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Surveillance coverage of mobile sensor networks under Brownian motion random node mobility model was addressed in [16].
The Gauss-Markov mobility model was designed to adapt to different levels of randomness via tuning parameters.
The mobility model was quantified with our device measurements for the best accuracy, and the characteristic fluctuation was validated with the experimentally measured DC baseband data from 15/20-nm 15/20-nm FinFET deviCMOSin our eandier work [24].
VANET connectivity analysis based on a comprehensive mobility model was presented in [16] by considering the arrival and departure of nodes at predefined entry and exit points along a highway.
The Random Waypoint (RWP) [3] mobility model was used to evaluate the performance of Bypass-AODV, which has shown a clear performance gain over the conventional AODV [9], but RWP does not reflect the mobile nodes' movement patterns in real-life applications.
Secondly, a gravity-type mobility model was adopted to identify commuting workers.
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The random direction mobility model is commonly adopted in the mobile sensor and ad hoc networks for simulation.
The fact that we consider VANETs and not mobile networks in general has a strong impact on the resulting mobility model being quite specific in highway scenarios.
A mobility model is proposed and performance of the positioning system in a mobile environment is assessed.
In section 3, network model and mobility model are presented.
A random waypoint mobility model is implemented for indoor users.
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