Abstract
Understanding node mobility is critical for the proper simulation of mobile devices in a wireless network. However, current mobility models often do not reflect the realistic movements of users within their environments. They also do not provide the freedom to adjust their degrees of randomness or adequately mimic human movements by injecting possible crossing points and adding recurrent patterns. In this paper, we propose the recurrent self-similar Gauss–Markov mobility (RSSGM) model, a novel mobility model that is suitable for applications in which nodes exhibit recurrent visits to selected locations with semi-similar routes. Examples of such applications include daily human routines, airplane and public transportation routes, and intra-campus student walks. First, we present the proposed algorithm and its assumptions, and then we study its behavior in different scenarios. The study’s results show that different and more realistic mobility traces can be achieved without the need for complex computational models or existing GPS records. Our model can flexibly adjust its behavior to fit any application by carefully tuning and choosing the right values for its parameters.
Highlights
Introduction and MotivationRecently, wireless networks have been widely deployed, as they offer high-speed data rates and have become more reliable and resilient against noise and interference
The random direction (RD) mobility model is almost identical to the random waypoint approach, as mobile users in both models travel in piece-wise linear segments
We define a new metric called spatial and temporal standard deviation (STSD), which measures the level of change in mobility with different random seeds
Summary
Wireless networks have been widely deployed, as they offer high-speed data rates and have become more reliable and resilient against noise and interference. Several studies have been conducted to offer appropriate environments to facilitate the simulation and evaluation of novel and enhanced network systems Some of these studies have focused on modeling the mobility of users within the wireless network to predict their movements [2]. The GM model yields better performance results than other random mobility models, but it is still far from providing realistic patterns This has caused many researchers to apply complex, application-driven mobility models that are based on the GPS traces of human walks or cellphone location tracking [10,11,12]. It is challenging to predict user movement patterns in a wireless network [13], yet mobile users, despite their random behaviors, sometimes pass particular points due to predetermined and realistic constraints.
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