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A PREDICTIVE HANDOFF APPROACH FOR MOBILE IP

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					                     A PREDICTIVE HANDOFF APPROACH FOR MOBILE IP
                                                    Da-Wei Zhang(1), Yi Yao(1)
                         (1)State Key Lab. of Software Development Environment, BeiHang University
                     Room 514, YiFu Building, 37 XueYuan Road, Haidian district, Beijing, 100083, China
                                   Phone:86-010-82317614,E-mail: dawei@nlsde.buaa.edu.cn

Abstract: We present a predictive handoff mechanism                 is higher than given threshold, MN start L3 handoff.
that can predict the occurrence of L2 handoff well. It can             We compare our prediction method with scheme of ZB.
be used as a trigger for Mobile IP to start network layer           By mobility prediction on Zhou[6] ‘s “current” adaptive
handoff ahead exactly or pre-allocate resources. Using              model and getting position information by nonlinear least
this approach link layer of MN detects signal changes               square directly, our proposed handoff trigger scheme can
based on mobility information to calculate the probability          reliably predict future L2 handoff and improve the
of coming handoff and trigger network layer. In this                handoff performance of Mobile IP.
paper we illustrate the theory of this approach in detail              This rest is organized as follows: part II provides
and provide experiments to evaluate the performance.                related work and part III describes mechanism of this
The experimental results show that this approach can                approach. Performance evaluation is presented in part IV
provide an exact trigger with low false alarm and miss              and we provide conclusions in part V.
detection and ensure the seamless handoff of Mobile IP.
Keywords: Mobile IP; link layer trigger; mobility
prediction; handoff; kalman filter;                                 2 . L2 Handoff Probability Estimate by
                                                                    Mobility Information
1.Introduction
                                                                       In approaches of Mobile IP which uses L2 trigger, it is
                                                                    necessary to start L3 handoff before L2 handoff. In order
   In standard Mobile IP the handoff of MN between FAs              to reduce effect of handoff we must ensure whole
brings on communication interruption and packets loss.              handoff completed in overlay area of preceding and
To shorten handoff latency there are some rapid handoff             subsequent subnet, i.e. we must get a proper time to start
approach such as fast handoff [1] ,pre-registration handoff         L3 handoff. If MN starts handoff later, there may be
and post-registration handoff. To decrease packets loss             packet lost because signal quality of preceding subnet has
there are some smooth handoff approaches such as                    been unusable before handoff completes. If MN starts
packet buffering transmitting mechanism[2] or dual-link             handoff earlier, an unnecessary handoff maybe occurs
registration[3]. In these approaches a proper L2 trigger            when preceding subnet is usable all the while. So L2
time is very important. In rapid handoff, some handoff              must trigger handoff to L3 ahead. We should gain L2
procedures should be initiated in advance of actual L2              handoff time by prediction, which lies on change of
handoff, so MN must get a proper L2 trigger time. In                signal quality in future.
smooth handoff old FA must know start time to buffer                   In wireless network, L2 handoff in general depends on
packets. But research about trigger time now is only                the signal strength of current BS (base station) and
restricted in “Sufficiently”. Literature [4] proposes to start      selectable base stations,which is shown in Figure 1.
L3 handoff together with L2 handoff. But this needs that            There are four rules for selecting:
L3 handoff must complete before L2 handoff, which is                   1) Comparative signal strength rule: select the
achieved difficultly. Zainab R. Zaidi and Brian L.                  strongest signal in any time
Mark(ZB) [5] proposed a handoff trigger scheme based on                2) Comparative signal strength rule with relative
autoregressive(AR-I) model of mobility, but because of              threshold: MN only starts handoff when current signal
deviation of extend kalman filter, it must get accurate             below some given threshold and signal strength of
position information from GPS or other orientation                  selectable BS stronger than signal strength of current BS.
equipment.                                                             3) Comparative signal strength rule with hysteresis
   We proposed a predictive handoff approach based on               margin: MN only starts handoff when signal strength of
mobility prediction for L2 of Mobile IP to exactly                  selectable BS is much stronger than current BS.
