Mobility Models for Wireless Ad Hoc Network Research

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Mobility Models for Wireless Ad Hoc Network
Research
EECS 600 Advanced Network Research, Spring 2005
Instructor Shudong Jin March 28, 2005
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Realistic Conditions for Testing Protocols

• Transmission range, link bandwidth, symmetry
• Buffer space for the storage of messages
• Data traffic models, and
• Realistic movements of the mobile users
• Mobility model

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Relevance of Mobility Models

• Dynamic topology
• How dynamic it is?
• Packet delivery ratio
• Path change causes unreachable destination
• Mobility may be helpful
• How?

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Traces versus Synthetic Models

• Traces are those mobility patterns that are
  observed in real life systems. Traces provide
  accurate information, especially when they
  involve a large number of participants and an
  appropriately long observation period.
• Not easy to model the networks if traces have not
  yet been created.
• Limited traces, fixed parameters, not scalable
  simulations
• Synthetic models attempt to realistically
  represent the behaviors of mobile nodes without
  the use of traces
• Less realistic
• Need to understand the models well before using
  them

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Seven Synthetic Entity Mobility Models

• Random Walk Mobility Model (including its many
  derivatives) picks random directions and speeds.
• Random Waypoint Mobility Model includes pause
  times between changes in destination and speed.
• Random Direction Mobility Model forces nodes to
  travel to the edge of the simulation area before
  changing direction and speed.
• A Boundless Simulation Area Mobility Model
  converts a 2D rectangular simulation area into a
  torus-shaped simulation area.
• Gauss-Markov Mobility Model uses one tuning
  parameter to vary the degree of randomness in the
  mobility pattern.
• A Probabilistic Version of the Random Walk
  Mobility Model utilizes a set of probabilities
  to determine the next position of a node.
• City Section Mobility Model represents streets
  within a city.

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Five Synthetic Group Mobility Models

• Exponential Correlated Random Mobility Model
  uses a motion function to create movements.
• Column Mobility Model the set of nodes form a
  line and are uniformly moving forward in a
  particular direction.
• Nomadic Community Mobility Model a set of nodes
  move together from one location to another.
• Pursue Mobility Model a set of nodes follow a
  given target.
• Reference Point Group Mobility Model group
  movements are based upon the path traveled by a
  logical center.

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Entity model Random Walk

• A mobile node moves from its current location to
  a new location by randomly choosing a direction
  and speed in which to travel.
• The new speed and direction are both chosen from
  pre-defined ranges, speedmin speedmax and
  02p respectively.
• 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 a move, a new direction and speed
  are calculated.
• If an MN which moves according to this model
  reaches a simulation boundary, it bounces off
  the simulation border with an angle determined by
  the incoming direction.
• Random Walk is a memory-less mobility pattern.
  This characteristic can generate unrealistic
  movements such as sudden stops and sharp turns

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Random Walk Example
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Entity model Random Waypoint

• The Random Waypoint Mobility Model includes pause
  times between changes in direction and/or speed.
• A mobile node stays in one location for a certain
  period of time (i.e., a pause time).
• Once this time expires, the node chooses a random
  destination in the simulation area and a speed
  that is uniformly distributed between
  minspeed,maxspeed. The node then travels toward
  the newly chosen destination at the selected
  speed.
• Repeat above two steps
• Often in the model, the nodes are initially
  distributed randomly around the simulation area.
  This initial random distribution of MNs is not
  representative of the manner in which nodes
  distribute themselves when moving.
• Q How do you understand this?
• Q How to overcome this initialization problem?

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Random Waypoint Example
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Entity model Random Direction

• A mobile node chooses a random direction in which
  to travel similar to the Random Walk Mobility
  Model. The node 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, chooses another angular
  direction (between 0 and 180 degrees) and
  continues the process.

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Random Direction Example
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Group model RPGM

• The Reference Point Group Mobility (RPGM) model
  represents the random motion of a group of nodes
  as well as the random motion of each individual
  MN within the group.
• Group movements are based upon the path traveled
  by a logical center for the group. The logical
  center for the group is used to calculate group
  motion via a group motion vector, GM. The motion
  of the group center completely characterizes the
  movement of its corresponding group of nodes
  (including their direction and speed).
• Individual nodes randomly move about their own
  pre-defined reference points, whose movements
  depend on the group movement. As the individual
  reference points move from time t to t1, their
  locations are updated according to the groups
  logical center. Once the updated reference
  points, RP(t1), are calculated, they are
  combined with a random motion vector, RM, to
  represent the random motion of each node about
  its individual reference point.

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RPGM Movements
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RPGM Example
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Importance of Mobility Models

• Objectives
• Simulations to illustrate the important of models
• Simulated models
• Random walk, random waypoint, random direction,
  RPGM (with inter-group communications, and with
  inter-group/intra-group communication)
• Simulation setup
• The ns-2 simulator DSR
• Long simulation
• 20 CBR flows (constant packet rate, 64B/s)
• packet delivery ratio, end-to-end delay,
  hop-count, etc
• Mean 95 confidence interval

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Packet Delivery Ratio
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End-to-end Delay
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Hop Count
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Comments from Students