exploitation of path diversity in cooperative multi-hop wireless networks dissertation committee...

123
Exploitation of Path Diversity in Cooperative Multi-Hop Wireless Networks Dissertation Committee Department of Electrical and Computing Engineering University of Delaware Dr. Cimini Dr. Cotton Dr. Shen Dr. Morris (ECE Department) (ECE Department) (CIS Department) Candidate Chair : Jonghyun Kim : Dr. Bohacek

Upload: bertram-lloyd

Post on 13-Dec-2015

214 views

Category:

Documents


0 download

TRANSCRIPT

Exploitation of Path Diversity in Cooperative Multi-Hop Wireless Networks

Dissertation Committee

Department of Electrical and Computing EngineeringUniversity of Delaware

Dr. CiminiDr. CottonDr. ShenDr. Morris

(ECE Department)(ECE Department)(CIS Department)(CERDEC)

CandidateChair

: Jonghyun Kim: Dr. Bohacek

Introduction and challenges Aggressive path quality monitoring

BSP Efficient path quality monitoring

LBSP Opportunistic forwarding

LBSP2, LOSP, LMOSP Conclusion and future work

Outline

Introduction and challenges

Mobility Modeling2004 ~ 2009

4 papers

Mobility Modeling2004 ~ 2009

4 papers

CooperativePath Diversity

2005 ~ present4 papers

CooperativePath Diversity

2005 ~ present4 papers

Channel Activity Analysis

2007 ~ 20091 paper

Channel Activity Analysis

2007 ~ 20091 paper

User Perceptual Quality

Evaluation2008 ~ 2009

0 paper

User Perceptual Quality

Evaluation2008 ~ 2009

0 paper

Application Traffic Identification & Modeling

2008 ~ 20111 paper

Application Traffic Identification & Modeling

2008 ~ 20111 paper

ResearchResearch

Routing Technique

Proactive(e.g., OLSR)

Reactive(e.g., AODV)

Introduction and challenges

Introduction and challenges

: Routing control packet transmission

: No transmission

Proactive

Introduction and challenges

: Routing control packet transmission

: No transmission

Reactive

Introduction and challenges

: data packet from transport layer

Reactive

Introduction and challenges

Routing Technique

Proactive(e.g., OLSR)

Reactive(e.g., AODV)

Single path(e.g., AODV)

Multiple paths(e.g., AOMDV)

Introduction and challenges

Single path

B

A

Introduction and challenges

Multiple paths

B

A

Introduction and challenges

Routing Technique

Proactive(e.g., OLSR)

Reactive(e.g., AODV)

Single path(e.g., AODV)

Multiple paths(e.g., AOMDV)

Cooperative path diversity

(BSP, LBSP, LOSP, LMOSP)

Cooperative path diversity

BA

Introduction and challenges

Cooperative path diversity

BA

One possible path

Introduction and challenges

Cooperative path diversity

BA

Another possible path

Introduction and challenges

Cooperative path diversity

B

Many possible paths

A

Introduction and challenges

Cooperative path diversity

B

Best path

A

Introduction and challenges

Cooperative path diversity

Nodes are moving

Link quality varies

Best path varies

Path quality varies

Introduction and challenges

Introduction and challenges

Challenges How to define the path quality

based on channel conditions? How to monitor the time-varying

path quality to determine the best path cooperatively?

