cross-layer network planning and performance optimization algorithms for wireless networks

Cross-Layer Network Planning and Performance Optimization

Algorithms for Wireless Networks

Yean-Fu WenAdvisor: Frank Yeong-Sung Lin

Department of Information Management,National Taiwan University

2007/4/24

Dissertation Proposal

2

Agenda

Introduction Wi-Fi Hotspots (Ch. 2)

System Throughput Maximization Subject to Time Fairness Constraints

Wireless Mesh Networks (Ch. 3 and Ch. 4) Fair Throughput and End-to-End Delay with Resource Allocation Fair Inter-TAP Routing and Backhaul Assignment Fair End-to-End Delay and Load-Balanced Routing

Ad Hoc Networks (Ch. 5) A Minimum Power Broadcast (or Multicast)

Wireless Sensor Networks (Ch. 6 and Ch. 7) Dynamic Radius, Duty Cycle Scheduling, Routing, Data Aggrega

tion, and Multi-Sink (Clusters)

Conclusion & Future Work

Agenda Ch.2Introduction Conclusion Ch. 3 Ch. 4 Ch.3 Ch.5 Ch.6,7

3

Background

Wireless networks are the key to improving person-to-person communications, person-to-machine communications, and machine-to-machine communications.

The research scope of this dissertation covers various network architectures, and various protocol layers

[Ref: B3G Planning]


4

AP5

MDE

Wire Network (Fiber, T3…etc.)

AP1

AP2

AP4

AP6

AP8

AP7

MDA

MDCMDB

MDD

Ad Hoc, Sensor or Hybrid Networks BS-oriented

Mesh Networks

Fairness model

PHY layer

MAC layer

Network

PHY layer

MAC layer

ApplicationPHY layer

MAC layer

Network

AP9

AP3


5

Motivation

Fairness to ensure the allocated resources are sufficient for all MDs

to achieve equivalent throughput or channel access time, and minimize end-to-end delay

to distribute and balance the traffic load or on related links to solve fairness issues due to spatial bias or energy

constraints in three networks with different structures Multi-range

causes different levels of energy consumption causes different bit-rates (capacity)

Multi-rate causes performance anomalies [Heusse’03]


6

Motivation

Multi-hop causes throughput and end-to-end delay fairness issues in WMNs causes inefficient energy usage in WSNs

Multicast reduce the number of duplicate packets in order to gain a “multicast

wireless advantage” and thereby reach multiple relay nodes reduce the number of duplicate packets in data-centric WSNs

Multi-channel vs. Multi-access whether to use multi-channel to reduce the number of collisions

Multi-sink in WMNs, find a TAP trade-off in routing to a backhaul via a shorter

path or routing to light-load links and a backhaul in WSNs, find a source sensor trade-off between the shortest relay

node or the sink node and the in-network process to reduce energy consumption


7

Objective

How to achieve throughput and channel access time fairness.

How to fairly allocate resources to solve the spatial bias problem in single hop or multi-hop wireless networks.

How to fairly distribute the traffic load among relay nodes to reduce end-to-end delay and among sensor nodes to increase the sensor network’s lifetime.


8

Research Approaches

Discrete event simulation [Harrell’92] NS2 [Fall’99]

Analytic heuristic modeling [Harrell’92] MATLAB Lagrangean Relaxation (LR) [Ahuja’93] [Fisher’81]

0

10

20

30

40

50

60

70

1 401 801 1201 1601 2001 2401

The number of iterations

Pow

er c

onsu

mpt

ion

UB

LB

PMST

=2

=1=0.5=0.25 =0.125 …


9

Agenda




Ad Hoc Networks (Ch. 5) A Minimum Power Broadcast Algorithm




Publication List[1] Y.F. Wen, Frank Y.S. Lin, and K.W. Lai, "System Throughput Maxi

mization Subject to Delay and Time Fairness Constraints in 802.11 WLANs," in Proc. of IEEE ICPADS, Fukuoka Institute of Technology (FIT), Fukuoka, Japan, Jul. 2005. (EI)

[2] Yu-Liang Kuo, Kun-Wai. Lai, Frank Yeong-Sung Lin, Yean-Fu Wen, Eric Hsiao-kuang Wu, and Gen-Huey Chen, "Multi-Rate Throughput Optimization with Fairness Constraints in Wireless Local Area Networks," IEEE Transactions on Vehicular Technology, Dec. 2006 (major revised).


10

System Throughput Maximization Subject to Time Fairness Constraints in WLANs

We discuss how to achieve a trade-off between throughput fairness and channel access time fairness in 802.11 WLANs.

