Beacon Placement Evaluation Using Geometric Mean Approximation
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Solution Overview
Problem
Designing beacon placement for indoor localization systems based on Wi-Fi or Bluetooth Low Energy (BLE) signal strength is challenging due to the need for expert knowledge and high computational costs, especially when scaling these systems.
Innovation Solution
A computer-implemented method for evaluating beacon placement using a probability distribution model that approximates the arithmetic mean with a geometric mean to calculate an evaluation metric, allowing for efficient assessment of localization errors and optimizing beacon placement in indoor environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge and detailed computational methods are used for beacon placement, then localization accuracy is improved, but computational cost and system complexity increase
Solution Approach 1:
The patent changes the computational parameter from arithmetic mean to geometric mean in the probability distribution calculation. This parameter change simplifies the computational complexity while maintaining localization accuracy, as the geometric mean reduces the computational burden of evaluating placement quality metrics without sacrificing the precision of location estimation.
Solution Approach 2:
The patent employs approximate computational methods that are less resource-intensive and can be executed efficiently. By using geometric mean approximation instead of exact arithmetic mean calculations, the system achieves comparable localization accuracy with reduced computational resources, effectively using 'cheaper' computational operations.
2Measurement precision
If expert knowledge is required for beacon placement design, then localization accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables the system to automatically evaluate and optimize beacon placement without requiring expert knowledge. The geometric mean-based probability distribution calculation allows the system to self-assess placement quality and guide optimization, eliminating the need for human experts to manually design beacon configurations while maintaining high localization accuracy.
Solution Approach 2:
By changing the computational approach to use geometric mean, the system becomes more automated and easier to operate. This parameter change simplifies the evaluation metric computation, making the system more accessible to non-experts who can now use the beacon placement system without requiring specialized knowledge of complex computational methods.
3Measurement precision
If traditional evaluation methods are used for beacon placement, then measurement accuracy is maintained, but productivity deteriorates due to high computational cost
Solution Approach 1:
The patent directly addresses the computational efficiency problem by changing the mathematical parameter from arithmetic mean to geometric mean. This parameter change maintains the accuracy of placement evaluation while significantly reducing computational cost, thereby improving productivity and enabling faster beacon placement optimization without sacrificing measurement precision.
Data Source
AI summary
A placement of a set of devices in an environment is evaluated. At least a plurality of neighboring locations is selected for a target location in the environment. A probability of an estimated location conditioned on the target location is calculated for each of at least the plurality of neighboring locations as the estimated location by using an observation model for obtaining a set of observation values given a location under the placement. An evaluation metric is computed by using the probability calculated for each of at least the plurality of neighboring locations.