Overview

Cooperative path diversity

(BSP, LBSP, LOSP, LMOSP)

Aggressivepath qualitymonitoring

(BSP)

Efficientpath qualitymonitoring

(LBSP)

Introduction and challenges

Opportunisticforwarding with

path qualities(LOSP, LMOSP)

Introduction and challenges Aggressive path quality monitoring

BSP Efficient path quality monitoring

LBSP Opportunistic forwarding

LBSP2, LOSP, LMOSP Conclusion and future work

Outline

Aggressive path quality monitoring

Objectives Define path quality Monitor path quality aggressively/ideally to

investigate maximally possible benefits offered by path diversity routing

Protocol proposed : BSP (best-select protocol)

Path quality Depends on channel conditions

(e.g., channel loss, SNR, transmit power)

Aggressive path quality monitoring

Depends on protocol designer’s routing objectives Maximize the minimum SNR along the path

(max-min SNR) Maximize delivery probability Maximize throughput Minimize end-to-end delay Minimize total power Minimize total energy

Dynamic programming Achieves routing objectives

Jn,i = cost-to-go from node (n,i) to destination

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

Jn,i = cost-to-go from node (n,i) to destination

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

src dst

Jn,i = cost-to-go from node (n,i) to destination

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

src dst

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

30

20

J(1,1) = 30

J(1,2) = 20

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

30

20

J(1,1) = 30

J(1,2) = 20

J(2,1) = 2020

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

30

20

J(1,1) = 30

J(1,2) = 20

J(2,1) = 20

10

J(2,1) = 10

20

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

30

20

J(2,1) = 20

10

20

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

30

20

J(2,1) = 20

10

20

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

0,1

1,1

1,2

2,1

2,2

3,1

Jn,i = cost-to-go from node (n,i) to destination

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

Jn,i = cost-to-go from node (n,i) to destination

Jn,i Sn,in 1,, Jn 1,

Previous step’s cost-to-goStage information

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

Jn,i = cost-to-go from node (n,i) to destination

Jn,i Sn,in 1,, Jn 1,

0,1

1,1

1,2

2,1

2,2

3,1

J(1,1) = 30

J(1,2) = 2010

20

Sn,in 1,

Aggressive path quality monitoring

Dynamic programming Achieves routing objectives

Jn,i = cost-to-go from node (n,i) to destination

Jn,i Sn,in 1,, Jn 1,

0,1

1,1

1,2

2,1

2,2

3,1

J(1,1) = 30

J(1,2) = 2010

20

Jn 1,

Aggressive path quality monitoring

Max-min SNR

Jn,i Sn,in 1,, Jn 1,

maxn 1,j

minSNRn,in 1,j

, Jn 1,j

Aggressive path quality monitoring

Max delivery probability

Jn,i fSNRn,in 1,In11 , B Jn 1,In11

1 fSNRn,in 1,In11 , B fSNRn,i

n 1,In12 , BJn 1,In12

. . .

Aggressive path quality monitoring

Max delivery probability

Jn,i fSNRn,in 1,In11 , B Jn 1,In11

1 fSNRn,in 1,In11 , B fSNRn,i

n 1,In12 , BJn 1,In12

. . .

Aggressive path quality monitoring

Jn 1,1 0. 8

Jn 1,2 0. 9

Jn 1,3 0. 7

In 11 2

In 12 1

In 13 3

Max delivery probability

Jn,i fSNRn,in 1,In11 , B Jn 1,In11

1 fSNRn,in 1,In11 , B fSNRn,i

n 1,In12 , BJn 1,In12

. . .

Aggressive path quality monitoring

n,i

n-1,In-1(1)

n-1,In-1(2)

n-1,In-1(3)

Max delivery probability

Jn,i fSNRn,in 1,In11 , B Jn 1,In11

1 fSNRn,in 1,In11 , B fSNRn,i

n 1,In12 , BJn 1,In12

. . .

Aggressive path quality monitoring

n,i

n-1,In-1(1)

n-1,In-1(2)

n-1,In-1(3)

Max delivery probability

Jn,i fSNRn,in 1,In11 , B Jn 1,In11

1 fSNRn,in 1,In11 , B fSNRn,i

n 1,In12 , BJn 1,In12

. . .

