Seamless Handover among heterogeneous wireless networksinfocom.uniroma1.it/~biagi/Tamea.pdf · Cost...

Seamless Handover among heterogeneous

wireless networks

Goal

Present the problem of vertical handover

Shows decision strategies supporting:

network heterogeneity (multiple parameters);

limited frequency (ping pong effect);

out of service and uncertainty in parameters

21/03/2011

Outline

Decision process in Vertical Handover

Single objective function

Ping Pong effect

Multiple-attribute decision making

21/03/2011

Introduction 1/2

regional

metropolitan area

campus-based

in-house

vertical

handover

horizontal

handover

Modern Wireless communications scenario: co-existence of

overlapped heterogeneous systems like GSM, UMTS, wifi, wimax, ...

21/03/2011

Introduction 2/2

Vertical Handover: allows

connectivity switching from

networks having different

technologies

Information Gathering

Decision

Execution

Serving NetworkCandidate Network

Mobile

Terminal

Decision Maker

Parameters

Strategy

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Decision process in vertical handover: Decision Maker

Handover can be initiated by network or

by mobile terminal1

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1 IEEE802.21 Media Independent Handover Services

Mobile Initiated

Mobile Node triggers a mobile-initiated handover (including query

phase)

Network Initiated

Serving Network triggers a network-initiated handover (including

query phase)

Decision process in vertical handover: Parameters

Parameter

Operator name

Authentication mechanism

Access technology

Services available

Geographic location

Coverage area

Parameter

Cost per byte

Total bandwidth

Utilization

Packet delay

Packet jitter

Packet loss

Non compensatory

CompensatoryRelative importance of the different parameters

Farooq Bari; Victor C.M. Leung, "Automated network selection in a heterogeneous wireless network environment,"

Network, IEEE , vol.21, no.1, pp.34-40, Jan.-Feb. 2007

one type of attribute value cannot be traded for

disadvantages of another attribute value

0

,

0

22,

0

11, ... MMiii xxxxxx

0

,

0

22,

0

11, ... MMiii xxxxxx

1

1 0,1

1

M

i

i

i

w

,...,Miw

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Decision process in vertical handover: Strategies

Function

Based

User

Centric

Multiple attribute

decision strategies

Fuzzy logic and

neural networks

Context

aware

Strategies

M. Kassar, B. Kervella and G. Pujolle, “An overview of vertical handover decision strategies in heterogeneous

wireless networks,” Comput. Commun. (2008), Doi:10. 1016/j.comcom.2008.01.044.

21/03/2011

Outline

Decision process in Vertical Handover

Single objective function

Ping pong effect

Multiple-attribute decision making

Conclusions

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Definition and open issues

H. Wang, R. Katz, J. Giese, Policy-enabled handoffs across heterogeneous wireless networks, Second IEEE

Workshop on Mobile Computing Systems and Applications, 1999 (Proceedings WMCSA‟99), 1999, pp. 51–60.

)1

()(1

),,(

n

cnp

n

b

nnnn

CNwPNw

BNw

CPBfC

Single objective function

Open issues:

Parameters modelling for specific technologies

Aggregate Objective

Function

(AOF)

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Vertical handover among broadcast networks 1/3

Power Saving

Bit Error Rate

User Preferences

Decision Plane

Q

Decision Plane

Q

Single quality function for DVB-H/UMTS (MBMS)

][][][][ kfwkfwkfwkQ UPUPBERBERPSPSNET

][][][ kQkQkQ UMTSHDVB

Tamea G., Inzerilli T., Rea P. and Cusani R., “Vertical handover among broadcast networks”, IEEE

ISWCS 2009

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Vertical handover among broadcast networks 2/3

Dh

hkPHRH

RH

PH

kQhDh

RIHQM

][1

Dh

hk

RH

Dh

hk

RH

Dh

hk

PH

RH

PH

RH

PH

RH

PH

kQ

kQkQ

RI

][

][][

Dh

hkPHRH

RH

PH

kQhDh

DS ][1

k

Q

k1

k2

k3

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Vertical handover among broadcast networks 3/3

Network Parameters

Parameter UMTS/MBMS DVB-H

Base Station

Power [dBm]

38-43 59-61

Power Save

[%]

0.42 0.91

Noise

Figure

[dB]

7 5

Bandwidth

[Mhz]

5 8

Thermal

Noise

-174 -174

Attenuatuion

Model

[dB, distance

in Km]

