Navigation Routing Using RF Signal Strength Metrics
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Solution Overview
Problem
Existing navigation systems do not effectively utilize network connectivity data to ensure reliable network connections during route planning, leading to inefficient routing and increased network reconnection attempts.
Innovation Solution
A computer-implemented method and system that collects and analyzes network connectivity data from user devices to generate routes that maintain a threshold level of network connectivity, using machine-learning models to predict connectivity metrics and prioritize connection strength and data throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If navigation systems use traditional routing algorithms that prioritize shortest distance or fastest time, then route efficiency is improved, but network connectivity reliability deteriorates
Solution Approach 1:
The patent applies parameter changes by incorporating network connectivity parameters (signal strength, data throughput) into the route selection process. The system evaluates multiple routes based on both traditional efficiency metrics and network connectivity metrics, dynamically adjusting route recommendations based on real-time and historical connectivity data. This resolves the contradiction by changing the parameters considered in route planning to include both efficiency and reliability factors.
Solution Approach 2:
The patent implements preliminary action by pre-collecting and analyzing network connectivity data from multiple sources before route planning occurs. The system builds a database of historical connectivity information and uses machine learning models to predict future connectivity conditions along potential routes. This allows the navigation system to proactively select routes with predicted reliable connectivity rather than reacting to connectivity issues after they occur.
2Reliability
If navigation systems collect and analyze extensive network connectivity data from multiple sources, then network connectivity reliability is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling user devices to autonomously collect, process, and contribute their own network connectivity data to a shared database. Each device performs local measurements of signal strength and data throughput, then contributes this data to improve overall route recommendations. This distributed approach reduces server-side processing complexity while maintaining high reliability through aggregated data from many independent sources.
Solution Approach 2:
The patent uses an intermediary approach by introducing a centralized database and machine learning model layer that mediates between raw connectivity data from multiple devices and the route planning function. This intermediary structure organizes and processes the complex data from various sources, transforming it into simplified route recommendations. The intermediary layer manages the complexity by providing a structured interface between data collection and route selection.
3Reliability
If navigation systems prioritize routes with stronger network signals, then network connectivity reliability is improved, but travel time may increase
Solution Approach 1:
The patent applies partial action by providing users with multiple route options rather than forcing a single optimal route. The system calculates several candidate routes with varying degrees of network connectivity quality and presents them to the user with information about expected connectivity conditions. This allows users to partially optimize for connectivity while accepting reasonable travel times, rather than requiring complete optimization for either metric.
Solution Approach 2:
The patent implements dynamics by making route recommendations adaptive and changeable based on real-time conditions. The system continuously monitors actual network connectivity along recommended routes and can dynamically suggest alternative routes if connectivity deteriorates. This dynamic approach allows the system to balance connectivity reliability and travel time flexibly, adjusting recommendations based on current conditions rather than relying on static pre-planned routes.
Data Source
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AI summary
The present disclosure is directed to a system and method for providing improved routing options using connectivity data. The method includes receiving, from a plurality of user devices, network connectivity data, the network connectivity data including associated geographic data. The method includes generating, using a machine learned model, one or more connectivity metrics for a plurality of geographic areas based on the network connectivity data. The method includes receiving a navigation request from a first user device, the navigation request including a destination location, an origin location, and one or more request parameters. The method includes determining a route to the destination location from the origin location that meets one or more connection criteria for the route based, at least in part, on connectivity metrics associated with one or more geographic areas along the route. The method includes transmitting data describing the route to a user device for display.