BEACONLESS GEOCAST PROTOCOLS ARE INTERESTING, EVEN IN 1D J oachim - - PowerPoint PPT Presentation

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BEACONLESS GEOCAST PROTOCOLS ARE INTERESTING, EVEN IN 1D J oachim Gudmundsson, Irina Kostitsyna, Maarten Lffler, Tobias Mller, Vera Sacristn, Rodrigo I. Silveira BEACONLESS GEOCAST ROUTING BEACONLESS GEOCAST ROUTING BEACONLESS GEOCAST

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SLIDE 1

BEACONLESS GEOCAST PROTOCOLS ARE INTERESTING, EVEN IN 1D

Joachim Gudmundsson, Irina Kostitsyna, Maarten Löffler, Tobias Müller, Vera Sacristán, Rodrigo I. Silveira

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SLIDE 2

BEACONLESS GEOCAST ROUTING

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SLIDE 3

BEACONLESS GEOCAST ROUTING

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SLIDE 4

BEACONLESS GEOCAST ROUTING

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SLIDE 5

BEACONLESS GEOCAST ROUTING

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SLIDE 6

BEACONLESS GEOCAST ROUTING

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SLIDE 7

BEACONLESS GEOCAST ROUTING

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SLIDE 8

BEACONLESS GEOCAST ROUTING

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SLIDE 9

BEACONLESS GEOCAST ROUTING

?

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SLIDE 10

Nodes only know their own location

BEACONLESS GEOCAST ROUTING

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SLIDE 11

Nodes only know their own location

BEACONLESS GEOCAST ROUTING

Messages must be sent from certain nodes to certain locations

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SLIDE 12

Nodes only know their own location

BEACONLESS GEOCAST ROUTING

Messages must be sent from certain nodes to certain locations When nodes receive messages, they must decide whether to retransmit them or not

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SLIDE 13

Nodes only know their own location

BEACONLESS GEOCAST ROUTING

Messages must be sent from certain nodes to certain locations When nodes receive messages, they must decide whether to retransmit them or not When a node retransmits a message, neighbouring nodes receive it

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SLIDE 14

Nodes only know their own location

BEACONLESS GEOCAST ROUTING

Messages must be sent from certain nodes to certain locations When nodes receive messages, they must decide whether to retransmit them or not When a node retransmits a message, neighbouring nodes receive it But remember: nodes do not know the graph structure!

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SLIDE 15

BEACONLESS GEOCAST PROTOCOLS

Many protocols exist and are used in practice.

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SLIDE 16

BEACONLESS GEOCAST PROTOCOLS

s t

Many protocols exist and are used in practice.

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SLIDE 17

BEACONLESS GEOCAST PROTOCOLS BEACONLESS GEOCAST PROTOCOLS

s t

Many protocols exist and are used in practice.

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SLIDE 18

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted

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SLIDE 19

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06]

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SLIDE 20

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06]

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SLIDE 21

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11]

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SLIDE 22

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11]

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SLIDE 23

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

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SLIDE 24

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

Beacon-Less Routing (BLR): based on dynamic forwarding delay

[Heissenbüttel et al ’04]

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SLIDE 25

BEACONLESS GEOCAST PROTOCOLS

s t

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

Beacon-Less Routing (BLR): based on dynamic forwarding delay

[Heissenbüttel et al ’04]

Geographic Distance Routing (GeDiR): beaconless version of greedy routing

[Stojmenovic and Lin ’01]

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SLIDE 26

BEACONLESS GEOCAST PROTOCOLS

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

Beacon-Less Routing (BLR): based on dynamic forwarding delay

[Heissenbüttel et al ’04]

Geographic Distance Routing (GeDiR): beaconless version of greedy routing

[Stojmenovic and Lin ’01]

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SLIDE 27

BEACONLESS GEOCAST PROTOCOLS

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

Beacon-Less Routing (BLR): based on dynamic forwarding delay

[Heissenbüttel et al ’04]

Geographic Distance Routing (GeDiR): beaconless version of greedy routing

[Stojmenovic and Lin ’01]

Many protocols exist and are used in practice. Different protocols cause different network load.

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SLIDE 28

BEACONLESS GEOCAST PROTOCOLS

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

Beacon-Less Routing (BLR): based on dynamic forwarding delay

[Heissenbüttel et al ’04]

Geographic Distance Routing (GeDiR): beaconless version of greedy routing

[Stojmenovic and Lin ’01]

Many protocols exist and are used in practice. Different protocols cause different network load. We wish to capture this phenomenon in mathematical language.

