© 2008 frans ekman mobility models for mobile ad hoc network simulations frans ekman...

© 2008 Frans Ekman

Mobility Models for Mobile Ad Hoc Network Simulations Frans Ekman

fekman@netlab.hut.fi

Supervisor: Jörg Ott

Instructor: Jouni Karvo

Mobility Models for Mobile Ad Hoc Network Simulations Thesis is done to the Networking

Laboratory /Helsinki University of Technology

Funded by Nokia Research Centre in the SINDTN projectAcademy of Finland in the DISTANCE project

© 2008 Frans Ekman

© 2008 Frans Ekman

Outline

1. Background

2. Working Day Movement model

3. Validation

4. Conclusions and future work

© 2008 Frans Ekman

Ad hoc networks

Infrastructure-less networks where each node acts as a router

© 2008 Frans Ekman

Delay-Tolerant Networks (DTN)

End-to-end connectivity does not exist Nodes need to store and forward packets

© 2008 Frans Ekman

Delay-Tolerant Networks (2/2)

Development of new protocols requires simulation

Simulation requires modeling of the target environment

User movement must be modeledContacts are transfer opportunities!

© 2008 Frans Ekman

Modeling user movement

Movement traces of real people Obtained from experiments:

Analyzing WLAN access-point dataBluetooth devices registering contacts

Limitations of tracesEnvironment specificLimited to certain areas or certain sets of nodes

© 2008 Frans Ekman

Movement models

A set of rules controlling node movement Configurable and easy to work with Models

Random Walk Random Waypoint (RWP)

Currently most models are too simple Homogeneous movement Some real world characteristics are missing

-> Need for a realistic movement model

© 2008 Frans Ekman

Outline

1. Background

2. Working Day Movement model

3. Validation

4. Conclusions and future work

© 2008 Frans Ekman

Working Day Movement Model

Idea: Combine as many different movement characteristics into one model as possible.

We developed our model as an extension to the ONE (Opportunistic Networking Environment) simulator

We model movement of nodes Staying at home Working at an office Doing some activity with friends in the evening Transportation between activities (bus, car or walking)

Use of real world maps

© 2008 Frans Ekman

How it works? (1)

A submodel for each activityHome activity submodelOffice activity submodelEvening activity submodelTransport submodels

Walking submodel Car submodel Bus submodel

© 2008 Frans Ekman

How it works? (2)

Each node is initially assigned:A home locationAn office locationA favorite meeting spot for evening activity

Fewer offices and meeting spots than nodes A configurable percentage moves by car (the

rest walks and/or uses the bus)

© 2008 Frans Ekman

How it works? (3)

1. Node wakes up in the morning

2. Goes to the office and works for a time defined in settings (default 8h)

3. Goes to its favorite meeting spot and does evening activity

4. Goes back home Nodes use the transportation submodels to travel

between activities

© 2008 Frans Ekman

Home activity submodel

Nodes walk a certain distance away from the road and stays there until wakeup

© 2008 Frans Ekman

Office activity submodel

An office is a square with the side defined in settings

Each node has a desk and moves continuously between its desk and a randomly selected location

Pareto distributed pause times

1

2

3

© 2008 Frans Ekman

Evening activity

A group walk according to the MBM model (Random Walk on the map)

A group is formed of nodes Ending their office activity at the same time Same favorite meeting spot Group sizes defined in settings

Pause at the end Split up and go back home

© 2008 Frans Ekman

Transport submodels

Walking submodelUses Dijkstra’s shortest path algorithmSpeed defined in settings

Car submodelSame as walking except higher speed

© 2008 Frans Ekman

Bus transportation submodel

For nodes not owning a car When a node wants to move from one location to

the other: Compare walk distances Take bus if it results in a shorter path to walk

A node can take the bus by walking to the closest bus stop and enter the bus when it stops.

The node will exit the bus at the bus stop closest to the destination

© 2008 Frans Ekman

The map

Road net For each node group

HomesOfficesMeeting spotsBus route

Possible to limit a node group’s movement to a specific area (district)

© 2008 Frans Ekman

Outline

1. Background

2. Working Day Movement model

3. Validation

4. Conclusions and future work

© 2008 Frans Ekman

Characterization of movement

Contact durationsLimits the amount of data that can be sent

Inter-contact timesTime interval a node pair is not in contactCorresponds to how often nodes have an

opportunity to send data Both measured as distributions (CCDF)

© 2008 Frans Ekman

Experimental setup (1/2)

4 Main districts 3 overlapping districts

e (A and B) f (A and C) g (A and D)

One large district h covering the whole map

gf

e

h

© 2008 Frans Ekman

Experimental setup (2/2)

Simulation time 7 · 105 s 1000 nodes 18 buses 10 nodes moving according to the

Shortest Path Map Based Movement (SPMBM) model. (RWP on a map)Background movement

© 2008 Frans Ekman

Results:

Default settings:

Power-law up to 12h like the iMote Bluetooth trace following with an exponential decay

Inter-contact times distribution

© 2008 Frans Ekman

Results: Contact durations

© 2008 Frans Ekman

Results: Contacts per hour

© 2008 Frans Ekman

Results: Total- vs. unique encounters for each node plotted on a scatter diagram

Working Day Movement RWP

© 2008 Frans Ekman

Results:

Impact of pause times inside office

Possible to vary the power-law exponent to meet a specific environment

© 2008 Frans Ekman

Outline

1. Background

2. Working Day Movement model

3. Validation

4. Conclusions and future work

© 2008 Frans Ekman

Conclusions and future work

Our model captures several real world properties It produces similar Inter-contact times and contact

durations as real world traces Heterogeneity in contact patterns Periodic nature

Ideas for future work More submodels Configuration scripts to deal with complexity Modeling of traffic

© 2008 Frans Ekman

Questions?