Optimal Network Planner is a project designed to optimize wireless network coverage by strategically placing nodes based on signal strength (RSSI). Using advanced clustering techniques and path planning algorithms like K-Median clustering and the Traveling Salesman Problem (TSP), the tool identifies key locations (primary nodes) that maximize network strength and coverage. Secondary nodes, which are closely related to primary nodes, are also considered to ensure comprehensive coverage across the target area. The project aims to provide a robust solution for network optimization, ensuring the strongest possible signal across the region while minimizing travel paths for maintenance and setup.
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| HeatMap | Scatter Plot |
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| 3D Scatter Plot | Graph plot |
A summary of the key features or functionalities of your project.
Clustering Techniques: Utilizes Manhattan distance-based clustering and K-median clustering to group data points by signal strength. Path Planning Algorithms: Implements the Traveling Salesman Problem (TSP) to find the shortest path covering all primary nodes. Correlation Analysis: Uses a correlation matrix to identify primary and secondary nodes based on RSSI relationships.
An overview of the methods and algorithms used in the project, explained in simple terms.
Data Clustering: Raw data with longitude, latitude, and RSSI values is first clustered into smaller groups using Manhattan clustering. Re-Clustering: These clusters are further refined using K-median clustering to reduce the number of groups and focus on key signal areas. Correlation Matrix: A 20x20 correlation matrix is created to identify the strength of relationships between clusters, helping to distinguish primary nodes from secondary ones. Path Optimization: The TSP algorithm is used to find the shortest path that covers all primary nodes, ensuring optimal network strength.
Explain how the results are visualized for better understanding and analysis.
Graph and Map Visualization: The shortest path and node locations are plotted on a graph and map, respectively, to visualize coverage areas and optimize planning. Heatmaps and Network Diagrams: Additional visualizations, such as heatmaps, show signal strength across different areas to identify potential coverage gaps.
Instructions on how to use the project or implement the algorithms for their use case.
Data Preparation: Input raw data with longitude, latitude, and RSSI values. Run Clustering: Use the provided scripts to perform clustering and re-clustering on the data. Analyze Correlation: Generate the correlation matrix to identify primary and secondary nodes. Find Optimal Path: Apply the TSP algorithm to determine the most efficient path for network coverage. Visualize Results: Use visualization tools to see the network coverage and path optimization on a map.
Wrap up with a summary of the benefits and potential applications of your project.
Benefits: Optimizes network coverage by minimizing signal drop and ensuring robust performance across targeted areas. Applications: Useful for network planners, telecom companies, and anyone looking to optimize wireless network infrastructure.
Make a 20X20 co-relation matrix according to the cluster number for each sub-clusters and the result of co-relation matrix should be calculated according to the avarage RSSI strength of that sub-cluster. Then count the total relation and take the highest as the primary Node and check the relation where the relation is more than 0.3 and store these cluster in to a variable calling secondary Node. after that clear the primary and secondary Nodes and Do repeat the process again untill the co-relation matrix is 0.
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| Co-relation Matrix | After doing 0 |
Now secondary Nodes are comming under primary Nodes and if we travel the primary nodes then we can travel secondary nodes as well as primary nodes.
Here is the primary Node and Secondary Node Lists:
And This is the poltting of the primary nodes and if you take this areas then we can cover all the area with a lower cost. There are only 744 points by which we can travel all the areas.
Open your 
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pip install pandas -
pip install numpy -
pip install seaborn -
pip install matplotlib -
pip install mpl_toolkits -
pip install seaborn -
pip install --upgrade pip -
pip install networkx -
pip install geodesic -
pip install pathlib -
pip install sklearn
Once Installed now we can import it inside our python code.
You can 
Go here if you aren't here already and click ➞ **✰ Star and ⵖ Fork button in the top right corner. You'll be asked to create a GitHub account if you don't already have one.
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Go here and click the big green ➞ **Code button in the top right of the page, then click ➞ Download ZIP.
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Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
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Launch ipython notebook from the folder which contains the notebooks. Open each one of them
Kernel > Restart & Clear Output
This will clear all the outputs and now you can understand each statement and learn interactively.
If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.
I'm Debasis Mishra and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏
See github's contributors page for details.
If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.
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MIT License
Copyright (c) 2024 DEBASIS MISHRA
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