SMOOTH: A Simple Way to Model Human Mobility
In Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems (MSWiM '11). ACM, New York, 351-360.
In addition to being realistic, a mobility model should be easy to understand and use. Unfortunately, most of the simple mobility models proposed thus far are not realistic and most of the realistic mobility models proposed thus far are not simple to use. The main contribution of this work is to present SMOOTH, a new mobility model that is realistic (e.g., SMOOTH is based on several known features of hu- man movement) and is simple to use (e.g., SMOOTH does not have any complex input parameters). We first present SMOOTH. We then validate that SMOOTH imitates human movement patterns present in real mobility traces collected from a range of diverse scenarios. In addition, we compare SMOOTH with the other mobility models developed based on these mobility traces. Thus, with SMOOTH, we provide researchers with a tool that allows them to leverage the sta- tistical features present in real human movement in a simple and easy to understand manner.
Extract the "smooth" folder and compile using:
The "smooth-bin" file is an executable that can be used in the following manner:
./smooth-bin 10 20000 20000 100 117 20 1.45 1 14000 1.5 10 40200
For the list of input parameters and their order, see "smooth.c" file.
Please check back for further information on the code.
Details on SMOOTH
SMOOTH is based on the following seven statistical features found in real human walk patterns:
1. feature1:The flights (i.e., a straight-line distance covered between two consecutive locations) distribu- tion of mobile nodes follows a truncated power-law (TPL).
2. feature2:The ICTs (inter-contact times) (i.e., the amount of time between two successive contacts of the same pair of nodes) distribution of mobile nodes fol- lows TPL.
3. feature3:The pause-times (i.e., the amount of time a node pauses at a location) distribution of mobile nodes follows TPL.
4. feature4:The distribution of mobile nodes is non-uniform in the network.
5. feature5:Mobile nodes do not move randomly in the network; instead, their movement patterns can be predicted to some extent due to the regularity present in their movement. (See the next two features for details.)
6. feature6:A mobile node explores a new location with probability (say prob_explore) inversely proportional to the total number of distinct locations it has visited so far.
7. feature7:A mobile nodes visits a previously visited location with probability given by (1-prob_explore).
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