Blindfolded Node Location Estimation via Geometric Center
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Solution Overview
Problem
Current range-free location estimation techniques in wireless sensor networks suffer from limited accuracy and complexity, especially in 3-D systems and irregularly deployed networks, requiring costly infrastructure and complex decision processes.
Innovation Solution
A range-free location estimation method using monotonic functions like received signal strength (RSS) or time of arrival (TOA) to determine the location of blindfolded nodes, where the location is estimated by combining signal strength measurements with reference nodes' locations and reducing probable areas through geometric center calculation, allowing for improved accuracy without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If range-based location estimation techniques are used, then measurement precision is improved, but device complexity and cost increase due to costly infrastructure and hardware requirements
Solution Approach 1:
The patent extracts the location estimation function from complex range-based systems and implements it through range-free techniques using only signal strength measurements and anchor nodes, removing the need for specialized ranging hardware and infrastructure
Solution Approach 2:
The patent uses inexpensive signal strength measurements and simple anchor nodes instead of expensive ranging infrastructure, achieving location estimation through computationally intensive but hardware-simple methods
2Device complexity
If traditional range-free techniques like proximity-based averaging are used, then device complexity is reduced, but measurement precision deteriorates due to very limited location estimation accuracy
Solution Approach 1:
The patent segments the estimation process into multiple stages: initial coarse estimation using anchor nodes, iterative refinement using geometric center calculations, and convergence to precise location, transforming a simple but inaccurate method into a multi-stage precise system
Solution Approach 2:
The patent performs preliminary location estimation using anchor nodes to establish initial probable regions, then uses these preliminary results as the basis for iterative refinement, where each iteration builds upon the previous estimation to improve accuracy
3Measurement precision
If triangular region methods are used for location estimation, then measurement precision is improved through area narrowing, but device complexity increases due to complicated decision processes
Solution Approach 1:
Instead of using complex triangular region intersection methods that require determining node positions relative to multiple triangles, the patent inverts the approach by using geometric center calculations of probable regions, simplifying the decision process while maintaining precision
Solution Approach 2:
The patent transitions from 2-D triangular region analysis to 3-D probable region visualization where location precision is represented as a volume in parameter space, allowing simpler geometric center calculations to achieve the same narrowing effect
4Measurement precision
If triangulation methods are used in irregularly deployed networks, then location estimation is achieved, but productivity decreases due to inefficiency in irregular deployments
Solution Approach 1:
The patent implements a dynamic iterative process that adapts to irregular network deployments by continuously refining location estimates through geometric center calculations, where the number of iterations and convergence criteria can be adjusted based on network characteristics rather than requiring fixed triangular geometries
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 method provides enhanced location estimation accuracy and stability, applicable to both regular and irregularly deployed networks, without the need for costly infrastructure, overcoming previous limitations in accuracy and decision complexity.
Implementation Method 1
monotonic functions, such as received signal strength (RSS) or time of arrival (TOA), to determine the location of blindfolded nodes
Implementation Method 2
monotonic functions, such as received signal strength (RSS) or time of arrival (TOA), to determine the location of blindfolded nodes
Data Source
AI summary
A method for estimating the location of a blindfolded node (235) in a wireless network having reference nodes (225, 230) is provided. The reference nodes (225, 230) are combined into pairs (301) and each pair is checked to determine if the reference nodes are within each other's communication rage (304). A plurality of probable regions (315) for the blindfolded node are obtained (313, 315). These probable regions are overlapped (320), and the blindfolded node's estimated location is estimated to be the geometric center of the overlapped regions (325).