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SLIDE 29

BEACONLESS GEOCAST PROTOCOLS

Simple flooding: all incoming packets are always retransmitted MinTrans (M)-heuristic: incoming packets are retransmitted up to M times [Hall, Auzins ’06] Threshold (T)-heuristic: retransmit a packet if heard from distance at least T [Hall, Auzins ’06] Center-Distance (CD): retransmit a packet if getting closer to the destination [Hall ’11] Center-Distance with Priority (CD-P): retransmit a packet that progresses the most to the destination [Hall ’11] Geometric Random Forwarding (GeRaF): nodes retransmit packets layer by layer

[Zorzi ’04]

Beacon-Less Routing (BLR): based on dynamic forwarding delay

[Heissenbüttel et al ’04]

Geographic Distance Routing (GeDiR): beaconless version of greedy routing

[Stojmenovic and Lin ’01]

π Π Σ +

  • ⊕

± ∂ √ 5 φ ∞ 2

  • !

≈ ∪ θ ∩ ∧ 7 ¬ → = 9 ∇ d σ ε R E P Q Z N ∨ = ≤ ≥ = ⇒ ∞ ≈ ∨ 4 ∇ ¬

  • θ

R ± ! = 9 E → Z φ ≤ 5 = ⇒ π √

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SLIDE 30

FAIR MEDIUM ACCESS

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SLIDE 31

FAIR MEDIUM ACCESS

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SLIDE 32

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 33

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 34

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 35

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 36

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 37

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 38

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 39

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 40

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 41

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 42

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate”

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SLIDE 43

FAIR MEDIUM ACCESS

At any point in time, every node has then same probability to be the next to “activate” This assumption abstracts from different underlying collision handling techniques

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SLIDE 44

CENTER-DISTANCE VS CENTER-DISTANCE-P

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SLIDE 45

CENTER-DISTANCE VS CENTER-DISTANCE-P

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SLIDE 46

CENTER-DISTANCE VS CENTER-DISTANCE-P

I

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SLIDE 47

CENTER-DISTANCE VS CENTER-DISTANCE-P

J I

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SLIDE 48

CENTER-DISTANCE VS CENTER-DISTANCE-P

V J I

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SLIDE 49

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J I

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SLIDE 50

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J I R

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SLIDE 51

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CD&CD-P:

I R

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SLIDE 52

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CDist(v) d

CD&CD-P:

I R

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SLIDE 53

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CDist(v) d

CD&CD-P:

I R

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SLIDE 54

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CD:

I R

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SLIDE 55

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CD:

I R

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SLIDE 56

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CD:

I R

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SLIDE 57

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CD-P:

I R

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SLIDE 58

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CDist(j) CDist(m)

CD-P:

I R

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SLIDE 59

CENTER-DISTANCE VS CENTER-DISTANCE-P

M V J

CDist(j) CDist(m)

CD-P:

I R

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SLIDE 60

OUR GOAL

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SLIDE 61

OUR GOAL

Analyze and compare heuristics

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SLIDE 62

OUR GOAL

Analyze and compare heuristics Develop theoretical model

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SLIDE 63

OUR GOAL

Analyze and compare heuristics Develop theoretical model

  • Quality measure: success rate and RecMess
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SLIDE 64

OUR GOAL

Analyze and compare heuristics Develop theoretical model

  • Quality measure: success rate and RecMess
  • Discrete time setting: packets sent in rounds
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SLIDE 65

OUR GOAL

Analyze and compare heuristics Develop theoretical model

  • Quality measure: success rate and RecMess
  • Discrete time setting: packets sent in rounds
  • Conflict resolution: fair medium access
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SLIDE 66

OUR GOAL

Analyze and compare heuristics Develop theoretical model

  • Quality measure: success rate and RecMess
  • Discrete time setting: packets sent in rounds
  • Conflict resolution: fair medium access
  • Problem. Validate beaconless geocast heuristics within
  • ur model, and analyze success rate and RecMess

under various scenarios.

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SLIDE 67

TODAY

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SLIDE 68

TODAY

2 scenarios in 1D:

  • Unbounded reach
  • Bounded reach
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SLIDE 69

TODAY

2 scenarios in 1D:

  • Unbounded reach
  • Bounded reach

Messages are sent from left to right, everybody can “hear” everybody.

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SLIDE 70

TODAY

2 scenarios in 1D:

  • Unbounded reach
  • Bounded reach

Messages are sent from left to right, everybody can “hear” everybody. Messages are sent from left to right. Each node can only hear from its r predecessors.

