Voronoi Partition Access Point Placement
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Solution Overview
Problem
Existing wireless location systems face accuracy issues due to the distance from mobile devices to access points, and increasing access points increases costs, with manual and symmetrical placement methods being time-consuming and inflexible.
Innovation Solution
The method involves determining existing access points, computing a Voronoi partition, and automatically placing additional access points at vertices farthest from defining locations or with worst accuracy, allowing for non-symmetrical patterns that adapt to the area's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of access points is increased to improve location accuracy, then the accuracy of determined location improves, but the cost of the system increases
Solution Approach 1:
The patent changes the placement pattern parameter from symmetrical/grid-based to Voronoi-based, which optimizes coverage efficiency. This allows achieving the same location accuracy with fewer access points by placing them at Voronoi vertices that maximize distance from existing points, thereby reducing the quantity of access points needed while maintaining measurement precision
Solution Approach 2:
The system performs preliminary computation of Voronoi partitions and identification of optimal placement vertices before actual access point deployment. This preliminary action enables precise placement that maximizes coverage efficiency, reducing the total number of access points required to achieve target accuracy
2Adaptability or versatility
If access points are placed manually to optimize locations, then placement flexibility improves, but time consumption increases
Solution Approach 1:
The patent replaces manual mechanical placement processes with an automated computational system that calculates Voronoi partitions and identifies optimal vertices algorithmically. This substitution maintains placement flexibility and adaptability to different area characteristics while dramatically reducing time consumption by eliminating manual iteration and decision-making
Solution Approach 2:
The system performs self-service by automatically computing optimal access point locations through Voronoi partition analysis without requiring manual intervention. The algorithm independently identifies vertices farthest from existing points and places access points accordingly, maintaining adaptability while eliminating time-consuming manual processes
3Ease of manufacture
If access points are placed according to a symmetrical pattern to simplify deployment, then ease of deployment improves, but adaptability to area characteristics worsens
Solution Approach 1:
The patent deliberately introduces asymmetry by using Voronoi-based placement instead of symmetrical patterns. The Voronoi vertices are naturally asymmetric and adapt to the specific geometry and characteristics of the deployment area, providing both ease of deployment through algorithmic determination and high adaptability to area characteristics
4Quantity of substance
If the number of access points is reduced to lower cost, then system cost decreases, but location accuracy deteriorates
Solution Approach 1:
The patent changes the placement parameter from uniform/grid-based to Voronoi-optimized, which improves coverage efficiency per access point. This allows reducing the number of access points while maintaining accuracy by placing each point at a Voronoi vertex that maximizes its effective coverage area and minimizes overlap
Data Source
AI summary
Devices, methods, and systems for placement of a wireless network access point are described herein. One method includes determining a location of any existing wireless network access points of a wireless location system in an area, computing a Voronoi partition around the locations of the existing wireless network access points in the area, wherein the Voronoi partition includes a number of vertices defined by the locations of the existing wireless network access points, and determining a location to place an additional wireless network access point in the area based on the Voronoi partition, wherein the location corresponds to the vertex in the Voronoi partition that is farthest from its defining locations and in the area and has a worst location accuracy as compared to a location accuracy threshold.