Wireless Networks and Mobility Model

January 21, 2018 Author: virendra
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In recent years, the deployment and importance of wireless networks has grown rapidly. Mobility plays a key role in this regard, and has driven the development of many new services, such as VoIP and interactive multimedia. Simulations of wireless networks employ several components critical to the accuracy of the simulations, one of the most important being the choice of mobility model, which creates the movement patterns of mobile nodes that forms the varying topology of the network. A typical mobility model first places the mobile nodes in their initial locations and defines the way that the nodes move within the network. Indeed, the mobility model emulates the real user movement through the inclusion of the critical movement factors such as direction, speed, destination, and the movement histories of the same, or similar, users.

Overview of Mobility Model

The mobility patterns are the key criteria that influence the performance characteristics of the mobile ad hoc networks. The mobility model is designed to describe the movement pattern of mobile users, and how their location, velocity and acceleration change over time. Since mobility patterns may play a significant role in determining the protocol performance, it is desirable for mobility models to emulate the movement pattern of targeted real life applications in a reasonable way. A mobility model attempts to mimic the movement of real mobile nodes that change the speed and direction with time. The mobility model that accurately represents the characteristics of the mobile nodes in an ad hoc network is the key to examine whether a given protocol is useful in a particular type of mobile scenario.

The most important characteristic of a mobility model is the degree of realism with respect to the movement of users in real life. More realistic models enable more accurate simulation and evaluation of network parameters. In addition, because there exists no single, comprehensive, mobility model the incorrect selection of an inappropriate model, not mimicking the movement patterns expected in the real life environment under consideration, leads to incorrect observations and results.

Types of Mobility Model

The movement of mobile users is represented by mobility models. In mobility modeling activity of user’s movement can be described using analytical and simulation models. Analytical models may provide performance parameters and Simulation models can derive valuable solutions for more complex cases. Typical mobility model includes-


The random waypoint mobility model introduces specific pause times between movements’ i.e. changes in direction and speed. The random waypoint model is the most popular mobility model employed in contemporary research, and can be considered a foundation for building other mobility models. An MOBILE NODE begins by staying in one location for a certain period of time (i.e., a pause time). Once this time expires, the MOBILE NODE chooses a random destination as well as a speed that is uniformly distributed between [0, MAXSPEED]. It then travels towards the newly chosen destination at the selected speed. Upon arrival, the MOBILE NODE takes another break before starting the process again.

Following Figure shows the travelling pattern of an MOBILE NODE using the Random Waypoint Mobility Model.

Traveling pattern of an MOBILE NODE using the 2-D Random Way Point Mobility Model

Figure 1: Traveling pattern of an MOBILE NODE using the 2-D Random Way Point Mobility Model


  • Lack of regular movement modeling
  • Exhibits speed decay
  • Exhibits density wave
  • Memory-less movement behaviors (a common problem for all random waypoint variations).


The random walk mobility model is the simplest mobility model, generating completely random movement patterns. It was designed for simulations in which the movement patterns of mobile nodes are completely unpredictable. Since many entities in nature move in extremely unpredictable ways, the Random Walk Mobility Model was developed to mimic this erratic movement In this mobility model, an MOBILE NODE moves from its current location to a new location by randomly choosing a direction and speed in which to travel In this model a mobile node is initially placed in a random location in the simulation area, and then moved in a randomly chosen direction between at a random speed between.

Traveling pattern of an MOBILE NODE using the 2-D Random Walk Mobility Model

Figure 2: Traveling pattern of an MOBILE NODE using the 2-D Random Walk Mobility Model

Each movement in the Random Walk Mobility Model occurs in either a constant time interval t or a constant distance traveled d, at the end of which a new direction and speed are calculated and this process is repeated a predetermined number of times. Figure 2 shows the result of a single node executing the random walk mobility model with a constant travel time. Many derivatives of the Random Walk Mobility Model have been developed including the 1-D, 2-D, 3-D, and d-D walks. In 1921, Polya proved that a random walk on a one or two-dimensional surface returns to the origin with complete certainty, i.e., a probability of 1.0. This characteristic ensures that the random walk represents a mobility model that tests the movements of entities around their starting points, without worry of the entities wandering away never to return.


  • Unrealistic movement patterns
  • Sharp and sudden turns
  • Wrapping not observed in real applications


The Random Direction Mobility Model (Royer et al., Submitted) was created in order to overcome a flaw discovered in the Random Waypoint Mobility Model. MOBILE NODEs using the Random Waypoint Mobility Model often choose new destinations, and the probability of choosing a new destination that is located in the center of the simulation area, or requires travel through the middle of the simulation area, is high. The MOBILE NODEs moving with the Random Waypoint Mobility Model appear to converge, disperse, converge again, etc. In order to alleviate this type of behavior and promote a semi-constant number of neighbors, the Random Direction Mobility Model was developed.

Traveling pattern of an MOBILE NODE using the Random Direction Mobility Model

Figure 3: Traveling pattern of an MOBILE NODE using the Random Direction Mobility Model

In this model, MOBILE NODEs choose a random direction in which to travel instead of a random destination. After choosing a random direction, an MOBILE NODE travels to the border of the simulation area in that direction. As soon as the boundary is reached the MOBILE NODE stops for a certain period of time, chooses another angular direction (between 0 and 180 degrees) and continues the process. Following figure shows an example path of an MOBILE NODE, which begins at the center of the simulation area using the Random Direction Mobility Model.

A slight modification to the Random Direction Mobility Model is the Modified Random Direction Mobility Model. In this modified version, MOBILE NODEs continue to choose random directions but they are no longer forced to travel to the simulation boundary before stopping to change direction. Instead, an MOBILE NODE chooses a random direction and selects a destination anywhere along that direction of travel.


  • Unrealistic movement pattern
  • Average distances between mobile nodes are much higher than other models, leading to incorrect results for routing protocols evaluation


[1] “Chapter 2: Mobility Model Characteristics”, available online at: https://www.springer.com/cda/content/document/cda_downloaddocument/9781441960481-c1.pdf?SGWID=0-0-45-1009743-p173970267.

[2] Bai, Fan, and Ahmed Helmy, “A survey of mobility models”, Wireless Ad-hoc Networks. University of Southern California, USA 206 (2004): 147.

[3] Mihail L Sichitiu, “Mobility models for ad hoc networks”, In Guide to Wireless Ad Hoc Networks, pp. 237-254, Springer London, 2009.

[4] Tracy Camp, Jeff Boleng and Vanessa Davies, “A survey of mobility models for ad hoc network research”, Wireless Communications and Mobile Computing, Wiley Online Library, Volume 2, Issue 5, 11 SEP 2002

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