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
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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, ...
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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.
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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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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
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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
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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:
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