calculate handoff probability and pass to L3. When MN                  4) Comparative signal strength rule with hysteresis
entered an overlapped wireless area, it keeps on detecting          margin and relative threshold: This rule combines 2 rules
the RSSI of APs and calculate the handoff probability by            above. Only when signal strength of current BS is under
mobility prediction model. Once the handoff probability             given threshold and signal strength of selectable BS is
hysteresis margin stronger than current BS, MN starts            mean equal with current acceleration and is considered for
handoff. This rule has been adopted in GSM.                      obeying the Rayleigh distribution. The continuous-time
                                                                 dynamic equation can be expressed as follows:
                                                                 ⎡. ⎤
                                                                 ⎢x(t) ⎥                            ⎡. ⎤
                                                                 ⎢ .. ⎥                             ⎢x(t) ⎥
                                                                 ⎢x(t) ⎥ ⎡0
                                                                   .
                                                                              1    0 0 0        0 ⎤⎢ .. ⎥ ⎡0          0⎤         ⎡0       0⎤
                                                                 ⎢ ⎥ ⎢                               x(t)
           BS1                            BS2                    ⎢... ⎥ ⎢0
                                                                   ..         0    1 0 0        0 ⎥⎢ ⎥ ⎢0
                                                                                                  ⎥⎢... ⎥ ⎢           0⎥⎥⎡ − ⎤ ⎢0
                                                                                                                                 ⎢        0⎥⎥
                                                                 ⎢x(t) ⎥ ⎢0   0    α 0 0        0 ⎥⎢x(t) ⎥ ⎢α         0 ⎥⎢ax ⎥ ⎢α         0 ⎥⎡wx (t)⎤
                                                                 ⎢. ⎥=⎢                           ⎥          +⎢         ⎥       +⎢          ⎥⎢      ⎥
                                                                 ⎢ y(t)⎥ ⎢0   0    0 0 1        0 ⎥⎢ . ⎥ ⎢0           0 ⎥⎢ − ⎥ ⎢0         0 ⎥⎣wy (t)⎦
                                                                                                    ⎢ y(t)⎥                ay ⎥
                                                                                                                          ⎢ ⎦
                                                                                                                          ⎣
                                                                 ⎢ ⎥ ⎢0       0    0 0 0        1 ⎥⎢ .. ⎥ ⎢0          0⎥         ⎢0       0⎥
                                                                 ⎢ .. ⎥ ⎢
                                                                    .
                                                                                                  ⎥⎢ ⎥ ⎢                ⎥        ⎢          ⎥
                                                                 ⎢ y(t)⎥ ⎢0
                                                                         ⎣    0 0 0 0           α ⎥⎢ y(t)⎥ ⎢0
                                                                                                  ⎦           ⎣       α⎥⎦        ⎢0
                                                                                                                                 ⎣        α⎥⎦
                                                                 ⎢ .. ⎥                             ⎢...(t)⎥
                Figure 1 Predictive Handoff                      ⎢... ⎥                             ⎣y ⎦
   In all, in whichever rule adopted, L2 handoff is              ⎢ y(t)⎦
                                                                 ⎣ ⎥
decided by signal strength, so we can get L2 handoff                                                                                                 (3)
time by studying the change of signal strength with time.                                   .   ..                .        ..
In existing cellular systems, MN measures RSSI                   Where        x(t ), x(t ), x(t ), y (t ), y (t ), y (t ) denote
(received signal strength indication) to describe signal         position, velocity and acceleration in x and y dimension
strength.                                                        and α is the reciprocal of the random acceleration time
   Measured in decibels RSSI can be modeled as the sum                         −    −
of two terms: one due to path loss, and another due to           constant. a x , a y are current acceleration in x and y
shadow fading. Fast fading is neglected assuming that a
low-pass filter is used to attenuate Rayleigh or Rician          dimension.       wx (t ), w y (t ) are white Gaussian noises and
fade. Therefore, the RSSI from a particular cell, can be         uncorrelated. It can be discrete as:
formulated as [7]                                                                                                          −