Problem multiple bit-rates cause performance anomalies [Heusse’03].

tF Slow MD

Ts TsTfTf

F Slow MD

Throughput fairness vs. channel access time fairness

Given:


11


Objective: to maximize system

throughput. Subject to:

initial contention window size;

packet size; multiple back-to-back pack

ets; maximum cycle time (dela

y)

time fairness

To determine: the initial contention windo

w size for each bit rate class,

the packet size for each bit rate class,

the number of multiple back-to-back packets of class-k Bk in a block within one transmission cycle

t

data ACK

SIFST(N)

DIFS

backoff time

SLOT

21

max K

IP kk

Z

(1 ) 1TFI

,1k k K

min max ,1k k KL L L min max ,1k k KW W W

max ,11 k k KB B

kL

kW

a great deal of computing time & non-

convex problem

1k k k

k

KB L W


12


Proposed algorithm Modified binary search (Unimodal curve interval based on f

airness index constraints [Jain’84] ) Theorem: If the time value x is deducted from a class-k M

H, and it does not change any other class-j MHs, then the fairness

increases iff x < xk – xj.

remains the same iff x = xk – xj.

decreases iff x > xk – xj.


13


Experiment results although the problem has been shown to be NP-complete

[Kuo’05], our numerical results reveal a simple unimodal feature

the relation between three MAC layer parameters (i.e., the initial contention window, packet size, and multiple back-to-back packets) and fairness achieves access time near-fairness and maximizes the system throughput with a simultaneous delay bound [Wen’06c].

21% improvement in system throughput over the original MAC protocol.


14


Related work “performance anomaly” [Heusse’03] (Grenoble) 802.11 system throughput analysis [Bianchi’00] performance analysis under a finite load and improvement

s for multirate 802.11b [Cantieni’05] (Brunel) to discuss the issues of cycle time (delay) [Wang’03], [Wu’

02], [Chatzimisios’03], and [Raptis’05] Jain’s Fairness Index (FI) model [Jain’84] integer programming [Lai’04] [Kuo’05] (NTU) an uplink solution with packet size or burst packets [Tan’04

a] [Tan’04b] (MIT) simulate a high quality signal with multiple back-to-back pa

ckets [Sadeghi’02] (Rice) [Sheu’02] (NCU)


15

Agenda








Publication List[1] Yean-Fu Wen and Frank Yeong-Sung Lin, "Fair Bandwidth Allocati

on and End-to-End Delay Routing Algorithms in Wireless Mesh Networks," Communications, IEICE Transactions on, E90-B(5), pp. xx–xx, May 2007. (SCI, EI)


16

Fair Throughput and End-to-End Delay with Resource Allocation for WMNs

We discuss the scenario where many clients use the same backhaul to access the Internet. Consequently, throughput depends on each client’s distance from the gateway node. [Karrer’04] [Gambiroza’04] Given:


17


Objective: to minimize the maximal

end-to-end delay of the WMN.

Subject to: capacity

delay

To determine: the resources cs(u,v) that sho

uld be allocated to the selected links of a TAP node s.

the end-to-end delay on the selected path of a TAP node.

the maximum end-to-end delay d of the WMN.

3 min IPZ d

( , ) ( , ) , ( , )s u v u v ss S

c C u v L

( , ) ( , )( , )

( , ) ,s

u v s s u vu v L

D c d s S

( , ) , ( , )s s u v sc u v L


18


Lemma 3-1: fair end-to-end delay is achievable monotonic increases in f(u,v)

the delay time approaches ∞, when f(u,v) C(u,v) the delay function is a convex function

020406080

100120140160180200

0.8

0.82

0.84

0.86

0.88 0.9

0.92

0.94

0.96

0.98

Traffic load / Link capacity

The

del

ay ti

me

Delay time

3 4

2

s1,1

s2,2 Link (2,3)

Link (3,4)

1 Link (1,3)


19


Experiment results

0

5

10

15

20

25

50 70 90 110

130

150

170

190

The number of TAP nodes

Nor

mal

ized

end

-to-e

nd d

elay

Extended delay fairness scheme (by EDTB)Spatial bias fairness schemeAverage capacity scheme


20


Related work wireless mesh networks: a survey [Akyildiz’05] (GIT) describe 10 challenging issues [Karrer’04] (Rice university) spatial bias fairness & temporal fairness [Gambiroza’04]

(Rice university) average delay, end-to-end delay routing and capacity

assignment for virtual circuit networks [Cheng’95] [Yen’01] (NTU)

to maximize spatial reuse of a spectrum by maintaining basic fairness among contending flows [Li’05] (Toronto)

hierarchically aggregated fair queuing (HAFQ) for per-flow fair bandwidth allocation [Maki’06] (Osaka)


21

Agenda








Publication List[1] Frank Yeong-Sung Lin and Yean-Fu Wen, "Fair Inter-TAP Routing a

nd Backhaul Assignment in Wireless Mesh Networks," was submitted to Journal of WCMC, Oct. 2006. (under review)

[2] Y.F. Wen and Frank Y.S. Lin, "The Top Load Balancing Forest Routing in Mesh Networks," in Proc. of IEEE CCNC, Las Vegas, NV, Jan. 2006. (EI)


22

Fair Inter-TAP Routing and Backhaul Assignment Algorithms for WMNs

How to cluster backbone mesh networks efficiently so that the load-balanced routing is concentrated on given and “to-be-determined” backhauls.

