Institut Eurécom † Department of Mobile Communications Sophia Antipolis, France

Neighborhood Changing Rate:

A Unifying Parameter to Characterize and Evaluate

Data Dissemination Scenarios

Institut Eurécom†

Department of Mobile Communications

Sophia Antipolis, France

Jérôme Härri†, Biao Zhou‡, Mario Gerla‡, Fethi Filali†, Christian Bonnet†

{haerri,filali,bonnet}@eurecom.fr{zhb,gerla}@cs.ucla.edu

University of California‡ Department of Computer

Science Los Angeles, USA

4th IEEE/IFIP Wireless On demand Network Systems and Services (WONS) Obergurgl, AustriaJanuary 24th 2007

mailto:[email protected]

mailto:[email protected]

2Härri et al. Neighborhood Changing Rate (NCR)

Agenda

• Data Dissemination in Mobile Ad Hoc Network

• NCR– Definition– Example– Justification

• The Mobeyes Protocol

• Performance Results

• Conclusion


Data Dissemination

t-3

t-2 t-1t

t-3

t-2t

t-1t

C2

C2C1

C3

C3

C1

• A single car has a packet to spread

• A car shares its packet with all vehicles reachable within its transmission range.

• Objective: Disseminating the packet throughout the network

• Example:

Spreading factor: 2


Data Dissemination

• At each encounter, the more vehicles the car meets, the more efficient is the spreading factor.

• Example:

Spreading factor: 5

t-3

C1

t-3

C3

t-3

C2

t-3

C4t-2

t

C4

t

C3

t-1

C1

C5

t-2

C6

C6

C6

t

t

C1

C5

t

C5

t-2

C1 C4

C3C2


Data Dissemination

• In order to reduce the broadcast storm effect, no relaying.

• Each car that receiving the set of data may in turn share it with any encountered vehicle.

• Best dissemination Strategy: At each encounter point, a single car with data shares it with a large set of vehicles.

• Group mobility does not help data dissemination, as in that case, a large set of cars containing data shares it with a potentially smaller set of vehicles.

Data Dissemination Efficiency : Time needed to spread a given set of data to the entire network.


Data Dissemination

• The data dissemination efficiency in therefore dependant to a large set of parameters:– The rate a car encounters other neighbors.– The number of vehicles met that do not follow a similar trajectory.– …

Objective: Define a single universal metric including all these parameters

In other terms, data dissemination efficiency may depend on a

Neighborhood Changing Rate


Agenda





• Conclusion


Neighborhood Changing Rate (NCR)

• Let’s define– : Sampling interval equal to the time needed for

a node to move a distance equal to its transmission range

– : Expected Neighbor entering node i’s neighborhood during the time interval

– : Expected Neighbor leaving node i’s neighborhood during the time interval

– : Node i’s nodal degree at time t.

• Then,

[ ] [ ][ ] [ ])(#)(

)(#)(#)(

tNbEtDegE

tNbEtNbEttNCR

inew

i

inew

ileavei

Δ+

Δ+Δ=Δ+

[ ])(# tNbE inew Δ

[ ])(# tNbE ileave Δ

tΔ

tΔ

tΔ

)(tDeg i


Neighborhood ChangingRate (NCR)

• Example:

The NCR of Car 1 as a function of time, with Δt=1

t-3

t-2 t-1t

t-3t-2 t

t-1t

C2

C2

C1

C3

C3

C1

NCR=0

NCR=_

NCR=_

NCR=1

C4

C4

C1 C1

C4

C4



Definition (Uniform Mobility Model) :A Uniform Mobility Model (UMM) is a model preserving uniformly distributed velocities and densities

Theorem :Defining speedav- representing and densityav– representing the average node density both generated by an UMM, NCR has the following features

1. 0 ≤ NCR(t) ≤ 12. NCR speedav

3. NCR densityav

Proof: See paper



• The performance of protocols using data dissemination usually depends on multiple criteria– Speed– Velocity– Mobility pattern– …

• Evaluating a protocol depending on multi-criteria is hard and gives arguable results.

• More specifically, Mobility Patterns are not easily quantifiable because they depend on a too large set of parameters.

• It would be preferable to evaluate it depending on a single

criterion.



• As NCR is independent to speedav and densityav, In all models where the real speed and density diverge

from the initial speedav and densityav,

NCR controls the set of parameters that generates the complex spatial and temporal dependencies we may observe in realistic mobility patterns

• Specific Topologies or Mobility Patterns may become less relevant to evaluate the performance of dissemination protocols

• With a given speedav , densityav, and NCR, we can perform cross-topology and cross-mobility patterns performance evaluation.



