RSSGM: Recurrent Self-Similar Gauss–Markov Mobility Model

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.

Performance Analysis of Random Access Mechanism in 5G Millimeter Wave Networks: Effect of Blockage, Shadowing and Mobility

“…Different mobility models such as the Manhattan model, Freeway model, Random Waypoint model have been studied in [21]. A recurrent Gaussian mobility model has been proposed in [22]. The model in [22] accurately represents the movement of a UE in a restricted area.…”

“…A recurrent Gaussian mobility model has been proposed in [22]. The model in [22] accurately represents the movement of a UE in a restricted area. This model has both temporal and spatial dependence, which is closer to the realistic movement of a UE.…”

“…From (19), we calculate the average interference caused by any random UE in the cell by averaging it over the parameters r, φ 1 and φ 2 in equations ( 20), ( 21) and (22), as shown at the bottom of the page.…”

“…We model the movement of UE in a restricted area with buildings as obstacles. Different mobility models have been considered for various scenarios such as Urban Macro (UMa), Urban Micro (UMi), Rural and indoor cases in [22]. We consider that the obstacles in the cell are stationary and the UEs move according to Gauss-Markov mobility model in the cell as explained below.…”

The “ReadyPark” Collaborative Parking Search Strategy

“…The Manhattan model is commonly used to simulate both the street network of a city and the movements of drivers on this network. We chose this model because it refers to the spatial dependency of a real city, it is able to capture many mobility scenarios and it is easily extensible and adaptable to a particular issue [27][28][29].…”