    RSSI (dB ) = R0 − ε lg d + ξ                      (1)        X (k + 1) = Φ (k + 1, k ) X (k ) + U (k ) a + W (k ) (4)
Where R0 is a constant determined by transmitted power,                                                                                                    T
wavelength, and antenna gain of cell . ε is a slope index                               ⎡       .       ..                      .           ..
                                                                                                                                                      ⎤
                                                                 Where X ( k ) = x( k ), x( k ), x ( k ), y ( k ), y ( k ), y ( k )
                                                                                        ⎢
                                                                                        ⎣                                                             ⎥
                                                                                                                                                      ⎦
(typically, ε = 20 for highways and ε = 40 for
microcells in a city), and ξ is the logarithm of the                   Besides state equation we should get enough
shadowing component, which is found to be a zero-mean            observation variables to estimate X (k ) . When MN can
Gaussian random variable with standard deviation 4–8 dB.         get the position coordinate directly by GPS, SOA or TOA
 d represents the distance between the MN and BS of cell ,       orientation systems, the observation equation is:
which can be further expressed in terms of the mobile’s                Z (k + 1) = H * X (k + 1) + V
position ( x (t ), y (t )) at time t and the location of
                                                                                   ⎡1 0 0 0 0 0⎤
base station   ( a , b) :                                        Where H = ⎢                    ⎥                                                    (5)
                                                                                   ⎣0 0 0 1 0 0 ⎦
d = ( x(t ) − a ) 2 + ( y (t ) − b) 2                    (2)          Otherwise MN can estimate by RSSI of BS it detects.
      All in all, to get L2 trigger time we must know d (t ) ,   Assume that there are at least 3 BS available, so the
                                                                 observation equation is [3]
i.e., we must estimate and predict movement state of MN.
When different information got, we can predict handoff by
                                                                 Z (k + 1) = H ( X (k + 1), k + 1) + V (k + 1) =
2 method below.                                                                      ⎡                       ( x ( k +1) − x1 ) 2 + ( y ( k +1) − y1 ) 2       ⎤
                                                                 ⎡ RSSI 1(k + 1) ⎤ ⎢ R1 − ε lg                                                                 ⎥
                                                                 ⎢ RSSI 2(k + 1)⎥ ⎢                          ( x ( k +1) − x 2 ) 2 + ( y ( k +1) − y 2 ) 2     ⎥
                                                                                 ⎥ = ⎢ R 2 − ε lg
2.1 Handoff Prediction with Movement
                                                                 ⎢                                                                                             ⎥
Model                                                            ⎢ RSSI 3(k + 1) ⎥ ⎢                         ( x ( k +1) − x3 ) 2 + ( y ( k +1) − y 3 ) 2      ⎥
                                                                 ⎢               ⎥ ⎢ R3 − ε lg                                                                 ⎥
     When exact position information can be estimated,           ⎣...            ⎦ ⎢...                                                                        ⎥
we can predict future position of MN and then estimate                               ⎣                                                                         ⎦
future RSSI MN detects. Based on theory of maneuvering                                                                                               (6)
targets tracking [8] [9], we propose a mobility predictive             Where        ( x1 , y1 ), ( x 2 , y 2 ), ( x 3 , y 3 ) denote the
model:                                                           position of BS. Applying linearization method in extended
     According to “current” model of Zhou [6], we                Kalman filter, where the linearization takes place about
suppose that random acceleration of MN is one rank               the filter’s estimated trajectory, the linearized
correlated in time. The acceleration of next time has a          measurement equation is:
           ⎡RSSI1 ⎤                                                                     signal strength rule with hysteresis margin K , then future
           ⎢RSSI2⎥       ^                       ^
                                                                                        handoff criterion is:
Z (k +1) = ⎢      ⎥ = h( X k +1,k ) + Hk Xk − Hk X k +1,k + Vk                          RSSI 2 (t 0 + jT , t 0 ) − RSSI 1(t 0 + jT , t 0 )
           ⎢RSSI3⎥
           ⎢      ⎥                                                                     = R 2 − R1                                                                                     (10)
           ⎣...   ⎦                                                                                     (( x ( t 0 + t ,t 0 ) − x 2 ) + ( y ( t 0 + t ,t 0 ) − y 2 ) )
                                                                                                                                    2                                2

                                                                                  (7)   − ε lg          (( x ( t 0 + t ,t 0 ) − x1) 2 + ( y ( t 0 + t , t 0 ) − y1 ) 2 )
                                                                                                                                                                           ≥K
And recursive equation is:
 ^                            ^                      −
X k +1,k = Φ k +1, k X k + U k a ( k )                                                  We can get L2 handoff probability at t 0 + jT :