Aggressive path quality monitoring

n-1,In-1(1)

n-1,In-1(2)

n-1,In-1(3)

n,i

Max throughput

Jn,i maxjmaxB|fSNR n,in1,j

,B PROB_THRESH minB, Jn 1,j

Aggressive path quality monitoring

Min end-to-end delay

Jn,i minB Jn,iB

Aggressive path quality monitoring

Jn,iB packet sizeB

Psucc Dn 1 T P fail

Psucc fSNRn,in 1,In11 , B

1 fSNRn,in 1,In11 , BfSNRn,i

n 1,In12 , B

Dn 1 fSNRn,in 1,In11 , BJn 1,In11

1 fSNRn,in 1,In11 , BfSNRn,i

n 1,In12 , BJn 1,In12

P fail 1 fSNRn,in 1,In11 , B1 fSNRn,i

n 1,In12 , B

#

Min total power

Jn,i minn 1,j 10SNR_THRESH Hn,i

n1,jN

10 Jn 1,j

Aggressive path quality monitoring

Min total energy

Jn,iB, PTn,i 10PTn,i

10 packet size

B Psucc Dn 1 T P fail

Aggressive path quality monitoring

Jn,i minB,PTn,i Jn,iB, PTn,i

Construction of relay-sets

0,1

1,1

1,2

2,1

2,2

3,1

AODV finds a traditional single path

Aggressive path quality monitoring

Construction of relay-sets

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Aggressive path quality monitoring

Construction of relay-sets

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Aggressive path quality monitoring

Construction of relay-sets

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Aggressive path quality monitoring

Construction of relay-sets

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

CIEREQ (channel info exchange request)CIEREP (channel info exchange reply)

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

CIEREQ

CIEREQ

: data frame

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

CIEREP

CIEREP

J2,1 , H2,1 3,1

J2,2 , H2,2 3,1

Path qualities between relay-set 3 and 2 are monitored

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

data

data

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

CIEREQ

CIEREQ

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

CIEREP

CIEREP

J1,1 , H1,1 2,1 , H1,1

2,2

J1,2 , H1,2 2,1 , H1,2

2,2

Path qualities between relay-set 2 and 1 are monitored

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

J2,1 J1,1 , J1,2 , H1,1 2,1 , H1,2

2,1

J2,2 J1,1 , J1,2 , H1,1 2,2 , H1,2

2,2 J2,1 J2,2

Assume that

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

data

data

Path qualities are monitored every packet transmission

Aggressive path quality monitoring

Path quality monitoring

0,1

1,1

1,2

2,1

2,2

3,1

Aggressive path quality monitoring

Simulation UDelModels :

Urban city, mobility, channel models Numerical analysis

Ideally construct relay-sets and receive CIEREQ/CIEREP Packet level simulation

QualNet network simulator CBR traffic (1024 bytes per second)

Comparison between J

single and J diversity

J single : source’s J along the single path found initially J diversity : source’s J along the best path among all paths

Aggressive path quality monitoring

Results : benefits of path diversity

Aggressive path quality monitoring

2 4 6 8 100

5

10

15

20

25

30 SparseDense

2

4

6

8

10

2 4 6 8 100

SparseDense

2 4 6 8 100

5

10

15SparseDense

2 4 6 8 100

5

10

15 SparseDense

2 4 6 8 10100

101

102

103

104

SparseDense

2 4 6 8 10100

101

102

103

SparseDense

Max delivery prob. Max throughput

J di

vers

ity /

J si

ngle

Max-min SNR

J di

vers

ity —

J si

ngle

J di

vers

ity /

J si

ngle

Min power Min energyMin delay

J di

vers

ity /

J si

ngle

J di

vers

ity /

J si

ngle

J di

vers

ity /

J si

ngle

Results : path selection differences

Aggressive path quality monitoring

2 4 6 8 100

0.2

0.4

0.6

0.8

1

Fra

ctio

n o

f re

lays

sh

ared

Minimum relay-set size

max-min SNRmax throughput

min total power min energy

min end-to-end delay max delivery probability

vs.

vs.vs.

