Wireless Beacon Placement Optimization Using Building Floorplan Simulation
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Solution Overview
Problem
Conventional methods for indoor localization using GPS are ineffective indoors due to signal attenuation, and uniform placement of wireless beacons in buildings often results in sub-optimal positioning, leading to either insufficient or excessive beacon deployment.
Innovation Solution
A computer-implemented system that constructs a floorplan representation of a building, simulates wireless signal coverage, and employs a multi-objective optimization algorithm to determine optimal beacon placement, balancing coverage, cost, and distribution, using evolutionary algorithms to iteratively refine beacon positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If wireless beacons are uniformly spread throughout the building, then the installation process is simple, but the positioning accuracy and optimization are compromised
Solution Approach 1:
The patent applies local quality by transitioning from uniform beacon distribution to non-uniform distribution where beacons are strategically positioned based on local building characteristics. The system analyzes specific areas (rooms, hallways, floors) and places beacons according to local requirements for positioning accuracy, such as concentrating beacons in areas requiring higher precision while reducing density in areas with sufficient coverage.
Solution Approach 2:
The patent changes the parameter of beacon distribution from uniform to optimized non-uniform patterns. By using optimization algorithms that consider multiple parameters (signal coverage, positioning accuracy, beacon density), the system dynamically adjusts beacon placement parameters to achieve optimal performance for each specific building layout and localization task.
2Measurement precision
If more wireless beacons are deployed throughout the building, then the signal coverage and positioning accuracy are improved, but the installation cost increases
Solution Approach 1:
The patent applies partial action by deploying only the necessary number of beacons required for accurate positioning in each specific area, rather than uniformly distributing beacons throughout the entire building. The optimization algorithm determines the minimum sufficient beacon density to achieve desired positioning accuracy, avoiding excessive deployment in areas where fewer beacons are needed.
Solution Approach 2:
The system changes the parameter of beacon quantity from fixed uniform distribution to variable optimized distribution. By analyzing building layout, signal propagation characteristics, and localization requirements, the system dynamically determines the optimal number and placement of beacons to achieve maximum positioning accuracy with minimum infrastructure cost.
3Quantity of substance
If fewer wireless beacons are deployed, then the installation cost is reduced, but the signal coverage and positioning capability become insufficient
Solution Approach 1:
The patent applies local quality by concentrating beacon deployment in specific critical areas where signal coverage is most needed, rather than spreading beacons uniformly throughout the building. The optimization algorithm identifies areas requiring stronger signal coverage and positions beacons strategically in those locations to maximize coverage efficiency with fewer total beacons.
Solution Approach 2:
The system changes the parameter of beacon distribution from uniform to optimized non-uniform patterns that maximize signal coverage efficiency. By considering building geometry, material properties, and signal propagation characteristics, the system determines optimal beacon placement to achieve reliable coverage with minimum infrastructure.
4Quantity of substance
If the number of wireless beacons is optimized, then the installation cost is reduced, but the complexity of determining optimal placement increases
Solution Approach 1:
The patent applies self-service by implementing an automated optimization system that independently determines optimal beacon placement without requiring manual intervention. The system uses algorithms that automatically analyze building data, simulate signal coverage, and compute optimal beacon positions and quantities, reducing the complexity burden on installers and enabling cost-effective optimization.
Solution Approach 2:
The patent replaces manual beacon placement determination with computational optimization algorithms. Instead of requiring physical surveying and manual calculation by installers, the system uses computer-based simulation and optimization to automatically determine optimal beacon positions, substituting mechanical/manual processes with automated computational methods.
Data Source
AI summary
Described herein are technologies relating to assigning wireless beacons to positions within a building, wherein the wireless beacons are included in an indoor positioning system. A computer-implemented floorplan representation is generated, wherein the representation includes a layout of the building and materials of structures of the building. Coverages of wireless beacons are simulated, and multiple objectives are balanced to identify a number of wireless beacons to deploy in the building and positions in the building where the wireless beacons are to be deployed.