Robot-Measured Network Signatures for Continuous Device Localization
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Solution Overview
Problem
Current methods for measuring and mapping Wi-Fi and cellular coverage rely on human operators, which is impractical due to inaccuracies, high costs, and the inability to provide continuous, temporally accurate coverage maps.
Innovation Solution
The use of robots equipped with sensors to collect network signature measurements, which are then used to generate discretized coverage maps. These maps are used to localize devices within an environment by matching network signatures from devices with those stored in the coverage maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators are used to measure and map Wi-Fi and cellular coverage, then coverage maps can be generated, but the measurements are inaccurate, costly, and cannot provide continuous temporal accuracy
Solution Approach 1:
The system enables automated coverage map generation where robots autonomously navigate environments, collect network signature measurements, and generate coverage maps without human intervention. The robots self-service the entire measurement and mapping process, eliminating manual operation requirements while improving accuracy and continuity of coverage map data
Solution Approach 2:
The patent replaces the mechanical human operator system with an automated robotic system equipped with sensors. The robots use electronic sensors to collect network signatures and automatically process this data into coverage maps, substituting manual mechanical measurement processes with automated electronic detection and processing systems
2Productivity
If human operators manually measure coverage, then coverage data can be collected, but the process is costly and impractical for continuous monitoring
Solution Approach 1:
The robotic system enables continuous coverage mapping operations where robots can autonomously navigate and collect network signature data continuously over time. This provides temporal accuracy and continuous monitoring capability that manual methods cannot achieve, as robots can operate without interruption and repeatedly map coverage areas
Solution Approach 2:
The system uses multiple relatively simple robotic units rather than expensive manual operations. The robots are designed as cost-effective automated devices that can be deployed in numbers to perform coverage mapping, replacing the high cost of manual human operations with cheaper automated robotic systems
3Measurement precision
If coverage maps are discretized into regions with network signatures, then device localization accuracy improves, but system complexity increases
Solution Approach 1:
The coverage map is divided into discrete regions, each associated with specific network signatures. This segmentation allows the system to match device network signatures against regional signatures to determine location. The discretization into manageable regions improves localization accuracy while keeping each region's data structure simple and comparable
Data Source
AI summary
Systems and methods for localizing devices using network signatures and coverage maps measured by robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may generate a coverage map based on measurements collected during operation of the robot. The coverage map generated by the robot may be temporally accurate such that a device may be localized within the coverage map based on a received network signature from the device. The network signature comprising a measure of amplitudes of Wi-Fi networks and/or cellular networks at a point within an environment of the coverage map.