Effects of Applying Mobility Localization on

Source Routing Algorithms for Mobile Ad Hoc Network

Hridesh Rajan

presented by Metin Tekkalmaz

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Outline Introduction Source Routing (SR) Problem Related Work SR w/ Mobility Localization (ML) An Example Scenario for ML Evaluation of ML Conclusion

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Introduction Routing is one of the problems in

Mobile Ad Hoc Networks (MANETs) Source Routing (SR) is one of the

solutions SR is easily affected by topology

changes Aim is to minimize possible

overheads due to toplogy changes

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Source Routing Problem (1/2)

SR is a problem of finding maintaining

a complete ordered list

of nodes through which the packet should pass to reach the destination.

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Source Routing Problem (2/2)

SR consists of Route discovery Route maintenance

Discovery procedure is done before any data packet can be send

The only overhead associated with SR is discovery (or maintenance) procedure

But this overhead can be significant due to rapid topology changes

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Related Work Works to reduce overhead in SR can be

grouped as two catagories Decrease the number of nodes receiving route

queries Decrease the number of route requests

generated by the source node Location Aided Routing is an example of

the first group and Dynamic Source Routing is an example of the second one

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SR w/ Mobility Localization Fundamental principle behind the

ML approach is Even if a mobile node has moved at a

time t, there is a fair possiblity that it might be found in the neighborhood until time t+δt

δ depends on Velocity vector V Definitiaon of neighborhood

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SR w/ Mobility Localization So in ML approach a node is

attempted to be found in the vicinity instead of restarting the discovery protocol

Use of ML affects maintenance procedure, not the discovery procedure

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SR w/ Mobility Localization When the data link layer of a node

reports a transmission error, it analyzes Source route Localization value (α) for the source

route Its position in the source route

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SR w/ Mobility Localization If the node is within α hops from the node

encountering next hop error it tries to repair the source route on its own

If it is successful traffic is forwarded to the new route

Else it checks the value of localization again to see whether any other node just before itself in the source route is eligible to repair the route

If such a node exists a route error message is sent to it

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SR w/ Mobility Localization A node can receive a route error

only if it is Within α hops to a node encountering

next hop error in a source route the source node

In the first case route repair and in the second case route request procedures are executed

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SR w/ Mobility Localization A node receiving the route error sends a

route repair message to every other one-hop neighbor except the node route error is received the node just before itself

Route repair is sent in a unicast manner If there is no node to send route repair as

unicast message, it is broadcasted Route repair messages have TTL-like info

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SR w/ Mobility Localization When a node repair message is

received It sends route repair reply to originator

if it knows the requested node Otherwise it checks whether it can

forward route repair message• If it can forward, it forwards the message

every node except the one the route repair is received from

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An Example Scenario for ML

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An Example Scenario for ML

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An Example Scenario for ML

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An Example Scenario for ML

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An Example Scenario for ML

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Evaluation Evaluation environment is NS Since prominent optimization techniques

are already implemented effect of ML on top of others is experimented

The maintenance cost is the number of control packets until network is stable

Experiment networks have 10 to 20 and 25 nodes

Value of localization is between 1 and 3

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Evaluation

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Evaluation

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Evaluation

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Conclusion ML is an enhancement over existing SR

techniques in ad hoc wireless networks ML approach decreases the overhead by

significant amount Overhead is not exponential with increase

in nuber of nodes Value of localization should be arranged

according to average source route length Probabilty of finding the moving node in

the neighborhood is important

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Conclusion Dynamic nature of MANETs strongly

affects the effectiveness of ML Lcalization value can be arranged

according to the changing dynamics of MANET