Problem

backhaul

TAP

link

Given:


23


Objective: to minimize the sum of the

aggregated flows of selected links

Subject to: budget backhaul assignment B backhaul selection routing Psb

link L, p(u,v), Hs

capacity Cuv

load balancing ’, ’’

To determine: which TAP should be selecte

d to be a backhaul b which backhaul should be se

lected for each TAP to transmit its data zsb.

The routing path from a TAP to a backhaul xp.

whether a link should be selected for the routing path y(u,v).

aggregated flow on top-level selected link f(u,b).

aggregated flow on each backhaul b.

a top-level load-balanced forest.


24


4 ( , )( , )

minIP u vu v L

Z f

1sb

b B

z

1sb

pb B p P

x

sb

sb pp P

z x

( , ) ( , )

sb

p p u v u vp P

x y

( , ) 1u vv V

y

( , ) ( , )

sb

p p u v s u vs V b B p P

x f

( , ) 0b vv V

y

( , )

( , )

| | | |u vu v L

y V B

( , )( , )sb

p p u v sb B p P u v L

x H

( , ) ( , )0 u v u vf C

( , )b u bu V

f

2( , )

2( , )

( )'

u bu V

b u bu V

f

E f

2

2

( )''

| |

bb B

bb B

B

sb bz

b bb B

s V

,s V b B

s V

s V

, , ( , )s V b B u v L

,s V b B

v V B

b B

b B

b B

( , )u v L

( , )u v L

(1)

(2)

(3)

(4)

(5)

(6)

(8)

(7)

(9)

(10)

(11)

(12)

(13)

(14)

(15)


BA+MCP=NP-

complete

25


Proposed algorithm weighted backhaul assignment (WBA) algorithm greedy load-balanced routing (GLBR) algorithm


26


Experiment results the load-balanced routing and backhaul assignment experi

ment results demonstrate that the GLBR plus WBA algorithms with the LR-based approach achieve a gap of 30% and outperform other algorithms by at least 10%

0

100

200

300

400

500

600

25 36 49 64 81 100 121 144Number of nodes

Nor

mal

ized

flow

s

GLBR-1 UB-1 LB-1GLBR-2 UB-2 LB-2GLBR-4 UB-4 LB-4

0

100

200

300

400

500

600

20 30 40 50 60 70 80 90 100

Number of nodes

Nor

mal

ized

flow

WBA LIDHD UBLB


27


Related work traditional AP assignment focuses on coverage of the servi

ce area [Tutschku’99], [Unbehaun’03], [Mathar’00], and [Fortune’95]

cluster-head assignment methods, such as max-min d-hop cluster [Amis’00], LCA [Baker’81]

multi-constrained path problem (MCP) is an NP-complete problem [Wang’96] (London)

a single sink to balance the traffic load on the incoming link of an egress node

a general tree structure [Hsiao’01] (Harvard) sensor networks [Dai’03] (Colorado)


28

Agenda






tion, and Multi-Sink (Clusters) Conclusion & Future Work

Publication List[1] Frank Yeong-Sung Lin and Yean-Fu Wen, "Fair Inter-TAP Routing and Bac

khaul Assignment in Wireless Mesh Networks," was submitted to Journal of WCMC, Oct. 2006. (under review)

[2] Yean-Fu Wen and Frank Yeong-Sung Lin, "Fair Bandwidth Allocation and End-to-End Delay Routing Algorithms in Wireless Mesh Networks," Communications, IEICE Transactions on, E90-B(5), pp. xx–xx, May 2007. (SCI, EI)


29

Fair End-to-End Delay and Load-Balanced Routing

How to cluster backbone mesh networks efficiently so that the load-balanced routing and fair end-to-end delay are concentrated on given backhauls.

Objective: to minimize the maximum

end-to-end delay

Subject to: routing Ps

link (tree or mesh) L, p(u,v)

resource allocation C(u,v)

delay (including end-to-end delay)

To determine: The routing path from a TAP to

a backhaul xp. whether a link should be select

ed for the routing path y(u,v). the resource that should be allo

cated to the selected links of a TAP node. cs(u,v)

the maximum end-to-end delay d of the WMN.