• A similar situation also exists in Transportation Planning:– How to represent traffic flows in transportation that depend

on multi-parameters such as:• Speed, density, volume/capacity ?

– Level of Service (LOS) : Works like an American report card grade, using the letters A through F, with A being best and F being worst.

– By using LOS classification and referring to a traffic situation as having a particular LOS , engineers can have a global knowledge of traffic condition in a particular area.

• NCR is designed to have the same usage:– By referring to data dissemination as having a particular

NCR, we can have an intuitive vision of its efficiency, and thus evaluate accurately VANET Protocols using this feature.


Agenda





• Conclusion


The Mobeyes Protocol

• Mobeyes [1] is a protocol for sensed data mining in vehicular environments:– Periodic diffusion of a summary of sensed data– On demand harvesting of sensed data

• Mobeyes Architecture– Mobeyes Sensing Interface (MSI) : Interface responsible for the

access to the sensors or GPS– Mobeyes Data Processor (MDP): Reads raw data and generates the

summaries– Mobeyes Diffusion/Harvesting Processor (MDHP): Opportunistically

diffuses the summaries or on demand harvests the raw data.

• Mobeyes uses epidemic dissemination to diffuse the summaries. So, it is an appropriate choice to validate NCR.

[1] Uichin Lee et al. University of California, PerSeNS 2006


The Mobeyes Protocol

• Example:

Mobeyes Single Hop Passive Diffusion


Agenda





• Conclusion


Simulation Results

Data Dissemination Protocol

Mobeyes

Simulator NS-2.27

Hello Intervals 3[s]

Data Generation intervals

10’000[s]

Simulation time 2000[s]

Simulation Area 2400[m] x 2400[m]

Number of Nodes 100

Tx Range 250[m]

Speed 5[m/s], 15[m/s], 25[m/s]

Simulation Parameters Simulation Environment

Urban Map Topology Triangle Topology

760m

2400m


Mobility Models

Track Model Random Waypoint Model

[1] Biao Zhou et al. University of California, MilCom 2004


Latency

Latency on a Triangle Topology as a function of speed

5 10 15 20 2520

30

40

50

60

70

80

90

100

Average velocity in [m/s]

Delay [s]

High NCRMedium NCRLow NCR


Latency

Latency on a Map Topology as a function of speed

5 10 15 20 25300

400

500

600

700

800

900

1000

1100

1200

1300

Average velocity in [m/s]

Delay [s]

High NCRMedium NCRLow NCR


Harvesting Efficiency

NCR on a Triangle Topology with a speed of 5 m/s

0 50 100 150 2000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]

Harversting efficiency [%]

High NCR, speed=5m/sMedium NCR speed=5m/sLow NCR speed=5m/s



0 10 20 30 40 50 60 70 800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]






0 10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]






• Mobeyes on a triangle topology:

Time before which 100% of the data is harvested

High Medium Low

5 m/s 75.08 s102.58

s181.89

s

15 m/s 50.27 s 55.97 s 77.41 s

25 m/s 38.1 s 47.23 s 49.08 s

velocityav

NCR


0 500 1000 1500 20000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]




NCR on a Map Topology with a speed of 5 m/s


0 500 1000 1500 20000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]






0 500 1000 1500 20000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]







• Mobeyes on a map topology:


High Medium Low

5 m/s 1986.3 s>>

2000s>>

2000s

15 m/s 1344 s1548.5

s>>

2000s

25 m/s 1093.1 s1246.1

s 1610.7

s

velocityav

NCR


10 15 20 250

5

10

15

20

25

30

35

40

Speed [m/s]

Harvesting delay [s]

MAPTriangleRWM

Cross-Topology Comparison

Latency for scenarios with same speed, density and NCR, and for different mobility models and topologies



0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]


MAP NCR, speed=15m/sTriangle NCR speed=15m/sRWM NCR speed=15m/s

Harvesting rate for scenarios with same density, speed and NCR, and different mobility models and topologies



• All Models having the same NCR


Mobeyes + Map

Mobeyes + Triangle

RWM + Triangle

15 m/s 58.43s 50.26s 61.58 s

velocityav

topology


Conclusion

• NCR is a novel parameter describing data dissemination

• NCR is able to describe spatial and temporal dependencies, not covered by speed or density.

• NCR is an unifying parameter, as it regroup mobility patterns and topology parameters.

• Data dissemination in any kind of topology and for any type of mobility pattern, can be the fully control by three parameters:– Average Speed– Average Density– NCR


Questions ?

Jérôme Hä[email protected]

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Institut Eurécom † Department of Mobile Communications Sophia Antipolis, France