Mobility Models for Wireless Ad Hoc Network Research EECS 600 Advanced Network Research, Spring 2005 Instructor: Shudong Jin March 28, 2005

EECS 600 Advanced Network Research, Spring 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

EECS 600 Advanced Network Research, Spring Relevance of Mobility Models Dynamic topology –How dynamic it is? Packet delivery ratio –Path change causes unreachable destination Mobility may be helpful –How?

EECS 600 Advanced Network Research, Spring 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

EECS 600 Advanced Network Research, Spring 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.

EECS 600 Advanced Network Research, Spring 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.

EECS 600 Advanced Network Research, Spring 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 [0;2π] 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

EECS 600 Advanced Network Research, Spring Random Walk Example

EECS 600 Advanced Network Research, Spring 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?

EECS 600 Advanced Network Research, Spring Random Waypoint Example

EECS 600 Advanced Network Research, Spring 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.

EECS 600 Advanced Network Research, Spring Random Direction Example

EECS 600 Advanced Network Research, Spring 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 t+1, their locations are updated according to the group’s logical center. Once the updated reference points, RP(t+1), are calculated, they are combined with a random motion vector, RM, to represent the random motion of each node about its individual reference point.

EECS 600 Advanced Network Research, Spring RPGM Movements

EECS 600 Advanced Network Research, Spring RPGM Example

EECS 600 Advanced Network Research, Spring 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

EECS 600 Advanced Network Research, Spring Packet Delivery Ratio

EECS 600 Advanced Network Research, Spring End-to-end Delay

EECS 600 Advanced Network Research, Spring Hop Count

EECS 600 Advanced Network Research, Spring Comments from Students