Mobility Models for
Mobile Ad Hoc
Network Simulations
Frans Ekman
fekman@netlab.hut.fi
Supervisor:
Instructor:
© 2008 Frans Ekman
Jörg Ott
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 project
 Academy of Finland in the DISTANCE project
© 2008 Frans Ekman
Outline
1.
2.
3.
4.
Background
Working Day Movement model
Validation
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 modeled

 Contacts
are transfer opportunities!
© 2008 Frans Ekman
Modeling user movement
Movement traces of real people
 Obtained from experiments:

 Analyzing
WLAN access-point data
 Bluetooth devices registering contacts

Limitations of traces
 Environment
specific
 Limited 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.
2.
3.
4.
Background
Working Day Movement model
Validation
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 activity
 Home
activity submodel
 Office activity submodel
 Evening activity submodel
 Transport submodels
Walking submodel
 Car submodel
 Bus submodel

© 2008 Frans Ekman
How it works? (2)

Each node is initially assigned:
A
home location
 An office location
 A 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.
2.
3.
4.

Node wakes up in the morning
Goes to the office and works for a time defined in
settings (default 8h)
Goes to its favorite meeting spot and does evening
activity
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
3
1
© 2008 Frans Ekman
2
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 submodel
 Uses
Dijkstra’s shortest path algorithm
 Speed defined in settings

Car submodel
 Same
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

 Homes
 Offices
 Meeting
spots
 Bus route

Possible to limit a node group’s movement
to a specific area (district)
© 2008 Frans Ekman
Outline
1.
2.
3.
4.
Background
Working Day Movement model
Validation
Conclusions and future work
© 2008 Frans Ekman
Characterization of movement

Contact durations
 Limits

the amount of data that can be sent
Inter-contact times
 Time
interval a node pair is not in contact
 Corresponds to how often nodes have an
opportunity to send data

Both measured as distributions (CCDF)
© 2008 Frans Ekman
Experimental setup (1/2)
g
f


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
© 2008 Frans Ekman
e
h
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: Inter-contact times distribution

Default settings:
Power-law up to
12h like the
iMote Bluetooth
trace following
with an
exponential
decay
© 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.
2.
3.
4.
Background
Working Day Movement model
Validation
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?
© 2008 Frans Ekman