WSN03-4: A Novel Semi-Markov Smooth Mobility Model for Mobile Ad Hoc Networks.

Existing random mobility models have their limitations such as speed decay and sharp turn which have been demonstrated by the previous studies. More importantly, mobility models need to mimic the movements that abide by the physical law for accurate analysis and simulations of mobile networks. Therefore, in this paper, we propose a novel mobility model, semi-Markov smooth (SMS) model. Each SMS movement includes three consecutive phases: speed up phase, middle smooth phase, and slow down phase. Thus, the entire motion in the SMS model is smooth and consistent with the moving behaviors in real environment. Through steady state analysis, we demonstrate that SMS model has no average speed decay problem and always maintains a uniform spatial node distribution. The analytical results are validated by extensive simulation experiments. In addition, we compare the simulation results on link lifetime and percentage of node degree with random waypoint model, Gauss-Markov model and the proposed SMS model.

[1]  Mingyan Liu,et al.  A general framework to construct stationary mobility models for the simulation of mobile networks , 2006, IEEE Transactions on Mobile Computing.

[2]  Paolo Santi,et al.  The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks , 2003, IEEE Trans. Mob. Comput..

[3]  Mingyan Liu,et al.  Sound mobility models , 2003, MobiCom '03.

[4]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[5]  Jeffrey L. Arthur Stochastic Models in Operations Research, Volume I. (Daniel P. Heyman and Matthew J. Sobel) , 1983 .

[6]  J. Broch,et al.  Dynamic source routing in ad hoc wireless networks , 1998 .

[7]  Christian Bettstetter,et al.  Smooth is better than sharp: a random mobility model for simulation of wireless networks , 2001, MSWIM '01.

[8]  Mingyan Liu,et al.  Random waypoint considered harmful , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[9]  Zygmunt J. Haas,et al.  Predictive distance-based mobility management for PCS networks , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[10]  Daniel P. Heyman,et al.  Stochastic models in operations research , 1982 .

[11]  Zhen Liu,et al.  Capacity, delay and mobility in wireless ad-hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[12]  Louise E. Moser,et al.  An analysis of the optimum node density for ad hoc mobile networks , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[13]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[14]  Donald F. Towsley,et al.  Properties of random direction models , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[15]  Stephen B. Wicker,et al.  On the behavior of communication links of a node in a multi-hop mobile environment , 2004, MobiHoc '04.