Take notes after reading several academic papers related to mobility models.

1. Introduction

The following paper presents a number of mobility models used in the simulations of ad hoc networks.

CAMP, Tracy, BOLENG, Jeff, et DAVIES, Vanessa. A survey of mobility models for ad hoc network research. Wireless communications and mobile computing, 2002, vol. 2, no 5, p. 483-502. BibTex

It’s worth mentioning that the traces of almost all mobility models can be easily generated by the tool BonnMotion.

1.1 Category

(1)Traces vs Synthetic

Traces models: those mobility patterns are observed in the real life systems.A lot of real traces can be found in CRAWDAD.

Synthetic models: those models attempt to realistically represent the behaviors of mobile nodes

(2)Entity vs Group

Entity mobility models: the behavior of mobile nodes is completely independent. (I think it is not precise)

Group mobility models: the movements of mobile nodes are (partially)dependent on each other.Multiple mobile nodes (a group, cooperative characteristics) move together.

Here is a diagram to depict its relationships.

Fig 1. The classification of mobility models (source from here)

1.2 Factors

In general, the following factors should be taken into account.

• when movements occur
• how to move, speed, direction,
• what happens after a movement, start another movement or wait for a while
• what happens when reaching the boundary 
• independent of or dependent on each other

2. Entity mobility models

2.1 Random-based models

Random-based mobile nodes move randomly and freely without restrictions. To be more specific, the destination, speed and direction are all chosen randomly and independently of other nodes. The random walk model and the random direction model are variants of the random waypoint model^[1].

2.1.1 Random walk

The random walk mobility model is a memoryless mobility pattern. The current speed and direction of mobile nodes is independent of its past speed and direction.

The key points are as follows:

• Nodes start moving at a speed randomly chosen from the predefined ranges [minSpeed, maxSpeed] and a direction randomly chosen from [0, 2π]
• A node reaches the destination and again starts a new movement without pausing
• A node bounces off the border with an angle determined by the incoming direction(?) once it reaches a simulation boundary
• Each movement occurs in either a constant time interval or a constant distance

Drawbacks

• generate unrealistic movements, such as sudden movements and sharp turns
• the movement pattern is restricted to a small portion of the simulation area if a constant time or distance is short

2.1.2 Random waypoint

Similar to random walk models, but the random waypoint mobility model includes pause times. Here are the key points:

• Nodes start moving at a speed uniformly distributed (is it the same as randomly choosing?) from the predefined ranges [minSpeed, maxSpeed] and a direction randomly chosen from [0, 2π]
• Upon arrival, the mobile node pauses for a specified time period or a range
• A node bounces off the border with an angle determined by the incoming direction once it reaches a simulation boundary
• Each movement occurs in either a constant time interval or a constant distance + pausing time

Drawbacks:

• density waves in the average number of neighbors happens due to mobile nodes clustering in the center of simulation area
• The high variability in average mobile nodes neighbor percentage will produce high variability in performance results unless the simulation results are calculated from long simulation runs

Discussions:

• it is a complex relationship between node speed and pause time
• a scenario with fast mobile nodes and long pause times actually produces a more stable network than a scenario with slower, mobile nodes and shorter pause times.
• with slow speeds and large pause times, the network topology hardly changes

2.1.3 Random direction

The random direction was created to overcome density waves in the average number of neighbors happened in the random waypoint and random walk mobility model.

• mobile nodes choose a random direction and then travels to the border of the simulation area in that direction
• Once the simulation boundary is reached, the node pauses for a specified time, after that, chooses another angular direction from [0, π] and continues the process.

Discussions:

• the average hop count for data packets will be much higher since the mobile nodes travel to and usually pause at the border of the simulation area

2.1.4 Probabilistic Random Walk

Chiang’s mobility model utilizes a probability matrix to determine the position of a particular mobile node in the next time step. Here is an example:

Drawbacks:

• choosing an appropriate probability matrix prove difficult

2.2 Gauss—Markov

The Gauss–Markov model was designed to adapt to different levels of randomness via one tuning parameter.

• At fixed intervals of time, the speed and direction is re-calculated in the following way: (a follows Gaussian distribution)

• mobile nodes does not remain near an edge of the grid for a long period of time by modifying the mean direction variable

Discussions:

• eliminate the sudden stops and sharp turns encountered in random walk model

2.3 City section mobility model

The city section mobility model provides realistic movements for a section of a city since it severely restricts the traveling behavior of mobile nodes.

• all nodes must follow predefined paths and behavior guidelines

3. Group mobility models

stay tuned……….

References:
[1]Wikipedia: Random waypoint model
[2]CAMP, Tracy, BOLENG, Jeff, et DAVIES, Vanessa. A survey of mobility models for ad hoc network research. Wireless communications and mobile computing, 2002, vol. 2, no 5, p. 483-502.
[2]HONG, Xiaoyan, GERLA, Mario, PEI, Guangyu, et al. A group mobility model for ad hoc wireless networks. In : Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems. ACM, 1999. p. 53-60.

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