration or GPS-denied areas, which impairs the deployment of new base station nodes and expansion of wireless carrier networks.
Innovation Solution
A user device telemetry data management tool that analyzes telemetry data sets, including GPS coordinates, multilateration, IoT device identification, and wireless access point data, to establish and predict user device locations and movement patterns, using machine-learning algorithms to generate profiles for real-time network traffic estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and traditional location methods are used, then location accuracy is improved in open areas, but location determination fails in GPS-denied indoor areas
Solution Approach 1:
The patent segments the location determination process into multiple independent methods: GPS-based location for outdoor areas, Wi-Fi triangulation for indoor areas with wireless access points, and cellular tower multilateration for areas with poor wireless signal penetration. Each method operates independently and can be selected based on the environment, allowing the system to maintain location accuracy across diverse settings including GPS-denied indoor areas.
2Reliability
If wireless signal penetration is poor in indoor locations, then network coverage is reduced, but infrastructure modification is required to improve it
Solution Approach 1:
The patent introduces wireless access points as intermediary devices that relay communication between user devices and the core network infrastructure. These access points can be strategically placed in indoor locations to improve wireless signal penetration without requiring modifications to the main network towers or core network architecture, thereby improving reliability while minimizing infrastructure complexity.
Solution Approach 2:
The patent utilizes the spatial dimension by deploying distributed wireless access points at various locations within indoor environments. This creates multiple signal pathways and relays that bypass physical obstacles, improving network coverage in areas with poor signal penetration without requiring changes to the vertical or core network structure.
3Productivity
If real-time network traffic monitoring is implemented, then network optimization is improved, but data collection requirements increase
Solution Approach 1:
The patent extracts only the essential location and traffic data elements needed for network optimization, such as user device locations, movement patterns, and network traffic density metrics. By filtering and selecting only the most relevant data points rather than collecting all possible telemetry information, the system achieves effective network optimization while managing data collection volumes.
Solution Approach 2:
The patent performs preliminary data processing and filtering at the edge devices and access points before data is transmitted to the core network. Location data is pre-processed to identify movement patterns and traffic density trends, so that only aggregated and pre-analyzed information needs to be transmitted and stored, reducing the overall data volume while maintaining optimization effectiveness.
Data Source
AI summary
A telemetry data computing engine may receive the telemetry data of a user device, via a network, and it may apply a machine learning algorithm to at least one telemetry data and generate a predicted location for the user device. The predicted location may be used to generate a location pattern for the user device and a user device profile for the user device. The user device profile may be routed to the wireless carrier core network.