30


3 2 min IPZ d objective function

1,s

pp P

x

subject to:

( , ) ( , ) ,s

p p u v s u vp P

x y

( , ) ( , ) ,s u v u vs S

c C

( , ) ( , ) ,s s u v s u vy c

( , ) ( , ) ( , )( , )

( , ) ,s u v u v s s u vu v L

y D c d

s S

( , ) ( , ) ,s u v u vy y

( , ) 1,u vv V

y

( , ) 1,u b

u V

y

;( , )s S u v L u V

( , )u v L ; ( , )s S u v L

s S

( , ) 1s s vv V

y

( , ) 1s u v

v V

y

( , ) 1s u b

u V

y

s S

;s S u V s S

(3-2.1)

(IP3-2)

(3-2.2)

(3-2.3)

(3-2.4)

(3-2.5)

(3-2.6)

(3-2.7)

(3-2.8)

(3-3.1)

(IP3-3)

(3-3.2)

(3-3.3)

(3-3.4)

(3-3.5)

(3-3.6)

(3-3.7)

(3-3.8)

3 3 min IPZ d


;( , )s S u v L

Tree structureMesh structure

Steiner tree & Knapsack Problem

=NP-complete

31


Lagrangean Relaxation (LR3-2)

Lagrangean dual problem (D3-2)

1 2 33 2( , , ) min{LR suv uv sZ d

1( , ) ( , )

( , )

( )s

suv p p u v s u vs S u v L p P

x y

2( , ) ( , )

( , )uv s u v u v

u v L s S

c C

3( , ) ( , ) ( , )

( , )

( , )s s u v u v s s u vs S u v L

y D c d

subject to: (3-2.1), (3-2.3), (3-2.4), (3-2.5), and (3-2.7).

1 2 33 2 3 2max ( , , )D LR suv uv sZ Z

1 2 3, , 0suv uv s

objective function

subject to

(D3-2)

(LR3-2)


Tree structure

32


Lagrangean Relaxation (LR3-3)

Lagrangean dual problem (D3-3)

1 2 33 3( , , ) min{LR suv uv sZ d

1( , ) ( , )

( , )

( )s

suv p p u v s u vs S u v L p P

x y

2( , ) ( , )

( , )uv s u v u v

u v L s S

c C

subject to: (3-3.1), (3-3.3), (3-3.4), (3-3.5), and (3-3.7).

1 2 33 3 3 3max ( , , )D LR suv uv sZ Z

1 2 3, , 0suv uv s

(D3-3)

(LR3-3)

subject to

objective function

3( , ) ( , ) ( , )

( , )

( , )s s u v u v s s u vs S u v L

y D c d


Mesh structure

33


Sub-problem (SUB3-2.1) is related to decision variable xp.

13 2.1 ( , )

( , )

mins

SUB suv p p u vs S u v L p P

Z x

objective function

subject to (3-2.1)

Each sub-problem of OD-pair, xp, is a shortest path problem solved by considering the link weight

1,s

pp P

x

s S (3-2.1)

1( , )suv p u v

a top-level load-balanced problem

(SUB3-2.1)


34


Sub-problem , related to decision variable y(u,v), ys(u,v), and cs(u,v).

3 1 23 2.2 ( , ) ( , ) ( , ) ( , )

( , )

min ( , ) )SUB s u v s s u v suv s u v uv s u vu v L s S

Z D c y c

(SUB3-2.2)

-3

-2.5

-2

-1.5

-1

-0.5

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

objective function

subject to (3-2.3), (3-2.4), (3-2.5), and (3-2.7).

we consider the delay function is M/M/1:

( , ) ( , )( , )

1( , )u v s s u v

s u v s

D cc

; ( , )s S u v L

3 1 23 2.2 ( , ) ( , ) ( , ) ( , )

( , )

min ( , )SUB s u v s s u v suv uv s u v s u vu v L s S

Z D c c y

32 1

( , )( , )

)ssuv uv s u v suv

s u v s

cc

3 1 23 3.2 ( , ) ( , ) ( , ) ( , )

( , )

min ( , )SUB s u v s s u v suv uv s u v s u vs S u v L

Z D c c y

(SUB3-3.2)


subject to (3-3.3), (3-3.4), (3-3.5), and (3-3.7).

(SUB3-2.2)(SUB3-3.2)Mesh structureTree structure

35


Sub-problem , related to decision variable d.

32 3 min (1 )SUB s

s S

Z d

subject to the lower and upper bound of d.

objective function

Lemma 3-3: how to determine the upper bound and lower bound of d?

The lower bound is calculated by the fully distributed the traffic load, aggregated from the higher level around the level of each node. (BFS)

As described in LR approach, the getting primal feasible solution gets the upper bound of this problem.

