Optical Fiber Deployment Clustering for Cost-Efficient Building Rollouts
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Solution Overview
Problem
Deployment of optical fiber to buildings without infrastructure is inefficient and costly due to methods that do not optimize construction and logistics.
Innovation Solution
A method involving geographic information systems and resolution reduction algorithms to identify target buildings, form clusters, and iteratively determine optimal building clusters based on cost-benefit analysis for efficient fiber deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optical fiber deployment is performed to all buildings individually without optimization, then complete coverage is achieved, but deployment cost and time increase significantly
Solution Approach 1:
The patent groups multiple buildings into clusters based on their geographic proximity and deployment characteristics. By treating clusters as unified deployment units rather than individual buildings, the system reduces redundant operations and optimizes resource allocation, thereby improving deployment efficiency and reducing overall deployment time while maintaining comprehensive coverage.
Solution Approach 2:
The patent segments the overall deployment task into smaller, manageable building clusters. This segmentation allows for parallel processing of different clusters, prioritization of high-value clusters, and more efficient resource allocation. The iterative removal and re-clustering process further refines this segmentation to optimize deployment pathways and reduce total deployment time.
2Productivity
If optical fiber deployment is performed to all buildings without optimization, then complete coverage is achieved, but deployment cost increases
Solution Approach 1:
The patent dynamically changes deployment parameters including cluster formation criteria, prioritization weights, and cost-benefit thresholds. By adjusting these parameters based on service provider goals and market conditions, the system optimizes the balance between deployment efficiency and cost, identifying the most economically viable buildings and clusters for fiber installation while reducing overall deployment expenditure.
Solution Approach 2:
The patent applies different deployment strategies and cost-benefit criteria to different building clusters based on their specific characteristics such as location, building type, and potential revenue. This localized approach allows optimization of resource allocation to high-value areas while reducing or eliminating deployment in low-priority areas, thereby reducing total deployment cost while maintaining efficiency in critical regions.
3Measurement precision
If building clusters are formed using detailed location coordinates, then accurate grouping is achieved, but computational complexity increases
Solution Approach 1:
The patent applies resolution reduction by truncating location coordinates to a defined precision level (e.g., reducing from full decimal precision to 2 decimal places). This partial action maintains sufficient accuracy for geographic clustering while dramatically reducing the computational complexity of distance calculations and cluster formation algorithms, enabling efficient processing of large building datasets.
4Productivity
If iterative optimization is performed on all building clusters, then optimal deployment paths are identified, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-identifying and removing already-served buildings and their neighboring buildings from the optimization process. This preliminary filtering reduces the size of the optimization problem in subsequent iterations, allowing the system to maintain high optimization quality for remaining buildings while significantly reducing overall processing time through progressive problem reduction.
Data Source
AI summary
Methods and systems for optimizing optical fiber deployment are described herein. A method includes identifying, using a selection model, target buildings for deployment of optical fiber, determining neighboring buildings for each target building in a defined range using location coordinates resolution reduction, grouping each target building and associated neighboring buildings into building clusters, generating a polygon for each building cluster which connects an associated target building and associated neighboring buildings, determining whether a building cluster is optimal based on total cost for an associated polygon and a cost benefit model, iteratively performing the grouping, the generating, and a second instance of the determining after removal of one or more target buildings and associated neighboring buildings for one or more optimal building clusters and until no target buildings remain, and deploying optical fiber based on the for one or more optimal building clusters.