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Social beings such as humans may act as a carrier of mobile nodes, and therefore mobility pattern of node follows a social-based movement model.
It is specifically designed for the mobile gateway scenario and takes advantage of the predictable mobility pattern of vehicles on highways.
As a result, the mobility pattern of SUs corresponds to that of their users.
Therefore, the mobility pattern of these nodes corresponds to that of their users.
In this paper, we first propose a social-based mobility model to simulate the mobility pattern of SUs.
Obviously, the new mobility model is more coincidental to the mobility pattern of human beings in the real world.
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It cannot adapt to the changes in mobility patterns of mobile clients.
The main advantage of this approach is that it can adapt to the changing mobility patterns of the mobile terminals.
Song et al. [61] studied the mobility patterns of anonymous mobile phone users and concluded that, despite the common perception that our actions are random and unpredictable, human mobility follows surprisingly regular patterns and is 93% predictable.
Here, we combine an analysis of the geographic variation in mobility patterns of 14,816,521 anonymous mobile phone subscribers in Kenya with modeled estimates of travel times to health facilities, generated through standard cost distance-based spatial analysis methods on various scost distance-based
This is a paper reporting an exploratory study in the use of a mobile phone app in tracking the mobility patterns of selected sample of people in Hong Kong.
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