(SUB3-2.3)(SUB3-3.3)


(SUB3-3.3)(SUB3-2.3)

33 3 min (1 )SUB s

s S

Z d

Mesh structureTree structure

36


Getting primal feasible solution the incoming link costs of the backhaul are set to delay fun

ction (u,v) = sS D(u,v)(C(u,v),s) * (a + ), where (u,v) L.

greedy load-balanced routing (GLBR) [Wen’06a] resource allocation scheme (EDTB) [Wen’07]

0

0.2

0.4

0.6

0.8

1

1.2

1 152 303 454 605 756 907 10581209 1360

The number of iterations

Nor

mal

ized

end

-to-e

nd d

elay UB LB


2 3uv s

0

2

4

6

8

10

12

14

16

The number of TAP nodes

Nor

mal

ized

end

-to-e

nd d

elay

SPA GLBR LRA (UB) LB

37

Agenda



Wireless Mesh Networks (Ch. 3 and Ch. 4) Fair Throughput and End-to-End Delay with Resource Allocation Fair Inter-TAP Routing and Backhaul Assignment Algorithms Fair End-to-End Delay and Load-balanced Routing





Publication List[1] Frank Yeong-Sung Lin and Yean-Fu Wen, "A Path-Based Minimum

Power Broadcast Algorithm in Wireless Networks," was submitted to ACM Baltzer Mobile Networks and Applications (MONET), Mar. 2007. (under review)

[2] Frank Y.S. Lin, Y. F. Wen, L.C. Fu, and S.P. Lin, “A Path-Based Minimum Power Broadcast Problem in Wireless Networks," in Proc. of IEEE TENCON, Melbourne, Australia, Nov. 2005. (EI)


38

A Minimum Power Broadcast Algorithm for Ad-hoc (Sensor) Networks

We discuss how to construct a multicast tree that minimizes power consumption with the “multicast wireless advantage”.

Problem

Given:

rv

ev(rv)

Power consumption

(normalized)

Power range

ev(rv)=rv + a

1

2

3

4

5

6

7

8

9

11

12

10

(1,2)

(1,3)


39


Objective: to minimize the total

broadcast power consumption

Subject to: routing

tree

radius

To determine: the routing path from each

source to the destination, denoted as an OD-pair xp.

whether a link should be on the multicast tree y(u,v) .

a multicast tree. transmission radius for ea

ch MD ru.

5 min ( )IP u uu V

Z e r T

1,s

pp P

x s S

( , ) ( , ) , ( , )s

p p u v u vp P

x y u v L

( , ) 1vv V

y

( , ) 1,u vu V

y v V

( , ) 1,u su V

y s S

( , ) ( , ) , ( , ) ,u v u v uy r u v L u V

a multicast tree which is also a Steiner tree

=NP-

complete


40


Proposed algorithm a minimum power broadcast algorithm

Experiment results

0

10

20

30

40

50

60

70

25 35 45 55 65 75 85 95 105

The number of nodes

Pow

er c

onsu

mption (norim

aliz

ed)

MSPT PMST GIBTBIP EWMA LR-UBLR-LB


41


Related work power range and topology control [Salhieh’01] [Bettstetter’02]

[Santi’01] to build minimum energy networks via the shortest path tree

(SPT) algorithm, measuring the cost of the edge by its power level [Salhieh’01], [Dowell’01], [Montemanni’04], and [Li’01]

node-based solutions in static all-wireless networks in terms of a trade-off: a node can reach more nodes in a single hop by using higher transmission power [Ahluwalia’05], [Wieselthier’00] and [Cagalj’02]

link-based solutions [Das’03a] a series of heuristics (e.g., BIP) to solve this problem

[Wieselthier’00], [Wieselthier’01], and [Wieselthier’02] r-shrink [Das’03b]


42

Agenda








Publication List[1] Frank Yeong-Sung Lin and Yean-Fu Wen, "Multi-sink Data Aggregation Ro

uting and Scheduling with Dynamic Radii in WSNs," IEEE Communications Letters, 10(10), pp. 692–694, Oct. 2006. (SCI, EI)

[2] Yean-Fu Wen and Frank Yeong-Sung. Lin, "Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing in Wireless Sensor Networks," Lecture Notes Computer Science (LNCS), vol. 4096, pp. 894–903. (Proceedings of IFIP EUC 2006) (SCI, EI)

[3]Y.F. Wen, Frank Y.S. Lin, and W.C. Kuo, "A Tree-based Energy-efficient Algorithm for Data-Centric Wireless Sensor Networks," in Proc. of IEEE AINA, Ontario, Canada, May 2007. (EI)

[4]Y.F. Wen and Frank Y.S. Lin, "Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based Sensor Networks," in Proc. of IEEE CCNC, Las Vegas, NV, Jan. 2007. (EI)