Okumura

Hata

137.4+

35.2log10(dist

ance)

Cost 231

124.3+

35.2log10(distanc

e)

Transmission

Frequency

[Mhz]

2140 700

1 2 3 4 5 6 7 8 90.1

0.15

0.2

0.25

0.3

0.35

handover duration

HQ

M

degree=1

degree=2

degree=3

degree=4

degree=5

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Outline

Decision process in Vertical Handover

Single objective function

Ping pong effect

Multiple-attribute decision making

Conclusions

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Ping Pong effect in handover 1/2

Ping-pong effect defined as: fast, repeated and undue handovers from

a system to the other, caused by wrong decisions

loss of packets, energy consumption, service interruptions

reduced Mobile Terminal performance, increased load network

1. H. Wang, R. Katz, J. Giese, Policy-enabled handoffs across heterogeneous wireless networks, Second

IEEE Workshop on Mobile Computing Systems and Applications, 1999 (Proceedings WMCSA‟99) ,

1999, pp. 51–60.

2. Ben-Jye Chang; Jun-Fu Chen; , "Cross-Layer-Based Adaptive Vertical Handoff With Predictive RSS in

Heterogeneous Wireless Networks," Vehicular Technology, IEEE Transactions on , vol.57, no.6, pp.3679-3692,

Nov. 2008

3. Barolli, L.; Xhafa, F.; Durresi, A.; Koyama, A.; , "A Fuzzy-Based Handover System for Avoiding Ping-Pong

Effect in Wireless Cellular Networks," Parallel Processing - Workshops, 2008. ICPP-W '08. International

Conference on , vol., no., pp.135-142, 8-12 Sept. 2008

4. Inzerilli, T.; Vegni, A.M.; Neri, A.; Cusani, R.; , "A Location-Based Vertical Handover Algorithm for Limitation

of the Ping-Pong Effect," Networking and Communications, 2008. WIMOB '08. IEEE International Conference

on Wireless and Mobile Computing, , vol., no., pp.385-389, 12-14 Oct. 2008

21/03/2011

Probability analysis for the limitation of the ping-pong effect :

C. Chi, X. Cai, R. Hao, and F. Liu, “Modeling and Analysis of Handover Algorithms”,

IEEE Int. Conf. Global Telecomm. 2007

Wrong Decision Probability (WDP) concept is given formed by:

unnecessary handovers, i.e. the new network cannot satisfy

requirements in the next interval;

missing handovers, i.e. the present network cannot satisfy requirements

in the next interval.

Limitations:

No common metric

No probability distribution is considered

Ping Pong effect in handover 2/2

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Algorithm for ping-pong limitation 1/2

Goodput (GP) is a common metric between different networks

Delta goodput stochastic process ΔGP[k] is defined as a function of k:

,i jGP k GP k GP k

GPVHO?

Reactive

vs

Probability based

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Algorithm for ping-pong limitation 2/2

Network i

Is

WDP<PTH? Network j

GP sign

transition

yes

no

Wrong Decision Probability

Different types of distribution:

Uniform

Exponential

Linear

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0 0.1 0.2 0.3 0.4 0.5

0

2

4

6

8

10

12

14

PTH

NV

HO

Opt

Exp

Linear

Const

Numerical Results: Number of VHO

Unnecessary

VHOs

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0 0.1 0.2 0.3 0.4 0.51.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3x 10

8

PTH

CB

R[B

its]

Opt

Exp

Linear

Const

Numerical Results: Cumulative Received Bits

Impact of unnecessary

VHO on CRB

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Outline

Vertical handover definition decision problem

Single objective function

Ping-Pong effect

Multiple-attribute decision making

Conclusions

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Introduction 1/2

Multiple-attribute decision making (MADM) aims at providing a support to

the decision makers when multiple and conflicting evaluations are

possible

MNN

M

xx

xx

,1,

,11,1

...

.........

...

N

alternatives

M parameters

Selection

matrix

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Introduction 1/3

Multiple-attribute decision making (MADM) aims at providing a support to

the decision makers when multiple and conflicting evaluations are

possible

MNN

M

xx

xx

,1,

,11,1

...

.........

...

