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Adisorn Lertsinsrubtavee,Dr.Teerapat Sanguankotchakorn,Dr.Anis Laouiti,Prof.Kanchana Kanchansut2010 – 02 – 25

The Third AsiaFI Winter SchoolSeoul National University, Seoul, Korea

Velocity effect on the Performance of MANEMO

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Outline

• Background• Objective• Movement Scenario• E- Model• Data analysis• Conclusion

BACKGROUND

Motivation• The Situation –A disaster area several miles on a side in which almost all communications have been wiped out.

• Cellular network and Public SW are notavailable so people will lost of contact

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Post disaster recovery

• Rescue operation• Small group of movement

(eg. Car, boat,..)• Communication between

group and to based camp• Multi-hops wireless

network with vehicular mobility

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NEtwork MObility (NEMO)• NEMO (RFC 3963)1

• Mobile Router (MR) MRs equip in vehicles Provide the connectivity to its

client via ingress interface Connect to high level MR to reach

the HA via egress interface

• Home Agent (HA) All the NEMO networks have to

register HoA and CoA to localize the position

• Mobile Network Node(MNN) Connect to MR through WiFi

[1] V. Devarapalli, R. Wakikawa, A. Petrescu, P. Thubert “RFC3963 - Network Mobility (NEMO) Basic Support Protocol”

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NEtwork MObility (NEMO) cont.

• NEMO problem Routing Optimization problem[2]

Routing is highly Inefficiency in nested NEMO

Packets have to route to HA

[2] Wakikawa,R., Thubert, P., Boot, T., Bound,J. & McCarthy, B Problem Statement and Requirements for MANEMO”, (draft-wakikawa-manemo-problem-statement-01.txt), IETF, Internet Draft, July 2007

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MANEMO

• NEMO + MANET MANEMO • Nested NEMO structure

NEMO is designed to provide global connectivity

MANET supports the data transfering in local connectivity

• Solution (NEMO +) TD (Tree Discovery) NINA (Network In Node

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Statement of Problem

• What is the limitation or optimal value of velocity? Vehicles are always used in MANEMO but until now we still

have not known the limitation of their velocity

• How accurate of the analytic or simulation result ? The real experiment can be the best solution to answer

this question but most of research works only consider to analytic model and simulation method

• How can we know the optimal value is useable ? Various movement scenarios can be provide to find out

more the accurate result

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Objective

• To study and apply the MANEMO approach to the real experiment

• To measure and evaluate the performance of MANEMO environment which impacted by velocity

• To assess the quality of VoIP service on the MANEMO environment

• To specify the limitation of velocity in the MANEMO environment

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MOVEMENT SCENARIO

Parameter Declaration

• Group of movement

• Outputs measurement Throughput Packet Loss End to End Delay VoIP Call Quality

• Input Factors Static case (ref.) Speed 5 – 35 km/h

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Output Performance

• Network performance assessment ICMP ping and Netperf RTT Packet Loss Rate (PLR) Throughput (Tp)

• VoIP call quality assessment Linnphone (SIP soft phone application) End to End Delay (Dee) Packet Loss Rate (PLR)

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Movement Scenario

IntERLab

200 m

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Data Measurement

MR 1MR 2

MR 3

Mobile Network 1

HQ (CN) HAHome Network

802.11 AP

IPv6 Router

MNN2MNN1

Move with different speed

Logical connection

Output Parameters ToolsTp Netperf

RTT,PLR PingDee(RTP), PLR (RTP) Wireshark

VoIP call Linnphone

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ITU-T G.107, THE E MODEL

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Introduction of E-Model

• Recommendation G.107 (ITU standard)• Transmission planning tool

Evaluating end-to-end Voice quality

• Calculation a scalar quality rating value Transmission rating factor (R)

• Rating Factor, R (0 - 100) 100 = Excellent performance 60 = Acceptable level

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E-ModelReference Connection

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E-Model Equation

• R = Rating factor (0-100)• Ro= the effect of noise and loudness ratio• Is = The effect of impairments occurring

simultaneous with the speech signal• Id = Delay impairment factor• Ie –eff =Equipment Impairment factor

• A= Expectation factor

o s d e effR R I I I A

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Default value from ITU-T G.107