43

Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs

We discuss how to increase the battery lifetime and energy consumption efficiency of a network from the Physical layer to the Application layer in terms of the following issues: single/multi-sink data aggregation tree structure routing duty-cycle scheduling node-to-node communication time the number of retransmissions dynamically adjusted radius Physical layer

Application layer

MAC layer

Network layer


44


Objective: minimize the total energy c

onsumed by a target transmission to one of sink nodes

Subject to: sink selection D restrictions on the structure

of trees in the form of three link constraints PsD, L, p(u,v), Hs

duty cycle scheduling the time for node-to-node c

ommunication [Shiou’05] dynamic radius uv, Ru

To determine: The sink node that a source

node bsg will route to; a routing path xp and link y(u,v)

from the source node to the sink node;

the earliest time nu at which a node wakes up and begins aggregating data; and

the time mu at which aggregation of sub-tree data will be completed;

the time uv needed for a successful node-to-node transmission.

the power range ru of each node;

a type of reverse-multicast tree which is

also a Steiner tree

MCP=

NP-complete


45


S4

13

1

1

2

2

3

D κ

S1 S2 S3

[0, 0+1][0, 0+3]

[0, 3+1]

[3, 4+1]

[3, 5+0]

[0, 3+2]

[0, 0+2][0, 0+3]

2

34

7

8

65

O

Proposed algorithm: single sink

00

00

0

0

0

0

∞ ∞∞

0

∞


46

Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling for Multi-Sink WSNs

Proposed algorithm: multi-sink We discuss how to increase the lifetime of the networks

already discussed with a multiple sink structure (outgoing information gateways)


47


Experiment results: single sink

Maximum Communication Radius = 2.5

0

10

20

30

40

50

60

70

80

10 20 30 40 50 60 70 80 90 100The number of sensors

Ene

rgy

cons

umpt

ion

LRA SPT GIT CNS

The number of nodes = 250The number of source nodes = 90

30

35

40

45

50

55

60

65

70

1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4

The power range

Ene

rgy

cons

umpt

ion

LRA SPT GIT CNS

The number of source nodes = 90Maximum power range = 2.5

30

35

40

45

50

55

60

65

70

75

100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

The number of sensor nodes

Ene

rgy

cons

umpt

ion

LRA SPT GIT CNS0

20

40

60

80

100

120

140

LRA SPT GIT CNSThe data aggregation routing algorithms

Ener

gy c

onsu

mption O-MAC T-MAC S-MAC


48

Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling for Multi-Sink WSNs

Experiment results: multi-sink

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10The number of sink nodes

Ener

gy c

onsu

mpt

ion

MDAR GIT CNS SPTO-MAC S-MAC T-MAC


49


Related work backhaul selection [Wen’07] (NTU) multi-sink [Yuen’06] (Toronto), [Kalantari’06] (Maryland),

[Kim’06] (Seoul) three aggregation heuristics, namely, the Shortest Paths

Tree (SPT), Center at Nearest Source (CNS), and the Greedy Incremental Tree (GIT) [Krishnamachari’02] (USC)

the tradeoff between power consumption and coverage of transmission nodes [Carle’04]

S-MAC [Ye’02], T-MAC [Dam’03], D-MAC [Lu’04a] [Lu’04b] retransmission [Shiou’05] [Bianchi’00] [Sheu’03] [Wen’06c] radius (refer to Ch. 5)


50

Agenda









51


For hot-spot networks system throughput maximization subject to time fairness

constraints

For mesh networks fair throughput and end-to-end delay with resource

allocation fair inter-TAP routing and backhaul assignment fair end-to-end delay and load balanced routing

For ad hoc networks message broadcasting dynamic adjustment of the transmission radius


52


For wireless sensor networks data aggregation tree structure routing duty-cycle scheduling node-to-node communication time retransmissions dynamic radius multi-sink cluster


53


Hot-spot and Mesh Networks channel assignment

Ad hoc and Sensor Networks the proposed maximization of mobile network lifetime is extende

d to include balanced power consumption among all nodes within a multiple session construction.

IEEE 802.16 BWA Networks optimization of the related parameters and placing controls on sc

heduling and admissions to minimize delay and maximize performance under QoS considerations;

minimization of end-to-end delay with controls on scheduling in IEEE 802.16 mesh mode.


