Network Device Site Detection Using Beacon Transmission Patterns
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Solution Overview
Problem
Conventional inventory systems for network devices require manual geo-location updates, are time-consuming, prone to human error, and lack automation for network topology changes, with inaccuracies up to 500 km or more, making them insufficient for accurate inventory management in multi-site networks.
Innovation Solution
A system using unsupervised and supervised machine learning to predict network device locations by analyzing packet transmission characteristics, such as latency and hop counts, without GPS, through clustering techniques and machine learning models trained on known beacon devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual geolocation methods are used, then device location information can be obtained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical geolocation processes with an automated machine learning system. The system uses packet transmission characteristics and latency measurements to automatically determine device locations, substituting human operators with computational algorithms that process network data to predict geographic positions.
Solution Approach 2:
The inventory system performs self-updating through automated machine learning processes. The system continuously monitors packet transmission characteristics and automatically updates device location information without requiring external manual intervention, enabling the system to maintain itself autonomously.
2Reliability
If manual inventory updates are performed, then device location changes can be tracked, but human errors occur and updates are prone to inaccuracies
Solution Approach 1:
The patent replaces manual inventory update processes with automated machine learning algorithms. These algorithms analyze packet transmission characteristics and automatically update device location information, eliminating human error-prone data entry and manual tracking processes.
Solution Approach 2:
The system continuously monitors packet transmission characteristics and uses this feedback to automatically detect and respond to device location changes. The machine learning model processes ongoing network data streams to identify when devices move and updates inventory information accordingly, creating a closed-loop system that self-corrects.
3Productivity
If conventional geo-location algorithms are used, then location estimation can be performed, but accuracy is insufficient with errors up to 500 km
Solution Approach 1:
The patent changes the parameters used for location determination from conventional GPS coordinates to packet transmission characteristics including latency, packet loss, and network hop counts. By measuring multiple transmission parameters between devices and comparing them against known beacon locations, the system achieves accurate site identification without relying on traditional geolocation methods.
Solution Approach 2:
The patent transitions from two-dimensional GPS coordinate-based location to a multi-dimensional approach using packet transmission characteristics. The system analyzes latency, packet loss, hop count, and other network parameters to determine location, adding multiple measurement dimensions that improve accuracy over conventional single-point GPS methods.
4Productivity
If automated machine learning processes are used, then geolocation prediction is accurate and fast, but the system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting packet transmission characteristics and pre-training machine learning models with known device locations. This preparation work is done in advance so that when location prediction is needed, the system can quickly process new data using pre-established patterns and models, reducing real-time computational complexity.
Solution Approach 2:
The patent introduces beacon devices as intermediaries to simplify the location determination process. These known-location devices serve as reference points that mediate between unknown devices and the central system, enabling the machine learning model to predict locations through comparative analysis rather than direct complex calculations.
Data Source
AI summary
Systems and methods for predicting the location of unknown network devices within a particular site of a multi-site network are provided. A method, according to one implementation, includes obtaining transmission characteristics related to any of packets and frames transmitted between an unknown network device and a plurality of beacon devices. Each beacon device is a network device associated with a fixed site location within a multi-site network. The method also includes utilizing the transmission characteristics to predict a site within the multi-site network where the unknown network device is located.