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Reduction model

• Substitute default values from ITU-T G.107• Idd represents the impairment caused by one way

delay (Ta)• Ie-eff represents the impairment caused by type of

codec, Packet loss, and Packet loss burst ratio

93.35 dd e eefR I I

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DATA ANALYSIS

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Network performance assessment

Speed Static 5 Km/h 10 km/h 15 km/h 25 km/h 35 km/h

PLR(%) 0.51 16.53 18.45 28.29 32.73 34.51

RTT (ms) 12.61 13.51 16.87 18.30 20.99 27.58

Static 5 10 15 25 350510152025303540

Packet Loss Rate

Speed (km/h)

Perc

enta

ges

Static 5 10 15 25 350

5

10

15

20

25

30RTT average

Speed (km/h)

ms

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Static 5 10 15 25 350

0.5

1

1.5

2

2.5

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Throughput

Speed (km/h)

TP [M

B/s]

Speed Static 5 km/h 10 km/h 15 km/h 25 km/h 35 km/h

Tp (Mbps) 2.55 2.23 2.16 2.09 1.62 1.52

Network performance assessment

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E model Rating factor

Speed Static 5 km/h 10 km/h 15 km/h 25 km/h 35 km/h

R Value 91.11 91.79 70.94 57.63 46.76 37.29

Static

<5

10

15

25

35

0 10 20 30 40 50 60 70 80 90 100

R value without H/O period

R value

Spee

d (k

m/h

)

Quality Bad Poor Low Medium High Best

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Rating factor with User satisfaction

Range of E model rating R

Speech transmission quality category

User satisfaction Speed (km/h)

90≤R<100 Best Very satisfied V≤5.43

80≤R<90 High Satisfied 5.43<V≤7.83

70<R≤80 Medium Some users dissatisfied 7.83<V≤10.35

60<R≤70 Low Many users dissatisfied 10.35<V≤14.11

50<R≤60 Poor Nearly all users dissatisfied 14.11<V≤22.02

R≤50 Bad Not Recommended V>22.02

Static <5 10 15 25 350102030405060708090100

Rating Factor of speed effect compare with user satisfaction level

Speed (km/h)

R va

lue

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Performance Comparison of single group movement

Output Static 35 km/h Difference (%)Throughput (Mbps) 2.55 1.51 40.56

Packet Loss Rate(%) 0.51 34.51 34

RTT Delay (ms) 12.61 27.58 118.75

R factor 91.11 26.90 53.82

Throughput Packet Loss RTT Delay R value R value exclude

H/O time

0

20

40

60

80

100

120

140

40.5634.00

118.75

53.82

Performance decreased

Perc

enta

ges

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VoIP Quality assessment

Static 5 10 15 25 350

5

10

15

20

25

Packet Loss

Speed (km/h)

Perc

enta

ges

Static 5 10 15 25 350

2

4

6

8

10

12

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End to End Delay avearage

Speed (km/h)

Tim

e [m

s]Speed Static 5 Km/h 10 km/h 15 km/h 25 km/h 35 km/h

PLR(%) 1.00 20.54 13.06 7.19 8.34 6.09

Dee (ms) 10.13 12.77 11.42 10.83 11.11 10.73

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VoIP Quality assessment : R value

Static

5

10

15

25

35

0 10 20 30 40 50 60 70 80 90 100

R value

R value

Spee

d (k

m/h

)

Quality Bad Poor Low Med High Best

Speed Static 5 km/h 10 km/h 15 km/h 25 km/h 35 km/h

R value 89.13 32.94 50.98 68.39 64.67 71.38

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CONCLUSION

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Conclusion

• Speed increment can decrease performance of MANEMO i.e. Packet loss, Throughput, Round Trip Time delay Performance decreased from 34-118.5%

• Acceptable level of E model (R=60) Speed must less than 14.11 km/h

• VoIP call quality is increased when speed is increased • H/O period

H/O time has a significant impact to quality of communication Reducing H/O time can increase the quality of speech

transmission

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Future work

• Validate the experiment result Simulation

• Reducing Hanover time Fast H/O

• Field experiment with different movement scenario• Other routing protocols

OLSR…

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THANK YOU