📶 Wi-Fi Network Optimization in a Building

Authors: Lukas249 and Suleyyy.

This project presents a Wi-Fi network optimization study using multi-criteria decision-making (MCDM) and optimization methods.
The goal was to select the best router model and optimally place multiple routers to cover an entire building while respecting installation constraints.
Implemented with Excel and R programming.

📄 Full Report: See the full report

Introduction

The project aims to optimize Wi-Fi coverage in a building by:

1. Selecting the best router model using multi-criteria methods.
2. Placing multiple routers in the building efficiently, maximizing coverage and minimizing cost.

Tools used:

• Microsoft Excel (for data modeling and calculations)
• R (for computations and algorithms)

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Router Model Selection

AHP Method

• The Analytic Hierarchy Process (AHP) was used to evaluate and rank available router models based on multiple criteria (signal strength, cost, reliability, etc.).
• Pairwise comparisons were performed, and weights calculated to select the most suitable router.

TOPSIS Method

• The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) provided a complementary ranking of routers.
• This method identifies the router closest to the ideal solution and farthest from the worst-case scenario.

Sensitivity Analysis

• Sensitivity analysis was conducted to verify the robustness of router selection against changes in criteria weights.

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Optimal Router Placement

After selecting the best router model, we addressed placement in the building to ensure full coverage.

Binary Genetic Algorithm (GA)

• A GA was used to determine optimal locations for routers within a building represented as a coverage matrix.
• Binary representation: 1 = router installed, 0 = empty cell.

Binary Linear Programming

• Alternative optimization using linear programming to minimize the number of routers while maximizing coverage.

Impact of Router Coverage on Installation Costs

• Analysis of how increasing router range affects the total number of routers and installation costs.

Solution with Later Constraints

• Certain matrix cells represented areas where routers cannot be installed.
• Optimization methods were adapted to respect these constraints.

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Conclusion

• Combined MCDM methods (AHP, TOPSIS) with optimization techniques (GA, linear programming) for practical Wi-Fi deployment.
• Achieved efficient router selection and placement while considering costs, coverage, and installation constraints.
• Project demonstrates applied operations research methods in a real-world scenario.