N

alternatives

M parameters

Selection

matrixnetworks

networks parameters (e.g. QoS)

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Introduction 2/3

Ph.D. discussion

MADM

No information

Information

On

Environment

Information

On

Attribute

Dominance

Pessimistic

Ottimistic

Maxmin

Minmax

Standard level

Ordinal

Cardinal

Conjunctive

Disjunctive

Lexicographic Method

Simple Additive Weighting

Weighted Product

TOPSIS

ELECTRE

Median Ranking Method

AHP

Kwangsun Yoon,Ching-Lai Hwang, “Multiple attribute decision making: an introduction”, Sage

Publications, Inc; 1 edition, 1995)

17/11/2010

1.Farooq Bari; Victor C.M. Leung, "Automated network

selection in a heterogeneous wireless network

environment," Network, IEEE , vol.21, no.1, pp.34-40,

Jan.-Feb. 2007

2.Bari, Farooq; Leung, Victor, "Multi-Attribute Network

Selection by Iterative TOPSIS for Heterogeneous

Wireless Access," Consumer Comm. and Networking

Conf., CCNC 2007. 4th IEEE, pp.808-812, Jan. 2007

3.F. Bari and V.C.M. Leung, “Network Selection with

Imprecise Information in Heterogeneous All IP Wireless

Systems”, Proc. WiCon, Austin, TX, Oct. 2007.

4.Eng Hwee Ong; Khan, J.Y.; , "On optimal network

selection in a dynamic multi-RAT environment“, Comm.

Letters, IEEE , vol.14, no.3, pp.217-219, March 2010

Out-of-service phenomenon is not explicitly taken into account

MADM

Information

On Attribute

Cardinal

Simple Additive Weighting

Weighted Product

TOPSIS

ELECTRE

Median Ranking Method

AHP

Introduction 3/3

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Handover Information Discovery Handover Execution

Handover

Initiation

L2/L3

Handover

Estimate

QoS

information

Network

Conditions

Conditions

Handover Decision

Estimated

QoS

Functional Architecture

Normalized

Weights

Normalization

Network

Quality

Probabilities

Decision

function

Outcome

Start

VHONetwork Selection

Weights

Norm.

Params

Netw.

QoS

QoS

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Decision Process 1/2

Technique for order Preference by Similarity to Ideal Solution

(TOPSIS)

A1

A2d2

+

d1+

Network 1

Network 2 The chosen solution is the nearest to the

optimal solution and the farthest from non-

optimal solution

The network with the highest similarity

coefficient is chosen

* max ( )TOPSIS i N iA CCii

i i

dCC

d d

C.L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Springer,

Heidelberg, 1987.

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Decision Process 2/2

Parameters

Evaluation

Start

Ideal and

non-ideal

solution

Network

Selection

Selected

Network

MNN

M

mm

mm

S

,1,

1,1,1

...

.........

...

}{min

}{max

,

,

jij

j

jij

j

mA

mAi

i

i i

dCC

d d

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Decision Process: ‘out of service’ 1/2

It is possible to

neglect the

uncertainly and

consider the out

of service

probability

MoutXX

outXX

pmm

pmm

S

MN

M

,

1,

1...

.........

1...

,1,1

,11,1

Parameters value below a certain threshold may result in out of

service

iiX

M

jout dxxpP

j

jii

01

)(11,

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Decision Process: ‘out of service’ 2/2

Not possible to discern between different statistical behaviour („hard‟

TOPSIS)

“Weight” of out of service depends by the number of parameters

p

m

k

kS

1

1pm kkCCCC 21

‘soft’ TOPSIS

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‘Soft’ TOPSIS Decision Proces: Parameters 1/2

Parameters

Evaluation

Start

Ideal and

non-ideal

solution

Network

Selection

Selected

Network

MNN

M

mm

mm

S

,1,

1,1,1

...

.........

...

}{min

}{max

,

,

jij

j

jij

j

mA

mAi

i

i i

dCC

d d

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‘Soft’ TOPSIS Decision Proces: Parameters 2/2

Every attribute, in each

network can be described

by a „normalized‟

probability

Parameters may be affect by uncertainty

measurement errors

statistical variations

)( ,, jiX xpji

0 100 200 300 400 500 600 700 800 900 10000

0.5

1

1.5

2

2.5

3

3.5

4x 10

-3

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Parameters

Evaluation

Start

Ideal and

non-ideal

solution

Network

Selection

Selected

Network

MNN

M

mm

mm

S

,1,

1,1,1

...

.........

...

