 Optimized Link State Routing (OLSR)

(1)

Übersicht

 Untersuchte Netzstruktur und Problemstellungen

 Topologie-basierte Routingprotokolle

• Destination-Sequenced Distance-Vector Routing (DSDV)

• Optimized Link State Routing (OLSR)

• Ad-Hoc On-Demand Distance Vector Routing (AODV)

• Dynamic Source Routing (DSR)

 Geographische Routingprotokolle

(2)

Proactive protocols – OLSR

 Combine link-state protocol & topology control

 Optimized Link State Routing (OLSR)

 Topology control component: Each node selects a minimal dominating set for its two-hop

neighborhood

• Called the multipoint relays

• Only these nodes are used for packet forwarding

• Allows for efficient flooding

 Link-state component: Essentially a standard link-state algorithm on this reduced topology

• Observation: Key idea is to reduce flooding overhead

(here by modifying topology)

(3)

Multipoint Relays

 MPR Concept

• Subset M of neighbors of given node S which covers all 2-hop neighbors of S

• Node A is covered if it can receive message from S either directly or via 1-hop neighbor

• Nodes in subset M are called relay point

 Minimum-size multipoint relay is of interest for energy efficient broadcasting

• Reduce number of broadcast relays but

• Cover all nodes by the broadcast

 Example for node S

• 1-hop neighbors: A, B, C, D, E, F

• 2-hop neighbors: U, V, W, X

• A multipoint relay: {A, B, D, E}

• A minimum-size multipoint relay: {C, F}

F U

X

D B

A

E V

W

S C

(4)

Multipoint Relays

 Greedy approximation

• Repeat selecting node B which maximizes set of not so far covered nodes

 Example

• (1) Maximum provided either by C or F; select C

• (2) Maximum provided then by F

• (3) All nodes covered; result {C, F}

F U

X B

A

E V

S

(5)

Übersicht

• Optimized Link State Routing (OLSR)

• Ad-Hoc On-Demand Distance Vector Routing (AODV)

• Dynamic Source Routing (DSR)

(6)

AODV: Main Idea

S

D

RREQ

RREQ RREQ

(7)

AODV: Main Idea

S

D RREQRREP

(8)

AODV: Main Idea

S

D 1

2 3

1 2 3

(9)

AODV: Node Mobility

S

D

RREQ RREQ RREQ

(10)

Übersicht

• Optimized Link State Routing (OLSR)

• Ad-Hoc On-Demand Distance Vector Routing (AODV)

• Dynamic Source Routing (DSR)

(11)

DSR route discovery procedure

Search for route from 1 to 5

1

7

6

5 4 3

2

[1]

[1] ¹

7

6

5 4 3

2

[1,7]

[1,4]

1

7

5 4 3

2 [1,7,2]

[1,4,6]

1

7

6

5 4 3

2

(12)

Übersicht

 Topologie-basierte Routingprotokolle

 Geographische Routingprotokolle

• Greedy-Routing und Planar-Graph-Routing

• Konstruktion von planaren Graphen

• Lokales Multicasting

• Beispiel MSTEAM

(13)

Localized Geographic Unicast Forwarding

Assumptions:

 Localization system

 Nodes know position of

• Themselves

• Their neighbors

• The destination source node

destination node

(14)

Geographic Greedy Routing

T S

A

(a)

F B

D C

E

Strategy: select from nodes closer to the destination the one which minimizes a local cost metric

B

E T S

(b)

?

A

C

D F

G

Problem: greedy routing failure

(15)

Recovery based on Planar-Graph Routing

source node

(16)

Planar Graph Routing Example

T S

P

F

(17)

T S

Q

• Greedy-Routing und Planar-Graph-Routing

• Konstruktion von planaren Graphen

• Beispiel MSTEAM

(21)

We need a Planar Graph

U V U V

Gabriel Graph (GG) Relative Neighborhood Graph (RNG)

W

U

V

Delaunay

Triangulation (DT)

(22)

We need a UDG or QUDG

 UDG: Localized GG and RNG versions based on 1-hop neighbors

 UDG: Localized DT version based on 2-hop neighbors (and less) Quasi unit disk graph (QUDG)

U V

Unit disk graph (UDG)

r_min r_max

U

(23)

Problems and Limitations

 Locally constructing a planar graph in arbitrary networks is impossible

 Even worse: localized unicast routing is impossible in arbitrary graphs

 Localized single path algorithms deviation from shortest paths

• Let k be the hop/Euclidean length of the shortest path connecting s and t

• Localized single path algorithms may produce paths of length O(k²)

• Some even worse but some exist which are upper bounded by O(k²) u

x v

y

t s

(24)

Localized Unicast Routing in Practice!

 Wireless network graph has structure!

 Aim at localized unicast approaches with high delivery rate

arbitrary graph wireless network graph

(25)

Geographic Clustering

(26)

(27)

(28)

(29)

K-Hop Clustering

(30)

(31)

(32)

(33)

Übersicht

• Lokales Multicasting

• Beispiel MSTEAM

(34)

Localized Multicast Forwarding Problem

Assumptions:

 Location system

 Nodes know position of

• Themselves

• Their neighbors source node

destination node destination node

(35)

Building Blocks

T1

T3

T2 S

B A

D C

(36)

Building Blocks – Message Split

T1

T3

T2 S

B A

D C

(37)

Building Blocks – Next Hop Selection

T1

T3

T2 S

B A

D C

?

(38)

Building Blocks – Recovery

T1

T3

T2 S

B A

D C

?

(39)

Übersicht

• Beispiel MSTEAM

(40)

EMST Backbone Assisted Localized Routing

T9

T7

T6

T1

T3 S

T8

T5

T2 T1,…,T9

(41)

T8 T9

T7

T6

T5

T1

T2

T3 S

EMST(S,T1,…,T9)

(42)

T8 T9

T7

T6

T5

T1

T2

T3

D1

D2 D3

S

(43)

T8 T9

T7

T6

T5

T1

T2

T3 A

B

C

S

T7,T8,T9

T1,T2,T3 T4,T5,T6

(44)

The Cost over Progress Framework

T3

T1

W V

T2 S

Which one is the better next hop node?

T1,T2,T3

(45)

The Cost over Progress Framework

 Approximate expected number of hops H(S,V)

 H(S,V)  |EMST(S,T1,T2,T3)| / (|EMST(S,T1,T2,T3)| - |EMST(V,T1,T2,T3)|)

 Approximate expected cost C(S,V) = cost(S,V) * H(S,V)



T3

T1

W V

T2 S

(46)

MSTEAM & MFACE

S T6

T5

T4

T1

T2

F1

F2

(47)

MSTEAM & MFACE

S T6

T5

T4

T1

T2

F1