Introduction and challenges Aggressive path quality monitoring

BSP Efficient path quality monitoring

LBSP Opportunistic forwarding

LBSP2, LOSP, LMOSP Conclusion and future work

Outline

Efficient path quality monitoring

Objectives Monitor path quality efficiently to

reduce overhead J broadcast, J-test, power control

Robust routing function Automatic path stretching and shrinking

Protocol proposed : LBSP (local BSP)

Path quality monitoring : J broadcast

0,1

1,1

1,2

2,1

2,2

3,1

JBC (J broadcast)

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Efficient path quality monitoring

Path quality monitoring : J broadcast

0,1

1,1

1,2

2,1

2,2

3,1

Efficient path quality monitoring

J0,10,1

Path quality monitoring : J broadcast

0,1

1,1

1,2

2,1

2,2

3,1

Efficient path quality monitoring

JBC

JBC

J0,11,1 minSNR1.1

0,1, J0,10,1

J0,11,2 minSNR1.2

0,1, J0,10,1

Path qualities between relay-set 1 and 0 are monitored

Path quality monitoring : J broadcast

0,1

1,1

1,2

2,1

2,2

3,1

Efficient path quality monitoring

JBC

JBC

J0,12,1 max1,j) min SNR2,1

1,j, J0,1

1,j

J0,12,2 max1,j) min SNR2,2

1,j, J0,1

1,j

Path qualities between relay-set 2 and 1 are monitored

Path quality monitoring : J broadcast

0,1

1,1

1,2

2,1

2,2

3,1

Efficient path quality monitoring

JBC

JBC

J0,13,1 max2,j) min SNR3,1

2,j, J0,1

2,j

Path qualities between relay-set 3 and 2 are monitored

Path quality monitoring : J broadcast

0,1

1,1

1,2

2,1

2,2

3,1

Efficient path quality monitoring

: best path

Next hop best node

arg maxn 1,j) min SNRn,1 n 1,j, J0,1

n 1,j

Path quality monitoring : J broadcast When this J-broadcast occurs?

When current best path quality degradation is experienced.

Efficient path quality monitoring

Jsrcdst Jsrc

dst Jthreshold or Jsrcdst Jmin

0,1

1,22,2

3,1

src dst

Jsrcsrc

Jsrc2,2 Jsrc

1,2Jsrc

dst

Path quality monitoring : J broadcast When this J-broadcast occurs?

When current best path quality degradation is experienced.

Efficient path quality monitoring

Jsrcdst Jsrc

dst Jthreshold or Jsrcdst Jmin

Reference path quality for the first data frame

Path quality for the subsequent data frame

Efficient path quality monitoring

Path quality monitoring : J-test

Jsrcdst Jsrc

dst Jthreshold or Jsrcdst Jmin

n-1,1

n-1,3

n-1,2

JBC

n,iJBCJBC

Jdstn,i maxn 1,j min SNRn,i

n 1,j, Jdst

n 1,j

Efficient path quality monitoring

Path quality monitoring : J-test

Jsrcdst Jsrc

dst Jthreshold or Jsrcdst Jmin

n-1,1

n-1,3

n-1,2n,i

Jdstn,i Jsrc

dstIf ,

Efficient path quality monitoring

Path quality monitoring : J-test

Jsrcdst Jsrc

dst Jthreshold or Jsrcdst Jmin

n-1,1

n-1,3

n-1,2

broadcast JBC

JBCrelay-set (n+1)