54

THANK YOU FOR YOUR ATTENTION


55

Journal Papers Yean-Fu Wen and Frank Yeong-Sung Lin, “Fair Bandwidth Allocation and

End-to-End Delay Routing Algorithms in Wireless Mesh Networks”, Communications, IEICE Transactions on, E90-B(5), pp. xx-xx, May. 2007. (SCI, EI) (in press)

Frank Yeong-Sung Lin and Yean-Fu Wen, “Multi-sink Data Aggregation Routing and Scheduling with Dynamic Radii in WSNs”, IEEE Communications Letters, 10(10), pp. 692-694, Oct. 2006. (SCI, EI)

Under revision: Yu-Liang Kuo, Kun-Wai. Lai, Frank Yeong-Sung Lin, Yean-Fu Wen, Eric Hsiao-

kuang Wu, Gen-Huey Chen, “Multi-Rate Throughput Optimization with Fairness Constraints in Wireless Local Area Networks”, IEEE Transactions on Vehicular Technology, Dec. 2006 (major revised).

Under review: Frank Yeong-Sung Lin and Yean-Fu Wen, “Fair Inter-TAP Routing and Backha

ul Assignment in Wireless Mesh Networks”, was submitted to Journal of WCMC, Oct. 2006.

Frank Yeong-Sung Lin and Yean-Fu Wen, ”A Path-Based Minimum Power Broadcast Algorithm in Wireless Networks”, was submitted to ACM Baltzer Mobile Networks and Applications (MONET), Mar. 2007.

Book Chapters Yean-Fu Wen and Frank Yeong-Sung. Lin, “Cross-Layer Duty Cycle Schedulin

g with Data Aggregation Routing in Wireless Sensor Networks”, Lecture Notes Computer Science (LNCS), vol. 4096, pp. 894-903. (Proceedings of IFIP EUC 2006) (SCI, EI)


56

Conference Papers Y.F. Wen and Frank Y.S. Lin, W.C. Kuo, “A Tree-based Energy-efficient

Algorithm for Data-Centric Wireless Sensor Networks”, was accepted to appear in IEEE AINA 2007, Ontario, Canada, May 2007. (EI)

Y.F. Wen and Frank Y.S. Lin, “Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based Sensor Networks”, IEEE CCNC 2007, Las Vegas, NV, Jan. 2007. (EI)

C.D. Lee, Frank Y.S. Lin and Y.F. Wen, “An Efficient Object Tracking Algorithm in Wireless Sensor Networks”, JCIS, Kaohsiung Taiwan, 8-11 October, 2006. (EI)

Y.F. Wen and Frank Y.S. Lin, “Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing in Wireless Sensor Networks”, IFIP EUC 2006, Seoul Korea, Aug. 1-4, 2006. (EI)

Frank Y.S. Lin, H.H Yen, S.P. Lin, and Y.F. Wen, “MAC Aware Energy-Efficient Data-Centric Routing in Wireless Sensor Networks”, IEEE ICC 2006, Istanbul, TURKEY, Jun. 2006. (EI)

Y.F. Wen and Frank Y.S. Lin, “The Top Load Balancing Forest Routing in Mesh Networks”, IEEE CCNC 2006, Las Vegas, NV, Jan. 2006. (EI)

Frank Y.S. Lin, Y. F. Wen, L.C. Fu, and S.P. Lin, “Path-Based Minimum Power Broadcast Problem in Wireless Networks“, IEEE TENCON 2005, Melbourne, Australia, Nov. 2005. (EI)


57

Y.L. Kuo, K.W. Lai, Frank Y.S. Lin, Y.F. Wen, H.K. Wu, G.H. Chen, “Multi-Rate Throughput Optimization for Wireless Local Area Network Anomaly Problem”, IEEE/ICST BroadNets 2005, Boston, MA, USA, Oct. 2005. (EI)

Y.F. Wen, Frank Y.S. Lin, and K.W. Lai, “System Throughput Maximization Subject to Delay and Time Fairness Constraints in 802.11 WLANs”, IEEE ICPADS 2005, Fukuoka Institute of Technology (FIT), Fukuoka, Japan, Jul. 2005. (EI)

C.W. Shiou, Frank Y.S. Lin, H.C. Cheng, and Y.F. Wen, “Optimal Energy-Efficient Routing for Wireless Sensor Networks”, IEEE AINA 2005, Tamkang Taiwan, Mar. 2005. (EI)

Y.F. Wen and Frank Y.S. Lin, “Minimum Energy Consumption Routing Protocol in Multi-Rate Non-Infrastructure Wireless Network”, ICS 2004, Taipei Taiwan, Dec. 2004.

Y.F. Wen, Frank Y.S. Lin, and K.W. Lai, “Maximization of System Throughput Subject to Access Time Fairness Constraints in Multi-Rate 802.11 WLANs”, ICT 2004, Bang Na Thailand, Nov. 2004, pp. 99-108.

Y.F. Wen, Frank Y.S. Lin, and K.W. Lai, “Access Delay and Throughput Evaluation of Block ACK under 802.11 WLAN,” IASTED CCN 2004, MIT, Cambridge, USA, Nov. 2004.