Pk +1, k = Φ k +1,k Pk Φ T k +1,k + Q k                                                 Ph (t 0 + jT ) =
                                                                                         ⎛ (( x ( t 0 + t ,t 0 ) − x 2 ) 2 + ( y ( t 0 + t ,t 0 ) − y 2 ) 2 )                       ⎞
                                                                                                    ^                              ^
                              T                                                                                                                                 R 2 − R1 − K
K k +1 = Pk +1,k H k              k +1   [ H k +1 Pk +1, k + R k +1 ] −1          (8)    ⎜
                                                                                        P⎜ ^                                                                  ≤e ε                  ⎟
 ^               ^                                                ^                      ⎜ (( x ( t 0 + t ,t 0 ) − x1 ) 2 + ( y ( t 0 + t ,t 0 ) − y1 ) 2 )
                                                                                                                               ^
                                                                                                                                                                                    ⎟
                                                                                                                                                                                    ⎟
X k +1 = X k +1,k + K k +1 [ Z k +1 − h ( X k +1, k )]                                   ⎝                                                                                          ⎠ (11)
Pk +1 = [ I − K k +1 H k +1 ] Pk +1,k                                                           (
                                                                                        = P X 2 + Y 2 ≤ R2                                )
     Because of nonlinear disposal in observation
equations the Kalman filter has sizeable deviation and
                                                                                        =         ∫∫ f       x   ( x ) f y ( y )dxdy
                                                                                             x2 + y2 ≤R2
often tends to distortion, so we proposed another method
                                                                                        Where
to increase precision. That is calculating position
                                                                                               ⎛^                    sx − x1                 ⎞
coordinate first by nonlinear least square such as                                      X ~ N ⎜ x(t 0 + jT , t 0 ) + 2         , Pj ,k (1,1) ⎟
Gauss-Newton Method. Then the observation equation is                                          ⎝                       1− s                  ⎠
equal to (5).
     Considering the mean of “current” acceleration is                                        ⎛  ^                   sy − y1                  ⎞
                                                                                        Y ~ N ⎜ y (t 0 + jT , t 0 ) + 2        , Pj ,k (4,4) ⎟
             ^
equal to a k , and sampling interval is T , then predictive                                   ⎝                        1− s                   ⎠
step is j , and optimal fixed advancing prediction is:
 ^                        ^
                                                                                        R2 =
                                                                                                s
                                                                                             1− s
                                                                                                      x2 + y 2 −
                                                                                                        2
                                                                                                                 (
                                                                                                                 2     1
                                                                                                                     1− s
                                                                                                                           x1 + y1 +
                                                                                                                             2         2
                                                                                                                                           )                   (                )
X    j ,k   =Φ Xk    1
                      j
                                                                                  (9)
                                                                                        ⎛ sx 2 − x1 ⎞ ⎛ sy 2 − y1 ⎞
                                                                                                                  2                                    2

           ⎡    T ⎤                          2
                                                                                        ⎜           ⎟ +⎜          ⎟
           ⎢1 T   ⎥                                                                     ⎝ 1− s ⎠ ⎝ 1− s ⎠
           ⎢     2⎥
                                                                                                    R2 − R1 − K
Where Φ1 = ⎢0 1 T ⎥
                                                                                        s=e ε
           ⎢0 0 1 ⎥
           ⎢      ⎥                                                                     f x ( x), f y ( y ) are probability density function of X and
           ⎢
           ⎣      ⎥
                  ⎦                                                                     Y.
And covariance matrix is
                                          j −1
Pj ,k = Φ 1j Pk Φ 1j      ( ) + ∑ΦT
                                                     1
                                                      j −1−i
                                                                  (
                                                               Qk Φ 1j −1−i   )
                                                                              T