Avoid broadcasting lower path quality than Jsrcdst

n,i

Efficient path quality monitoring

Path quality monitoring : power control

Efficient path quality advertisement

Higher path qualityLower path quality

Higher powerLower power

n,1

n,3

n,2n+1,i

PRn 1,in,1 PRn 1,i

n,2 PRn 1,in,3

Efficient path quality monitoring

Path quality monitoring : power control

Efficient path quality advertisement

Higher path qualityLower path quality

Higher powerLower power

Exploit the “near-far” problem

Efficient path quality monitoring

Path quality monitoring : power control

PTn,i min MAX_POWER, Jdstn,i Jsrc

dst TARGET_POWER

Efficient path quality monitoring

Path quality monitoring : power control

PTn,i min MAX_POWER, Jdstn,i Jsrc

dst TARGET_POWER

n,1

n,3

n,2n+1,i

PTn,1

PTn,2

PTn,3

17dBm

12dBm

10dBm

Jdstn,1 Jdst

n,2 Jdstn,3

Efficient path quality monitoring

Path quality monitoring : power control

PTn,i min MAX_POWER, Jdstn,i Jsrc

dst TARGET_POWER

n,1

n,3

n,2n+1,i

PTn,1

PTn,2

PTn,3

17dBm

12dBm

10dBm

Jdstn,1 Jdst

n,2 Jdstn,3

JBC

JBC

JBC

Efficient path quality monitoring

Automatic path stretching and shrinking Stretching

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

A

: Current active best path

Efficient path quality monitoring

Automatic path stretching and shrinking Stretching

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

A

Efficient path quality monitoring

Automatic path stretching and shrinking Stretching

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

A

Efficient path quality monitoring

Automatic path stretching and shrinking Stretching

0,1

1,1

1,2

2,1

2,2

4,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

3,1

Relay-set 4

Efficient path quality monitoring

Automatic path stretching and shrinking Shrinking

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

: Current active best path

Efficient path quality monitoring

Automatic path stretching and shrinking Shrinking

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Efficient path quality monitoring

Automatic path stretching and shrinking Shrinking

0,1

1,1

1,2

2,1

2,2

3,1

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Efficient path quality monitoring

Automatic path stretching and shrinking Shrinking

0,1

1,1

1,2

2,1

2,2

2,3

Relay-set 3 Relay-set 2 Relay-set 1 Relay-set 0

Efficient path quality monitoring

Numerical analysis : setting

source destination

50m 100m 100m 50m

relay-set 2 relay-set 1relay-set 3 relay-set 0

Efficient path quality monitoring

Numerical analysis : results

25 nodes per relay-set

20 nodes per relay-set

15 nodes per relay-set

10 nodes per relay-set

5 nodes per relay-set

0 10 20 30 40 50 60 703.5

4

4.5

5

5.5

6

6.5

7

7.5

8

Solid lineDashed line

: optimal: LBSP

Chips per symbol

Impr

ovem

ent

in S

NR

(dB

)

(J di

vers

ity —

J si

ngle)

Efficient path quality monitoring

Numerical analysis : results

Chips per symbol

Impr

ovem

ent

in S

NR

(dB

)

no power control and no J-test

no power control but with J-test

with power control but no J-test

with power control and J-test

0 10 20 30 40 50 60 700

1

2

3

4

5

6

7

8

Efficient path quality monitoring

Numerical analysis : results

Chips per symbol

Impr

ovem

ent

in S

NR

(dB

)

0 10 20 30 40 50 60 704.5

5

5.5

6

6.5

7

7.5

MAX_POWER – TARGET_POWER

5 dB7 dB

10 dB15 dB*20 dB

Efficient path quality monitoring Packet level simulation : setting

UDelModels : Urban city, mobility, channel models

Simulator : QualNet CBR traffic (1024 bytes per 50 ms for 5 min)

Indoor mobility Outdoor mobility

Scenario number # of nodes Scenario number # of nodes

1 64 1 64

2 128 2 128

3 256 3 256

4 512

5 1024

Efficient path quality monitoring Packet simulation : results

Pa

cke

t d

eliv

ery

rat

io

1 2 3 4 596

96.5

97

97.5

98

98.5

99

99.5

100

AODVAOMDVLBSP

confidence interval

# o

f n

ew

ro

ute

sea

rch

es

1 2 3 4 50

10

20

30

40

50

60

70

AODVAOMDVLBSP

En

d-t

o-e

nd

de

lay

(m

s)

