How to implement Manhattan Mobility Model (or custom grid-based mobility) in Mininet-WiFi instead of Random Waypoint?

[Valorant Brimstone]

I would like to replace the Random Way Point Mobility Model with the Manhattan Mobility Model (also called Manhattan Grid model).

The desired behavior is as follows:

• The network consists of horizontal and vertical lines only.
• Stations can move in four directions: North/South (vertical) and East/West (horizontal).
• At each intersection, a station decides the next direction with the following probabilities:
  • Go straight: 0.5
  • Turn right: 0.25
  • Turn left: 0.25

Is there any built-in support for the Manhattan mobility model in the latest Mininet-WiFi?

If not, what is the recommended way to implement this custom mobility model?

• Should I extend the existing mobility framework?
• Is it better to use a custom Python script with node.moveTo() in a loop?
• Are there any open examples or community implementations for grid-based / Manhattan mobility that I can build upon?

Any help, code snippet, or pointer to relevant documentation would be helpful.

[Valorant Brimstone]

[Ramon Fontes]

Wouldn't it make more sense to implement the new model as a new function instead of a direct replacement?

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[Valorant Brimstone]

Can you elaborate it? Will mn-WiFi support such customised mobility pattern of Manhattan Model? Because its a controlled mobility pattern irrespective of random way point Model

[Ramon Fontes]

The Random Waypoint and Manhattan models are independent patterns, and I didn't get your point. Instead of looking for a relationship between them, you should focus on implementing the new Manhattan code and testing it directly in the emulator to see how it performs.

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