                                          i =k
So we know
                                                 ^                    ^
^                             ^     ( jT ) 2 ..  .
x(t 0 + jT , t 0 ) = x 0 + jT x 0 +          x0
                                       2
                                                 ^                    ^
^                             ^      ( jT ) 2 .. .
y (t 0 + jT , t 0 ) = y 0 + jT y 0 +          y0
                                        2
And actual position in t 0 + jT can be expressed as
Gauss distribution:
                              ^                                                              The probability described by duplicate-integral can
x(t 0 + jT ) ~ N ( x(t 0 + jT , t 0 ), Pj ,k (1,1))                                     not be expressed by elementary functions. But we can
                              ^
                                                                                        calculate it approximately by weighted sum of 4 point in
y (t 0 + jT ) ~ N ( y (t 0 + jT , t 0 ), Pj ,k (4,4))                                   integral circle area:
        We suppose that L2 handoff adopts comparative
                  πR 2        R       R          R       R                   It can be denoted as
Ph (t 0 + jT ) =       ( f x ( ) f y ( ) + f x (− ) f y ( )
                    4         2       2          2       2                   y = k 0 + k1t + k 2 t 2 + k 3 t 3 + k 4 t 4
         R        R            R        R
+ f x ( ) f y (− ) + f x (− ) f y (− )) + O( R 6 )
         2        2            2        2                                    Suppose       ϕ = [1, t , t 2 , t 3 , t 4 ]T ,
      When R2 = R1 , especially in some networks (such
as WLAN), K = 0 , then
                                                                             And    θ = [k 0 , k1 , k 2 , k 3 , k 4 ]T doesn’t change in
 Ph (t 0 + jT ) =
                                                                             several sampling period, so it can be estimated it by
 ⎛                  A+ B                   ⎞
Φ⎜                                         ⎟                                 RFM:
 ⎜ 4( x − x ) P (1,1) + 4( y − y ) P (4,4) ⎟
             2                    2
 ⎝     2   1   j ,k         2   1   j ,k   ⎠                                                         Pk ϕ k +1ϕ k +1 Pk
                                                                                                                T

                                          ^                                  Qk +1 = Pk −
A = ( x 2 − x1 )( x1 + x 2 − 2 x(t 0 + jT , t 0 ))                                              1 + ϕ k +1 Pk ϕ k +1
                                                                                                      T


                                              ^                                                       Qk +1ϕ1ϕ1T Qk +1
B = ( y 2 − y1 )( y1 + y 2 − 2 y (t 0 + jT , t 0 ))                          Pk +1 = Qk +1 −
                                                    (12)                                              1 + ϕ 1T Qk +1ϕ1
Where Φ is the Guass probability distribution function.                      θ k +1 = θ k + Pk +1 (ϕ k +1 ∆y n + k +1 − ϕ1 ∆y n +1 )
It meanings that at time t 0 + jT , L2 connects with new                                     ^ 2
subnet with probability Ph (t 0 + jT ) .                                           Then d i (t 0 + jT , t 0 ) can be calculated
                                                                             And future handoff criterion is:
2.2 Handoff Prediction by RFM                                                      RSSI 2 (t 0 + jT , t 0 ) − RSSI 1(t 0 + jT , t 0 )
                                                                                   = R 2 − R1
     When MN can’t get enough information to                                                    2
                                                                                               d2
determine its position, we can use recursive forgetting                                ε         1
                                                                                   −       lg d1 ≥ K
memory linear square (RFM) to estimate future RSSI.                                    2
When handoff MN can observe 2 RSSI at least:                                       Then L2 handoff probability at t 0 + jT :
          2 ( Ri − RSSI i )                                                  Ph (t 0 + jT )
di = e           ε
                              + η i = ( x(t ) − ai ) 2 + ( y (t ) − bi ) 2
   2

                                                                                ⎛      ^ 2                     ^ 2                      ⎞
                                                                                ⎜    η d 1 (t 0 + jT , t 0 ) − d 2 (t 0 + jT , t 0 )    ⎟
Where    ηi is the deviation of d i 2 .Then                                  = Φ⎜ 2                                                     ⎟
                                                                                ⎜ η D(d 1 (t 0 + jT , t 0 )) + D(d 2 (t 0 + jT , t 0 )) ⎟
                                                                                         2                             2

Because                                                                         ⎝                                                       ⎠