1 2 3 4 55

1015202530354045

AODVAOMDVLBSP

J di

vers

ity —

J si

ngl

e

1 2 3 4 5-0.50

0.51

1.52

2.53

3.5

AODVAOMDV

Introduction and challenges Aggressive path quality monitoring

BSP Efficient path quality monitoring

LBSP Opportunistic forwarding

LBSP2, LOSP, LMOSP Conclusion and future work

Outline

Opportunistic forwarding Objectives

Compare opportunistic forwarding (OF) and deterministic forwarding (DF) to see if path diversity is better exploited by OF or DF. Without node mobility With node mobility

Protocol proposed LOSP (local opportunistic-select protocol) LMOSP (local monitoring-added OSP)

Opportunistic forwarding

How it works?

IN1

IN2T

IN3

: data frame

: transmitter: intended node

TIN

Opportunistic forwarding

How it works?

IN1

IN2T

IN3

Opportunistic forwarding

How it works?

IN1

IN2T

IN3

Priority : IN1 > IN2 > IN3

Opportunistic forwarding

Agreement

IN1

IN2T

IN3

ACK

: overhearing

Opportunistic forwarding

Agreement

IN1

IN2T

IN3

ACKACK

: overhearing

Opportunistic forwarding

Agreement

IN1

IN2T

IN3

ACK

: overhearing

obstacle

Opportunistic forwarding

List of priority nodes

pn

tnT

bn

Preferred node

Target node

Backup node

Priority : pn > tn > bnLPN = {pn, tn, bn}

Opportunistic forwarding

List of priority nodes

T

Jdstpn Jdst

tn , SNRpnT SNRtn

T

Jdstbn Jdst

tn , SNRbnT SNRtn

T

pn

tn

bn

Preferred node

Backup node

Target node

Opportunistic forwarding

Sequence of nodes Deterministic forwarding

(src, tn, tn, tn, tn, dst) Opportunistic forwarding

(src, tn, pn, pn, tn, dst) (src, tn, bn, pn, tn, dst) …

Opportunistic forwarding

Bit-rate

bit-rateDF maxB

such that

fSNRTtn

, B THRESH

#

bit-rateOF maxB

such that

1 k pn or tn

1 fSNRTk , B THRESH

#

bit-rateDF bit-rateOF

Opportunistic forwarding

Protocols to be compared Deterministic forwarding

LBSP (local best-select protocol) Efficient path quality monitoring, automatic path stretching and sh

rinking, route recovery

Opportunistic forwarding LOSP (local opportunistic-select protocol)

One time J broadcast to construct LPN for each route failure, no route recovery

Opportunistic forwarding with the path quality degradation detection LMOSP (local monitoring-added OSP)

Path quality monitoring is added like LBSP, mixture of OF and DF

Opportunistic forwarding

Sequence of nodes Deterministic forwarding

(src, tn, tn, tn, tn, dst) Opportunistic forwarding

(src, tn, pn, pn, tn, dst) (src, tn, bn, pn, tn, dst) …

Opportunistic forwarding

Radio modelP

acke

t er

ror

pro

bab

ilit

y

nominal

steepsteepest

shallowest

shallower

shallow

SNR (dB)-5 0 5 10 15 20 25

10-4

10-3

10-2

10-1

100

2 Mbps

Opportunistic forwarding Packet level simulation : setting

UDelModels : Urban city, mobility, channel models

Simulator : QualNet CBR traffic (512bytes per 50 ms for 5 min)

Outdoor mobility

Scenario number # of nodes

1 64

2 128

3 256

4 512

5 1024

Opportunistic forwarding

Results : performance of the first data packet

1 2 3 4 51.5

2

2.5

3

3.5

4LBSPv2 LOSP LMOSP

nominalsteepsteepest

shallowershallow shallowest

1 2 3 4 51.5

2

2.5

3

3.5

4

1 2 3 4 51.5

2

2.5

3

3.5

4

scenario number

bit-

rate

(M

bps)