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SLIDE 71

1D UNBOUNDED REACH SCENARIO

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SLIDE 72

1D UNBOUNDED REACH SCENARIO

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SLIDE 73

1D UNBOUNDED REACH SCENARIO

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SLIDE 74

FLOODING IN 1D UNBOUNDED REACH SCENARIO

6 6 6 6 6 6 6

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SLIDE 75

FLOODING IN 1D UNBOUNDED REACH SCENARIO

6 6 6 6 6 6 6

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SLIDE 76

FLOODING IN 1D UNBOUNDED REACH SCENARIO

7 6 7 7 7 7 7

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SLIDE 77

FLOODING IN 1D UNBOUNDED REACH SCENARIO

7 6 7 7 7 7 7

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SLIDE 78

FLOODING IN 1D UNBOUNDED REACH SCENARIO

7 7 8 8 8 8 8

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SLIDE 79

FLOODING IN 1D UNBOUNDED REACH SCENARIO

7 7 8 8 8 8 8

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SLIDE 80

FLOODING IN 1D UNBOUNDED REACH SCENARIO

8 8 8 9 9 9 9

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SLIDE 81

FLOODING IN 1D UNBOUNDED REACH SCENARIO

8 8 8 9 9 9 9

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SLIDE 82

FLOODING IN 1D UNBOUNDED REACH SCENARIO

9 9 8 10 10 10 10

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SLIDE 83

FLOODING IN 1D UNBOUNDED REACH SCENARIO

9 9 8 10 10 10 10

success rate 100% RecMess = nk

n nodes, k messages

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SLIDE 84

1D BOUNDED REACH SCENARIO

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SLIDE 85

1D BOUNDED REACH SCENARIO

r

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SLIDE 86

1D BOUNDED REACH SCENARIO

r

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SLIDE 87

FLOODING IN 1D BOUNDED REACH SCENARIO

6 6

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SLIDE 88

FLOODING IN 1D BOUNDED REACH SCENARIO

6 6

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SLIDE 89

FLOODING IN 1D BOUNDED REACH SCENARIO

7 6 1 1

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SLIDE 90

FLOODING IN 1D BOUNDED REACH SCENARIO

7 6 1 1

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SLIDE 91

FLOODING IN 1D BOUNDED REACH SCENARIO

7 7 2 1

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SLIDE 92

FLOODING IN 1D BOUNDED REACH SCENARIO

7 7 2 1

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SLIDE 93

FLOODING IN 1D BOUNDED REACH SCENARIO

8 8 2 2 1

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SLIDE 94

FLOODING IN 1D BOUNDED REACH SCENARIO

8 8 2 2 1

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SLIDE 95

FLOODING IN 1D BOUNDED REACH SCENARIO

8 9 3 2 1

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SLIDE 96

FLOODING IN 1D BOUNDED REACH SCENARIO

8 9 3 2 1

success rate 100% RecMess = O(rk)

n nodes, k messages, range r

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SLIDE 97

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based Lower bound

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SLIDE 98

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based Lower bound

Ω(k) Ω(k)

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SLIDE 99

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk O(rk)

Lower bound

Ω(k) Ω(k)

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SLIDE 100

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk Mk O(rk) min{Mk, 2rk}

Lower bound

Ω(k) Ω(k)

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SLIDE 101

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk Mk

  • ⌈ n

2T ⌉k, ⌈ n T ⌉k

  • O(rk)

min{Mk, 2rk} O( rk

T )

Lower bound

Ω(k) Ω(k)

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SLIDE 102

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk Mk

  • ⌈ n

2T ⌉k, ⌈ n T ⌉k

  • O(rk)

min{Mk, 2rk} O( rk

T )

Lower bound

Ω(k) O(k3/2) Θ(k2 log(⌈n/k⌉ + 1)) Ω(k) Θ(nk) if k > n, else

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SLIDE 103

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk Mk

  • ⌈ n

2T ⌉k, ⌈ n T ⌉k

  • O(rk)

min{Mk, 2rk} O( rk

T )

Lower bound

Ω(k) O(k3/2) Θ(k) O(k log n) Θ(k2 log(⌈n/k⌉ + 1)) Ω(k) Θ(nk) if k > n, else

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SLIDE 104

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk Mk

  • ⌈ n

2T ⌉k, ⌈ n T ⌉k

  • min{2k, n(1+k−log n)}

O(rk) min{Mk, 2rk} O( rk

T )

O( nk

r )