}{min

}{max

,

,

jij

j

jij

j

mA

mAi

i

i i

dCC

d d

‘Soft’ TOPSIS Decision Proces: Ideal and non-ideal

solution 1/2

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1, 2, ,max , ,........m m m N mZ X X X ?)( mZP

N

ijj

mX

N

i

mXmZ xPxpxpmjmim

11

)()()(,,

N

i

mim XPZP1

, )()(

Benchmark network -> ideal network consisting of “maximum”

Maximum distribution under parameters independency

‘Soft’ TOPSIS Decision Process: Ideal and non-ideal

solution 2/2

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Parameters

Evaluation

Start

Ideal and

non-ideal

solution

Network

Selection

Selected

Network

MNN

M

mm

mm

S

,1,

1,1,1

...

.........

...

}{min

}{max

,

,

jij

j

jij

j

mA

mAi

i

i i

dCC

d d

‘Soft’ TOPSIS Decision Process: Network selection 1/3

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“distance”

strategies

‘Soft’ TOPSIS Decision Process: Network selection 2/3

2

,min, }){( jij XEXEMean1

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“distance”

strategies

‘Soft’ TOPSIS Decision Process: Network selection 2/3

2

,min, }){( jij XEXEMean1

2Kullback

Leibler

1

0 min,

,

,)(

)(log)(

xp

xpxp

j

ji

ji

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“distance”

strategies

‘Soft’ TOPSIS Decision Process: Network selection 2/3

2

,min, }){( jij XEXEMean1

2Kullback

Leibler

1

0 min,

,

,)(

)(log)(

xp

xpxp

j

ji

ji

)()()1(1

1

0

min,, ijXiX

k dxxpxpxji

3 Hellinger

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‘Soft’ TOPSIS Decision Process: Network selection 3/3

selection

strategies

Maximum-minimum1 itotd min,

i max

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‘Soft’ TOPSIS Decision Process: Network selection 3/3

selection

strategies

Maximum-minimum1

2 Minimum-maximum

itotd min,

i max

itotd max,

i min

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‘Soft’ TOPSIS Decision Process: Network selection 3/3

selection

strategies

Maximum-minimum1

2 Minimum-maximum

3 Vicinity coefficient

itotd min,

i max

itotd max,

i min

maxmax,min,

min,

itot

itot

itot

i dd

d

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‘Soft’ TOPSIS Decision Process: case study

(Gaussian Distribution) (1/4)

1 Parameter (Gaussian distribution)

2 Networks

2

1

12

)(

2

2

2

2

22

)(

2

1

11max,

22

1

2

1

2

1

22

1

2

1

2

1)(

22

22

21

21

xerfe

xerfexp

x

x

0.2 0.4 0.6 0.80

1

2

3

4

5

6x 10

-3

X1

pX

1,1

pX

2,1

pX

max,1

pX

min,1

1

0

12

1

12

)(

2

2

2

2

22

)(

2

1

2

)(

2

1

1max,

22

1

2

1

2

1

22

1

2

1

2

1

2

11

22

22

21

21

21

21

dxx

erfex

erfeed

xxx

21/03/2011

k=2

2

m

-0.025 -0.02 -0.015 -0.01 -0.005 0 0.005 0.01 0.015 0.02 0.025-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

‘Soft’ TOPSIS Decision Process: case study

(Gaussian Distribution) 2/4

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Soft TOPSIS exhibits adequate trade-off between outage probability and

parameter efficiency

‘Soft’ TOPSIS Decision Process: case study

(Gaussian Distribution) (3/4)

min p_out STOPSIS HTOPSIS0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Strategy

e

Performances for the different strategies (Pout

)

min p_out STOPSIS HTOPSIS0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Strategy

e

Performances for the different strategies (m)

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‘Soft’ TOPSIS Decision Process: case study

(Gaussian Distribution) (4/4)

0 2 50

0.2

0.4

0.6

0.8

1

1.2x 10

-4

k

po

ut

min pout

STOPSIS

HTOPSIS

Decrease

in

Pout

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‘Soft’ TOPSIS Decision Process: multiple

parameters/networks 1/2

Efficiency

gain

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‘Soft’ TOPSIS Decision Process: multiple

parameters/networks 2/2

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Conclusion

Defined decision criteria for vertical handover:

based on quality function for specific technologies (DVB-H/UMTS)

including modelling of specific parameters;

specific for the minimization of ping-pong effect with limited knowledge

of network dynamics;

based on multi-decision criteria metodologies with „soft‟ approach, trade

off with parameters.

Additional information:

mauro.biagi@uniroma1.it,

cusani@infocom.uniroma1.it

tamea@infocom.uniroma1.it

21/03/2011