(no node mobility)

Opportunistic forwarding

Results : performance of the first data packet

nominalsteepsteepest

shallowershallow shallowest

1 2 3 4 522

23

24

25

26

27

scenario number1 2 3 4 5

22

23

24

25

26

27

1 2 3 4 522

23

24

25

26

27

SN

R (

dB)

LBSPv2 LOSP LMOSP

(no node mobility)

Opportunistic forwarding

Results : performance before the first route failure (node mobility involved)

nominalsteepsteepest

shallowershallow shallowest

scenario number

LBSPv2 LOSP LMOSP

1 2 3 4 51.5

2

2.5

3

3.5

1 2 3 4 51.5

2

2.5

3

3.5

bit-

rate

(M

bps)

1 2 3 4 51.5

2

2.5

3

3.5

Opportunistic forwarding

Results : performance before the first route failure (node mobility involved)

nominalsteepsteepest

shallowershallow shallowest

scenario number

LBSPv2 LOSP LMOSP

SN

R (

dB)

1 2 3 4 522

24

26

28

30

1 2 3 4 522

24

26

28

30

1 2 3 4 522

24

26

28

30

Opportunistic forwarding

Results : performance during the connection lifetime

nominalsteepsteepest

shallowershallow shallowest

scenario number

LBSPv2 LOSP LMOSP

pack

et d

eliv

ery

ratio

1 2 3 4 50.985

0.99

0.995

1

1 2 3 4 50.985

0.99

0.995

1

1 2 3 4 50.985

0.99

0.995

1

Opportunistic forwarding

Results : performance during the connection lifetime

nominalsteepsteepest

shallowershallow shallowest

LBSPv2 LOSP LMOSP

Rou

te f

ailu

re r

ate

scenario number1 2 3 4 5

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3 4 50

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3 4 50

0.05

0.1

0.15

0.2

0.25

0.3

Opportunistic forwarding

Results : performance during the connection lifetime

nominalsteepsteepest

shallowershallow shallowest

LBSPv2 LOSP LMOSP

Eff

icie

ncy

scenario number1 2 3 4 5

0.88

0.9

0.92

0.94

0.96

0.98

1

1 2 3 4 5

0.88

0.9

0.92

0.94

0.96

0.98

1

1 2 3 4 5

0.88

0.9

0.92

0.94

0.96

0.98

1

duration that user data packets are transmittedduration that any packet including overhead is transmittedEfficiency =

Opportunistic forwarding

Conclusion Without mobility (e.g., stationary mesh network)

Opportunistic forwarding is preferred except for the overhead

With mobility Deterministic forwarding is preferred Path diversity is better exploited by deterministic

forwarding

Introduction and challenges Aggressive path quality monitoring

BSP Efficient path quality monitoring

LBSP Opportunistic forwarding

LBSP2, LOSP, LMOSP Conclusion and future work

Outline

Conclusions The significant benefits of path diversity are

possible using aggressive path quality monitoring.

Reducing overhead and advertising path quality efficiently are possible using the proposed novel techniques, still maintaining high benefits.

Path diversity is better exploited by deterministic forwarding with node mobility.

Conclusions and future work

Future work Estimate the dynamics of channel by observing

ongoing channel activity. Achieve fast estimation of link/path qualities

from the channel dynamic estimation. i.e., given the estimated current channel state,

estimate link/path qualities. Develop models of channel evolution.

Conclusions and future work

Schedule

Conclusions and future work

Date Task11/11/2011 11/20/2011∼ Packet level simulation11/21/2011 12/31/2011∼ Real channel measurement12/16/2011 12/31/2011∼ Develop models of channel

evolution01/01/2012 01/15/2012∼ Writing up all findings12/01/2011 01/31/2012∼ Proofreading the whole

thesis

Thanks