Mobile Node Network Localization Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing localization schemes for wireless network nodes face inaccuracies in estimating distances between nodes due to reliance on techniques like received signal strength (RSS) and packet success rate (PSR), which are influenced by various network parameters and require additional environmental information.
Innovation Solution
A method involving a mobile node that travels along a predetermined path to collect measurements of RSS or PSR with stationary nodes, utilizing sensors and temporary ground truth systems like Lidar or GPS to provide accurate distance estimates, and iteratively refining these estimates using previous data for improved localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If RSS or PSR techniques are used to estimate distance between network nodes, then localization can be performed without additional hardware, but the measurement precision deteriorates due to dependence on network parameters and environmental factors
Solution Approach 1:
The patent applies preliminary action by performing a training phase before actual localization. During this phase, a mobile node traverses known paths to collect RSS/PSR measurements, which are then used to create accurate distance estimation models. This pre-collected data compensates for the inherent imprecision of RSS/PSR techniques, allowing accurate localization without additional hardware.
Solution Approach 2:
The patent introduces a mobile node as an intermediary that collects measurements along predetermined paths. This mobile node acts as a mediator between the stationary nodes and the localization algorithm, gathering the data needed to create accurate distance models that compensate for the imprecision of direct RSS/PSR measurements between stationary nodes.
2Measurement precision
If a mobile node traverses predetermined paths to collect measurements, then distance estimation accuracy improves, but the time and complexity of the localization process increases
Solution Approach 1:
The training phase with mobile node traversal is performed as a preliminary action during network installation or setup. Although this initial phase requires time, the resulting distance estimation models are reused for subsequent localization operations, making the actual localization process fast and efficient.
Solution Approach 2:
The patent uses partial action by having the mobile node traverse only predetermined paths that provide sufficient data for model creation, rather than requiring complete coverage of all possible node pairs. This selective approach achieves adequate measurement precision while minimizing the time required for the training phase.
3Measurement precision
If multiple measurement techniques and sensor data are integrated, then localization accuracy improves, but the device complexity increases
Solution Approach 1:
The mobile node serves multiple functions: it acts as a measurement collection device, a reference for known positions, and a data source for model training. By making the mobile node multi-functional, the patent integrates multiple measurement techniques without proportionally increasing overall system complexity.
Solution Approach 2:
The system performs self-calibration through the mobile node's traversal and measurement collection. The mobile node automatically gathers the data needed to create accurate distance models, eliminating the need for external calibration equipment or manual setup, thereby reducing operational complexity despite using multiple measurement techniques.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of node localization by leveraging multiple measurement techniques and sensor data, providing a more reliable method for determining the location of stationary nodes in wireless networks.
Implementation Method 1
a temporary ground truth system may be, for example, a Lidar, Global Positioning System (GPS), or a stereo camera
Implementation Method 2
a temporary ground truth system may be, for example, a Lidar, Global Positioning System (GPS), or a stereo camera
Implementation Method 3
the received signal strength (RSS) between the mobile node and one or more stationary nodes of the network
Implementation Method 4
the packet success rate (PSR) of transmission(s) between the mobile node and one or more stationary nodes of the network
Data Source
AI summary
A method and system for determining a location of at least one stationary node of a wireless network, which includes providing a predetermined path within a geographic space of the wireless network, prior to localization, moving a mobile node along the predetermined path, measuring a network parameter with respect to the mobile node as it moves along the predetermined path, and performing a localization scheme to estimate the location of the at least one stationary node using the measured network parameter.