RSSI Grading for Location Measurement in Low Node Sensor Networks
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Solution Overview
Problem
In resource-limited defense sensor networks with low node density, existing location measurement methods face accuracy issues due to errors in received signal strength indication (RSSI) and insufficient node participation, making precise location tracking challenging or impossible.
Innovation Solution
A method and apparatus that classify irregular communication RSSIs into three levels using two sensing nodes, calculating the object's range based on these levels, and predicting its final location by estimating velocity from previous location information and presumed locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If RSSI-based range measurement is used in resource-limited sensor networks, then device complexity is reduced, but measurement precision deteriorates due to multi-path fading errors
Solution Approach 1:
The patent segments the RSSI measurement process into three distinct grades (first, second, third grades) based on signal strength thresholds. This segmentation allows the system to handle different measurement scenarios differently, improving overall measurement precision while maintaining simplicity in each grade's processing method.
Solution Approach 2:
The patent changes the parameter of RSSI interpretation by introducing grade-based range determination. Instead of directly using raw RSSI values, the system transforms RSSI into graded categories that correspond to different distance ranges, compensating for multi-path fading effects and improving measurement accuracy in resource-limited environments.
2Device complexity
If location measurement uses only one or more close nodes within communication range, then device complexity is minimized, but measurement precision deteriorates when node density is low
Solution Approach 1:
The patent introduces a temporal dimension to location measurement by calculating presumed locations at multiple time points and using movement prediction. This allows the system to achieve better location accuracy with fewer spatial nodes by utilizing time-series data and motion dynamics, effectively compensating for low node density without increasing device complexity.
3Device complexity
If range-free method is used with low sensor node density, then device complexity is reduced, but measurement precision deteriorates compared to range-based method
Solution Approach 1:
The patent merges the advantages of both range-based and range-free methods by using RSSI-based grading (range-based) combined with movement prediction and presumed location calculation (range-free elements). This hybrid approach achieves higher measurement precision than either method alone while maintaining relatively low device complexity suitable for resource-limited sensor nodes.
Data Source
AI summary
Disclosed herein are a location measurement method and apparatus. The apparatus includes a first grading unit, a first presumed line calculation unit, a second grading unit, a second presumed lined calculating unit, a presumed location calculation unit, and a final location calculation unit. The first grading unit determines the grade of a first RSSI. The first presumed line calculation unit calculates the range of the object from a first node based on the grade of the first RSSI. The second grading unit determines the grade of a second RSSI. The second presumed line calculating unit calculates the range of the object from a second node based on the grade of the second RSSI. The presumed location calculation unit calculates two presumed locations. The final location calculation unit determines one of the two presumed locations to be the final location of the object.