A stochastic vehicle mobility model with environmental condition adaptation capability

Abstract

In mobile communication systems, mobility modeling is involved in several aspects such as signaling and traffic load analysis. In particular, accurate mobility models are essential for the evaluation of the system design alternatives and network implementation cost issues. In this paper, we propose a mobility model that is appropriate for practical analysis of the mobile systems' design issues. Our model provides different levels of details for the user mobility behavior and can be adapted to any environmental topology. This model considers direction and velocity of the user mobility. The motion direction is modeled based on real street patterns and is described by discrete state Gauss–Markov technique whose parameters are calculated by combining the information derived by the topological map and wireless network performance report (WNPR) of the simulation area. In the proposed method, the user velocity is assumed to be the average velocity that is found based on traffic condition and type of the street. The computational complexity of our model is to the extent that can be used not only in mobility management test procedures but also in order to calculate some useful parameters of these procedures. In this paper, we apply real data to validate the proposed model by simulation which is used to confirm the accuracy of the proposed model and its analytical analysis. Copyright © 2008 John Wiley & Sons, Ltd.

Mobility Support for Millimeter Wave Communications: Opportunities and Challenges

“…2) Vehicular Mobility Model (VMM): V2X communications have great potential to enable future intelligent applications, such as smart cities and intelligent transport systems, and exploiting vehicle mobility is of great importance in designing efficient V2X protocols and applications [80], [83], [103], [104]. By now, researchers have understood the main features in various V2X scenarios and have built novel VMMs [79], [105], [106]. Based on the characteristics of models and the priorities of different applications, VMMs can be categorized into the following cases.…”

“…However, these models are capable of capturing the stochastic nature of traffic arrivals as well as the complicated movements of vehicles in an ITS [107]. The stochastic vehicle mobility model of [105] considered the direction and velocity of the user mobility and was capable of adapting to the traffic condition and type of the street. The work [108] emulated the network throughput with the Manhattan model, and the study [109] leveraged a stochastic VMM in the dynamic optimization of D2D communications.…”

Communication protocols for vehicular ad hoc networks

“…The most common car mobility models were classified into four categories in Reference 31: stochastic models (vehicles movement follows casual paths in the grid at randomly chosen speed), traffic stream models (traffic seen from a macroscopic point of view and modeled by fluid dynamics equations), car‐following models (the position, speed, and acceleration of vehicles are determined by the state of surrounding vehicles), flows‐interaction models (extending any of the previous models with specific behavior at intersections, e.g., considering the effects of traffic lights and crossing streams). We may add cellular automata models , such as the one recently proposed in Reference 32, or multi‐level models 33, which do not fall in these categories. The impact of the mobility model on connectivity metrics, such as link duration , nodal degree , size of clusters , and clustering coefficient (ratio of the effective number of links in the cluster over the number of links in the corresponding complete graph), was studied in Reference 31 for several models from the four above categories.…”

An Analytical Approach for Mobility Load Balancing in Wireless Networks

“…Prioritization method for combining bandwidth borrowing and reservation with mentoring the rate-adaptiveness of ongoing calls in cell [6] and an efficient channel allocation scheme for mobile cellular networks can be performed [7][8]. Stochastic vehicle mobility with environmental condition adaption capability analysis in [9], and dynamic optimiz-ing the QoS of high speed moving terminals and handoff calls in cellular networks was discussed in [10]. The effect of different mobility patterns on the handoff probability was studied in [11].The purpose of this paper is to present evaluation of handoff probabilities of traffic in next generation of high speed wireless mobile networks.…”