Sensor Grouping for Automatic Sub-Area Location Assignment
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Solution Overview
Problem
Existing solutions for locating sensors and actuators in smart environments are costly, time-consuming, and prone to errors, especially in large areas, as they require manual input and updates, and rely on beacon systems that need labor-intensive installation and maintenance.
Innovation Solution
A method that groups sensors based on data analysis to determine the location of unknown sensors by associating them with known sensors within predefined sub-areas, using data partitioning techniques like unsupervised classification algorithms, and propagating location information automatically, reducing the need for manual updates and beacon systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to obtain sensor location information from authorized professionals or users, then location data can be acquired, but the process becomes expensive and very time-consuming especially when the number of objects is high
Solution Approach 1:
The system enables sensors to automatically determine their own locations through data partitioning and grouping algorithms, eliminating the need for manual location input by professionals or users. Each sensor autonomously contributes to the location determination process by providing its sensor data, which is then processed to identify groups and propagate location information automatically.
Solution Approach 2:
The patent replaces the mechanical/manual process of location data collection with an automated computational system. Instead of physically visiting each location to record sensor positions, the system uses data partitioning, grouping algorithms, and automatic location propagation to determine sensor locations computationally, significantly reducing time and labor requirements.
2Reliability
If manual updating is performed when network configuration changes, then location information can be maintained, but the process generates errors and requires careful tracking that users do not have time to perform
Solution Approach 1:
The system continuously monitors sensor data and automatically detects changes in network configuration. When a sensor is added, removed, or moved, the data partitioning and grouping algorithms automatically recalculate locations and propagate updated information throughout the system, providing continuous feedback-driven updates without manual intervention.
Solution Approach 2:
The system performs preliminary grouping of sensors based on their data characteristics before location propagation. This pre-processing step organizes sensors into logical groups that facilitate automatic location determination, so when configuration changes occur, the system can quickly identify affected groups and propagate location updates only where necessary, improving both speed and reliability.
3Measurement precision
If beacon systems are deployed to track fixed objects, then location information can be obtained, but significant costs are incurred in labor, configuration, and maintenance especially in large buildings
Solution Approach 1:
The patent extracts the location determination function from the sensors themselves rather than requiring external beacon infrastructure. Each sensor's own data is used to determine its location, and this information is propagated to other sensors. This eliminates the need for separate beacon devices, reducing device complexity and infrastructure requirements while maintaining location tracking capability.
Solution Approach 2:
The system makes the sensor data serve multiple functions: both its primary sensing function and location determination. By partitioning sensor data to identify groups and propagate location information, the same sensor network that collects environmental data also autonomously determines locations, eliminating the need for dedicated beacon infrastructure and reducing overall system complexity.
Data Source
AI summary
A method for locating sensors situated in a geographical area including of at least one reference sub-area. the sensors include: at least one first sensor, each first sensor being associated with a reference sub-area in which it is situated; and at least one second sensor, each second sensor not being associated with any sub-area. The method includes: grouping the sensors into a plurality of groups based on data from the sensors; for at least one group of sensors, determining whether the group includes a first sensor; an if the group includes a first sensor, associating each second sensor of the group with the sub-area corresponding to the first sensor.