Thesis Author: Shan Gong Supervisor:Sven-Gustav Häggman

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S-72.158 Master’s thesis seminar 8th August 2006 QUALITY OF SERVICE AWARE ROUTING PROTOCOLS IN MOBILE AD HOC NETWORKS Thesis Author: Shan Gong Supervisor: Sven-Gustav Häggman

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S-72.158 Master’s thesis seminar 8th August 2006 QUALITY OF SERVICE AWARE ROUTING PROTOCOLS IN MOBILE AD HOC NETWORKS. Thesis Author: Shan Gong Supervisor:Sven-Gustav Häggman. Outlines. Ad Hoc Networks - PowerPoint PPT Presentation

Transcript of Thesis Author: Shan Gong Supervisor:Sven-Gustav Häggman

Page 1: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

S-72.158 Master’s thesis seminar 8th August 2006

QUALITY OF SERVICE AWARE ROUTING PROTOCOLS IN MOBILE AD HOC NETWORKS Thesis Author: Shan Gong

Supervisor: Sven-Gustav Häggman

Page 2: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Outlines

Ad Hoc Networks QoS (Challenge of implementing QoS in a

d hoc networks, QoS metrics, QoS metric calculation)

AODVAccess admission control in QAODV QAODVSimulation Environment and Results

Page 3: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Mobile Ad hoc networks

The application of mobile ad hoc networks becomes more and more popular.

Applications of Mobile Ad Hoc Networks (Military Applications, Emergency Operations, Wireless Mesh networks, Wireless sensor networks).

Page 4: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

QoS

“Quality of Service is the performance level of a service offered by the network to the user”.

QoS considered in this thesis work: low end to end delay (e.g. real time traffics)

Page 5: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Challenge of QoS in ad hoc networks

Dynamically varying network topology Lack of precise state information Shared radio channel The resources such as data rate, battery

life, and storage space are all very limited in ad hoc networks.

Page 6: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

QoS metrics

Additive metrics (end to end delay)Concave metrics (data rate)Multiplicative metrics. (link outage

probability)The generally used metrics for real time

applications are data rate, delay, delay variance (jitter), and packet loss.

Page 7: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Calculation for locally available data rate

Method 1: Transmission range = Carrier sensing range

Case 1 is the data rate used by node i for receiving data.

Case 2 is the data rate consumed by neighbors who are receiving.

Case 3 is the data rate consumed by neighbors who are sending.

Case3

Nk,Njjk

Case2

Njj

Case1

iii

iii

)xZZ(Rate DataRate Data Available

Page 8: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Calculation for local available data rate

Method 2: Carrier sensing range is more than twice of the transmission range (more realistic)

Data Rate =(N*S*8)/T

The used data rate is the sum of the sent, received and sensed data rate

Page 9: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

AODV routing protocol

ReactiveRREQ (broadcast) and RREP (unicast)RERR

Page 10: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Access Admission Control

Available bandwidth ? Required bandwidth

Page 11: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Required data rate at each node Method 1 carrier sensing range = transmission rangeWith a N-hop route, the source and destination nodes should satisfy ABi>=2r, the second and N-1 nodes ABi >=3r and the intermediate nodes ABi >=4r. Here, r is the required data rate requirement and ABi is the available data rate at node i. N-1 node is the node on the path which is next to the destination node.

EDCBA

Page 12: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Required data rate at each node

Method 2 carrier sensing range >= 2* transmission range

The contention count is calculated as follows If hreq > 2 hreq = 2

otherwise hreq=hreq If hrep > 3 hrep = 3

otherwise hrep=hrep

CC = hreq + hrep

Page 13: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Required data rate at each node Method 2 carrier sensing range >= 2* transmission range Example

1

2

3

4

5

6

7

Page 14: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Maximum data rate Vs. Hops of a route

Maximum data rate

0

0,5

1

1,5

2

2,5

3

3,5

4

0 2 4 6 8 10 12

Contention Count

Max

imu

m d

ata

rate

Page 15: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

QAODV routing protocol-draft

Session IDMaximum delay extension fieldMinimum data rate extension field

Node satisfying these requests could broadcast the RREQ further

List of sources requesting QoS guarantees

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AODV vs. QAODV

Recv RREQ

Recv RREP

Enough Data rate?

Broadcast RREQ

Forward RREP

Enough Data rate?

Drop RREQ

Drop RREP

Periodically check Available Data rate

Available data rate >0

Do nothing

Send ICMP_QoS_Lost

Recv ICMP_QoS_ Lost

Whether I am the source node

Forward ICMP_QoS Lost

Stop traffic

Page 17: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Example for QAODV—periodic check for available data rate

1

0

3

2600 m

150 m

33

Move direction of Node 3

550 m (Interference range)

Traffic stopped

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Scenario

Page 19: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Simulations with both AODV and QAODV

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Performance metrics

Average end to end delayPacket Delivery Ratio (PDR) Normalized Overhead Load (NOL) Route finding time of the first route

Page 21: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Simulation Environment

The channel type is “wireless channel” radio propagation model is “two ray ground”. MAC layer based on CSMA/CA as in IEEE

802.11 is used with RTS/CTS mechanism. The data rate at physical layer is 11 Mbps. Queue type is “drop tail” the maximum queue length is 50. Routing protocols are the AODV and the

QAODV. The transmission range and carrier sensing

range are 250 m and 550 m respectively.

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Specific Scenarios for Simulations.

The area size is 700 m * 700 m 20 nodes in this area. Every experiment will be run 1000 s in total. (500 s is added at the

beginning of each simulation to stabilize the mobility model.) Each data point in the results represents an average of 10 trails

with same traffic model but different randomly mobility scenarios.

For fairness comparisons, same mobility and traffic scenarios are used in both the AODV and the QAODV routing protocols.

In the following set of simulations, a group of data rates ranging from 50 kbps to 1800 kbps is applied.

The mobility scenario is with a pause time of 10 seconds and the maximum node speed of nodes is 1 m/s.

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Traffic pattern

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Average end to end delay

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Packet Delivery Ratio

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Normalized Overhead Load

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Time used to find the first route--First traffic flow

0

0,05

0,1

0,15

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0,25

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0,05 0,3 0,6 0,9 1,2 1,5 1,8

Data rate (Mbps)

Tim

e d

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tio

n (

seco

nd

)

AODV - traffic flow 1

QAODV - traffic flow 1

Time used to find the first route

Time used to find the first route--Second traffic flow

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0,05 0,3 0,6 0,9 1,2 1,5 1,8

Data rate (Mbps)

Tim

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seco

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AODV - traffic flow 2

QAODV - traffic flow 2

The first traffic flow 553 s ~ 774 s.

The second traffic flow 680 s ~ 780 s.

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Summary and Conclusions

QAODV outperforms AODV in terms of end to end delay

Constrain the packets which might be useless to the network

More routing packets are sent (brings problem when node density is high)

More QoS metrics could be added to the routing protocol (delay, packet loss)

Page 29: Thesis Author: Shan Gong    Supervisor:Sven-Gustav Häggman

Thank you