I know the random walk mobility model, but I can not understand what are the differences with respect to gaussian random walk.
In other words, I know how to implement the two-dimensional random walk: in according to the random walk mobility model, each node moves with a speed and direction choosen at random using the following rules:
- the speed $v$ is uniformly chosen in the range $[V_{min}, V_{max}]$.
- the direction $\theta$ is uniformly chosen in the range $[0, 2\pi]$.
- moreover also a time interval $\Delta t$ is uniformly chosen in the range $[T_{min}, T_{max}]$: given the speed and the time interval, the distance of the trip is $d= (v)(\Delta t)$, so it is also possible to calculate the destination $(x, y)$ of the trip.
However, since I do not understand the differences with respect to gaussian random walk, I can not understand how to implement two-dimensional gaussian random walk.