58


Sender-based [Wen’07] Receiver-based [Cheng’95]

Network type wireless network virtual circuit networks

Given • traffic requirement s

• link capacity

• traffic requirement s

• total capacity

Resource control

an outgoing link by resource allocation

incoming links by capacity assignment

If link capacity is given

fully utilize the resources of each selected link

partially utilize the capacity to achieve minimax delay

Sources any node must be a leaf node

Perfect end-to-end delay

achieved by the next hop from the sink node.

achieved in the sink tree

The # of usage queues

the # of queues is equal to the # of branches.

the # of queues is equal to the # of branches.

Routing dynamically adjustable. fixed with capacity assignment; otherwise, partial usage


59

To increase a sensor network’s lifetime

Destination

Origin


[Wen’07]

60


6 min [( ) ( ) ( )]IP u u r data uv u uu V v V

Z m n E t RTS c e r

Objective function:

Subject to path constraints

0 1,px or , sDp P s S 1,

sD

pp P

x

s S

( , ) ( , ) ,sD

p p u v u vp P

x y

, ( , )s S u v L

(1)

(2)

(3)

….

...

...

S

1

2

3 7κ

654 Xp = 1

Xp’ = 0


61


link constraints

( , ) 0 1u vy or ( , )u v L

( , ) 1s vv V

y

s S

( , ) 1u vv V

y

u V

( , ) 1uu V

y

( , )( , )

max ,u vu v L

y H S

(4)

(5)

(6)

(7)

(8)

…....

...

S

1

2

3S7

κ

654

Note that (8) is added to the LR approach


62


( 2 )

( ( ) )ju

j V

ju jvj V j V

z

uv z RTS z

e RTS CTS B Nl N

e e

,u v V

5uvRTS CTS B l M 0 or 1uvz

( , )1

(1 )u uvu v uv

r dy z

M

,u v V ,u v V

uv uv uz d r

u ur R u V

0sr s S

,u v V

,u v V

(9)

(10)

(11)(12)

(13)

(14)(15)

….

...

...

S

1

2

3S7

κ

654luv

node-to-node transmission time constraints


63


( , )(1 )

( 2 )

u v

jvj V

y M

uv RTS z

ec

e

,u v V

{0, 1, 2,...., }uvc T ,u v V

[Shiou’05]

the number of retransmission constraints


64


3 ( . )( ) (1 )v vu v u um l M y m ,u v V

30 um M u V

4 (1 )u v vun m M y ,u v V

40 un M u V

(18)

(19)

(20)

(21)

1

S1

S2

S3

S4

1

3

1

12

2 3[0, 0+1]

[0, 0+3]

[0, 3+1]

[3, 4]

[3, 5+0]

[0, 3+2]

[0, 0+2]

[0, 0+3]

2

34

7

86

5

scheduling constraints


65


LR-based approach 0.115 (0.017 )

( 2 )

( 330)

juj V

ju jvj V j V

z

uv z RTS z

e RTS CTSl

e e

0.115 0.017

( 2 )

( 330)

juj V

jvj V

z

RTS z

e RTS CTS

e

( , )(1 )

5 ( , )( 2 )ln( ) ln ln( ) ( 2 ) (1 )

u v

jvj V

y M

uv uv jv u vRTS zj V

ec c RTS z M y

e

ln( ) ln 330 0.115 0.017 ( 2 )uv ju jvj V j V

l RTS CTS z RTS z


66

1 2 3 4 5 6 7( , , , , , , )LR suv uv uv uv uv uv uvZ u u u u u u u =

min [( ) ( ) ( )]u u r data uv u uu V v V

m n E t RTS c e r

1( , ) ( , )

s

suv p p u v u vs S u V v V p P

x y

2( , )

1

( (1 ) )u uvuv u v uv

u V v V

r dy z

M

3 ( )uv uv uv u

u V v V

z d r

4

5 ( , )( 2 ) (1 ) ln( )uv jv u v uvu V v V j V

RTS z M y c

5

ln 330 0.115 0.017

( 2 ) ln( )

juj V

uvu V v V jv uv

j V

RTS CTS z

RTS z l

63 ( , )(1 )vu v vu v u u

v V u V

m l M y m

+

74 ( , )(1 )uv u v u v

u V v V

n m M y

subject to: (1), (2), (4), (5), (6), (7), (8), (10), (11), (14), (15), (17), (19) and (21).

+

+ +

+

+


67

Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based WSNs

Problem we discuss how to increase the lifetime of a network

(including the previous issues) with a multiple sink structure (outgoing information gateways) and a cluster structure (source node’s message must be forwarded to the cluster-head first)


cross-layer network planning and performance optimization algorithms for wireless networks

Documents

introduction conclusion

sensor networks lifetime

multihop wireless networks

backgroundwireless networks

fair throughput

sensor nodes

causes throughput

channel access time