1 / 12

Modeling Mobility in Wireless Network Simulations

Explore different mobility models for wireless network simulations, including traces and synthetic models for entities and groups such as humans, animals, and vehicles. Understand the trade-off between accuracy and implementation costs and find the right model for your scenario.

billysims
Download Presentation

Modeling Mobility in Wireless Network Simulations

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Chapter 14 : Modeling Mobility Andreas Berl

  2. Motivation • Wireless network simulations often involve movements of entities • Examples • Users are roaming on WLAN access points that are installed in buildings (infrastructure mode) • Cell phone users are walking a city • Cars use car-to-car communication • Ad hoc networks in emergency situations, e.g., earthquake or fire • Mobility of entities plays an important role in such scenarios • Results may vary significantly if the mobility pattern is changed

  3. Categorization • Traces and synthetic mobility models • Traces: Mobility patterns that are logged from real life situations • Synthetic mobility models: Generated by algorithms that specify virtual behavior of users and predict their movements • Entity and group mobility models • Entity models:Movement of a single individual entity, e.g., a human being or an animal. • Group models:A group of individual entities, which is moving as a whole. • Human, animal, and vehicle mobility models • Human: Human beings in certain scenarios. • Animal: Herds or swarms • Vehicle: Restricted to traffic rules • Normal situation and special situation mobility models • Special: earthquake

  4. Random Walk model • Entities in nature move in unpredictable ways • Entity moves from its current location to a following location by choosing randomly a new direction and speed • Every movement is limited to a constant time interval

  5. Random Waypoint Model • The entity chooses a random destination coordinate • Then it moves from its current location to the destination location • After a pause, the new movement is calculated

  6. Random Direction Model • The entity chooses a random direction and a random speed • Entity moves towards the boundary of the simulation area • After a pause, the new movement is calculated

  7. Gauss-Markov Model • The entity gets initially assigned a speed and a direction • At fixed intervals of time, an update of direction and speed is applied • This model enables movements that are depending on previous movements

  8. Manhattan Model • Entities are bound to streets or highways • Map with streets and crossings is defined • Safety distance between entities • Street changes at crossings happen according to a probability

  9. Column Model • Group mobility model in which each entity follows a reference point • Reference points are arranged in a line • The line is moving • Entities are choosing random point near the reference point to move to • Example: tanks

  10. Persue Model • Group of mobile entities are pursuing single reference entity • The reference entity is using an entity mobility model • Group entities are pursuing the reference entity while adding small deviations • Example: Tourists following guide

  11. Nomadic Community Model • Group of entities are following a single reference point • All entities are sharing the same reference point • Each entity is randomly moving around the reference point • Example: Nomads moving from one place to another

  12. Selection of Models • Trade-off between accuracy and costs of the model • Detailed models fit into real-life scenarios • Realistic models impose high complexity • Implementation efforts • Performance of calculation • Simple models are easy to implement and can be calculated fast • Finding the right model • Review available models of the area • Start with a simple model that fits best • Improve it to the specified scenario • Alternatively: Use the same model as it is used in related research  comparable results

More Related