NETW 707
1 / 21

NETW 707 Modeling and Simulation Amr El Mougy Maggie Mashaly - PowerPoint PPT Presentation

  • Uploaded on

NETW 707 Modeling and Simulation Amr El Mougy Maggie Mashaly. Lecture (7) Mobility Modeling. Mobility in Wireless Networks. In mobile networks, users are free to move around Mobility models are used to describe these movement patterns

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'NETW 707 Modeling and Simulation Amr El Mougy Maggie Mashaly' - ollie

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

NETW 707




Amr El Mougy

Maggie Mashaly


Mobility Modeling

Mobility in wireless networks
Mobility in Wireless Networks

  • In mobile networks, users are free to move around

  • Mobility models are used to describe these movement patterns

  • Movement patterns depend on the type of network

  • Trace-based mobility models are always an accurate way for producing movement patterns. However, they are not always available

Random models
Random Models

  • Nodes move freely

  • No restrictions in speed, direction, or destination. No correlation with other nodes

  • Models are simple but may not be realistic

Random waypoint model
Random Waypoint Model

  • Benchmark mobility model

  • Simple implementation:

  • Each node randomly selects a location in the field as its destination

  • The node travels to its destination at a speed chosen uniformly from 0,

  • Each node choses its velocity and direction independently of other nodes

  • Upon reaching the destination, each node pauses for a random pause time

  • The process is repeated

  • determine the dynamicity of the topology

Random walk model
Random Walk Model

  • Motivated by the observation that some nodes often move in an unexpected way

  • As with random waypoint, movement is totally random

  • The pause time is equal to zero, i.e.

  • Time intervals are defined, and nodes change their speed and direction at every interval

  • Every interval, nodes choose a new direction from (0, 2π] and a new speed from 0,

  • Uniform or Gaussian distributions are typically used

  • When nodes reach the border, they bounce back with the same or opposite angle

  • Changes in every time interval are memoryless

Random direction model
Random Direction Model

  • A problem with the random waypoint and random walk models is that they result in non-uniform node distribution

  • Nodes tend to converge towards the center, diverge away, then converge again, creating density fluctuations

  • Random direction chooses a direction so that the node will reach the boundary. Then another direction is chosen towards another boundary and the process is repeated

  • Less fluctuations in node density

Limitations of random models
Limitations of Random Models

  • Lack of temporal dependence of velocity: sudden stops or movements, increases in speed, are not captured

  • Lack of spatial dependence of velocity: nodes move independently of other nodes. Not true for example in battlefields

  • Lack of geographic restrictions on movement: obstacles, streets, freeways are not represented

Mobility models with temporal dependency
Mobility Models with Temporal Dependency

  • In reality, mobility may be constrained by physical laws of acceleration, velocity, and rate of change in direction

  • Thus, current mobility patterns of a node may depend on past patterns

  • Random models are memoryless and cannot capture such dependency

Gauss markov mobility model
Gauss-Markov Mobility Model

  • Mobility has a “memory” that is captured by a Gauss-Markov Process

  • Velocity is defined over the x and ydirections as:

  • Velocity at time t is given by , and at t-1 it is ,

  • αis called the memory level, is the mean and is the variance

  • is a Gaussian process with mean 0 and variance

Gauss markov mobility model1
Gauss-Markov Mobility Model

  • When α= 0, the model reduces to random walk, i.e. no memory

  • When α= 1, the current velocity is the same as the previous one

  • When α is between 0 and 1, the current velocity partially depend on the previous velocity and partially on the random Gaussian value

Smooth random mobility
Smooth Random Mobility

  • Suggests changing speed and direction incrementally and gradually

  • Nodes often move in preferred speeds rather than uniform distribution over

  • Speeds within the preferred set have high probability, while the rest are uniformly distributed

  • For example, for the preferred set

Smooth random mobility1
Smooth Random Mobility

  • Frequency of speed change follows a Poisson process

  • Upon a speed change event, a new speed is chosen according to the aforementioned probability distribution

  • The speed is changed to the new one smoothly using uniformly distributed variables from [0, amax], [amin,0] according to:

  • Thus, the new speed is calculated as

Smooth random mobility2
Smooth Random Mobility

  • If a(t) is small then change in speed is gradual and temporal correlation is strong

  • Change in direction is assumed to be uniform over [0, 2π]

  • The frequency of direction change follows an exponential distribution

  • The difference between the old direction and the new one is given by

  • If the change in direction is too large, it is divided into small slots

Mobility models with spatial dependency
Mobility Models with Spatial Dependency

  • In certain situations, the velocities of nodes are correlated in space

  • Ex: speed of a vehicle is bounded by the vehicle ahead of it, Soldiers on a battlefield move in units

  • Random and temporal models do not capture this effect

Reference point group mobility model
Reference Point Group Mobility Model

  • Each group is composed of a leader and members. The mobility of the group is defined by its leader

  • The motion of the group leader, and thus the motion trend of the group is defined by the vector

  • The motion of each member deviates from by some degree

  • The final motion vector of member i is deviated from using

  • +

  • has length uniformly distributed in [0, and angle uniformly distributed in [0, 2π]

Pursue mobility model
Pursue Mobility Model

  • Models situations where several nodes attempt to capture a single node

  • The target node (being pursued) moves using the random waypoint model

  • The remaining nodes move using

  • May be generated by the reference point group mobility model

Mobility models with geographical restrictions
Mobility Models with Geographical Restrictions

  • Mobility of nodes is bounded by the environment

  • Used to model movements along freeways, streets, around obstacles, etc.

  • Nodes move in pseudo-random or predefined pathways

Pathway mobility model
Pathway Mobility Model

  • A predefined map is first created either randomly or based on a real city

  • The map is a graph where vertices represent buildings and edges represent streets between buildings

  • The movement of nodes resemble the random waypoint model, but bounded by the map

  • Nodes choose a destination, travel using a constant speed, pause for and repeat the process

Obstacle mobility model
Obstacle Mobility Model

  • Obstacles are modeled as rectangular objects randomly placed in a field

  • Nodes must change their trajectory upon reaching an obstacle

  • Wireless signals are assumed to be blocked by the obstacles

  • Could be used to model conferences, disaster relief or event coverage scenarios