Lower bound

Ω(k) O(k3/2) Θ(k) O(k log n) Θ(k2 log(⌈n/k⌉ + 1)) Ω(k) Θ(nk) if k > n, else

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SLIDE 105

RESULTS: RecMess

Unbounded reach scenario Bounded reach scenario Flooding M-heuristic T-heuristic CD CD-P Delay-based

nk Mk

  • ⌈ n

2T ⌉k, ⌈ n T ⌉k

  • min{2k, n(1+k−log n)}

O(rk) min{Mk, 2rk} O( rk

T )

O( nk

r )

Lower bound

Ω(k) O(k3/2) Θ(k) O(k log n) Θ(k2 log(⌈n/k⌉ + 1)) Ω(k) Θ(nk) if k > n, else

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SLIDE 106

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 107

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 108

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 109

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 110

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 111

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 112

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 113

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 114

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P

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SLIDE 115

CD AND CD-P IN BOUNDED REACH SCENARIO CD CD-P CD-P is better than CD

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SLIDE 116

CD AND CD-P IN BOUNDED REACH SCENARIO

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SLIDE 117

CD AND CD-P IN BOUNDED REACH SCENARIO

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SLIDE 118

CD AND CD-P IN BOUNDED REACH SCENARIO CD

slide-119
SLIDE 119

CD AND CD-P IN BOUNDED REACH SCENARIO CD

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SLIDE 120

CD AND CD-P IN BOUNDED REACH SCENARIO CD E(progress) >

r √ k+1

RecMess = O(k3/2)

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SLIDE 121

CD AND CD-P IN BOUNDED REACH SCENARIO

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SLIDE 122

CD AND CD-P IN BOUNDED REACH SCENARIO CD-P

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SLIDE 123

CD AND CD-P IN BOUNDED REACH SCENARIO CD-P

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SLIDE 124

CD AND CD-P IN BOUNDED REACH SCENARIO CD-P

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SLIDE 125

CD AND CD-P IN BOUNDED REACH SCENARIO CD-P E(progress) > r

2

RecMess = O(k)

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SLIDE 126

CD IN UNBOUNDED REACH SCENARIO

slide-127
SLIDE 127

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 128

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 129

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 130

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 131

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 132

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 133

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 134

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 135

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 136

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 137

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 138

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 139

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 140

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 141

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 142

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 143

CD IN UNBOUNDED REACH SCENARIO

slide-144
SLIDE 144

CD IN UNBOUNDED REACH SCENARIO

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SLIDE 145

CD IN UNBOUNDED REACH SCENARIO

Probility of choosing each node changes with the number of non-empty nodes!

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SLIDE 146

CD IN UNBOUNDED REACH SCENARIO

Probility of choosing each node changes with the number of non-empty nodes! RecMess is equal to the number of steps before all nodes are empty.

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SLIDE 147

CD IN UNBOUNDED REACH SCENARIO

Probility of choosing each node changes with the number of non-empty nodes! RecMess is equal to the number of steps before all nodes are empty.

RecMess

  • Θ(k2 log(⌈n/k⌉ + 1)) ,

if k ≤ n Θ(nk) , if k > n

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SLIDE 148

SUMMARY AND FUTURE WORK

slide-149
SLIDE 149

SUMMARY AND FUTURE WORK

Conclusion: beaconless geocast protocols are interesting in 1D!

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SLIDE 150

SUMMARY AND FUTURE WORK

1D scenarios Conclusion: beaconless geocast protocols are interesting in 1D!

slide-151
SLIDE 151

SUMMARY AND FUTURE WORK

1D scenarios

  • improve bounds

Conclusion: beaconless geocast protocols are interesting in 1D!

slide-152
SLIDE 152

SUMMARY AND FUTURE WORK

1D scenarios

  • improve bounds
  • non-uniform bounded reach scenario

Conclusion: beaconless geocast protocols are interesting in 1D!

slide-153
SLIDE 153

SUMMARY AND FUTURE WORK

1D scenarios

  • improve bounds
  • non-uniform bounded reach scenario

2D scenarios Conclusion: beaconless geocast protocols are interesting in 1D!

slide-154
SLIDE 154

SUMMARY AND FUTURE WORK

1D scenarios

  • improve bounds
  • non-uniform bounded reach scenario

2D scenarios

  • dense networks

Conclusion: beaconless geocast protocols are interesting in 1D!

slide-155
SLIDE 155

SUMMARY AND FUTURE WORK

1D scenarios

  • improve bounds
  • non-uniform bounded reach scenario

2D scenarios

  • dense networks
  • bottleneck scenarios

Conclusion: beaconless geocast protocols are interesting in 1D!

slide-156
SLIDE 156

THANK YOU!