                        1                                                          Where D(d i (t 0 + jT , t 0 ))
                                                                                                         2
                                                                                                                              is   deviation   of
x(t ) = s x 0 + vx t + a x t 2
                        2                                                    ^ 2
                        1                                                    d i (t 0 + jT , t 0 )
y (t ) = s y 0 + v y t + a y t 2
                        2
                                                                             3. Handoff Trigger Scheme for Mobile IP
So we have

d i2 = (( s x 0 − a i ) 2 + ( s y 0 − bi ) 2 )
                                                                             3.1 Estimation of Ahead handoff time Tcos t
+ (2( s x 0 − a i )v x + 2( s y 0 − bi )v y )t
                                                                                  In fast or smooth handoff Mobile IP to start handoff
+ (v x + v y + a x ( s x 0 − a i ) + a y ( s y 0 − bi ))t 2
     2     2
                                                                             ahead with L2 trigger, we must estimate Tcos t .It means
+ (a x v x + a y v y )t 3                                                    L3 handoff latency such as pre-registration or time to
                                                                             prepare buffer(dispose). In common MN is belonging to
   1 2 1 2
+ ( a x + a y )t 4                                                           individual who often move on particular path. So network
   4     4                                                                   handoff will often implement between some certain
                                                                             subnets. Because of unchangeable of network topology,
                                                                             handoff latency is usually unchangeable when MN
switches between two subnets. So we can estimate this                     4. Experiment and Performance Analysis
latency by MN’s handoff history. MN records handoff
latency as follows:
  Handoff       Process       1    2    …     Last n
                                                         Tcos t           4.1 Experiment             of    Predictive       Handoff
                                                                          Accuracy
  FA1           dispose       T1   T2   …     (T1+T2     (T1+T2
  To FA2                                      +     …    +     …
                                              +Tn)/n     +Tn)/n+                In the experiment, we create simulation scene as
                Transmit      Current RTT                RTT              same as ZB [5]. We assume a rectangular are with cells of
                data
                                                                          size 2000m × 2000m . Each cell contains one base
  …             …             …    …    …     …          …
                                                                          station located at the center of the cell. The mobile
                 Table 1 History Handoff Delay
                                                                          station moving along the test trajectory receives signals
     According to table 1, when MN switches between
subnets, it records disposing time and transmitting time.                 from the base stations. The parameter R0 is assumed to
Before the next handoff, it will search handoff between                   be -30db for all base stations and ε is set to be 40. The
the same FAs. For disposing time, average time of front N                 shadowing variance is taken as 6 dB. The hysteresis
times is used as current predictive time (N can be                        margin is 3 dB.
configure).Transmitting time could be measured by Ping.                        Test trajectories are generated using the linear
                                                                          dynamic system model of mobility [6]. The parameters of
3.2 Ahead Handoff criterion                                               the Linear dynamic system-based model of mobility are
                                                                                       follows: α = 1000s
                                                                                                                −1
                                                                          set     as                          , T = 1s and
     According to (11), L3 should get handoff probability                 σ     = 1dB .The discrete command process u x (t ) and
function from L2, the probability of L2 handoff after time
Tcos t is                                                                 u y (t ) are independent semi-Markov processes, each
          Ph (t 0 + Tcos t ) . Assume that the false alarm cost           taking on five possible levels of acceleration comprising
is C1 and the miss detection cost is C2, then handoff
                                                                          the set (-0.5,-0.25, 0, 0.25, 0.5) m / s . This set of
                                                                                                                       2
evaluation expression is
      C1 (1 − Ph (t 0 + Tcos t ) ) < C 2 Ph (t 0 + Tcos t )
                                                                          acceleration levels produces an average speed of
                                                                          approximately 15 m / s . The initial probability vector
      That is                                                             π for the semi-Markov model (SMM) governing
                               C1                                         u x (t ) and u y (t ) is initialized to a uniform distribution.
      Ph (t 0 + Tcos t ) >            =β
                             C1 + C 2                                     The elements of the transition probability matrix for the
     Where β is the probability threshold for Mobile                      SMM are initialized to a common value of 1/5. The
                                                                          initial position of the mobile is randomly selected within
IP handoff.
                                                                          a 200 m × 200 m area centered at the origin of the
                                                                          coverage area.
3.3 Handoff procedure with L2 trigger                                           Figure 2 shows a snippet simulation result. We
                                                                          record the predictive handoff time together with actual
    Above we have illustrate the estimate method of                       L2 handoff. We can see there is 2 false alarm after 1400s
Ph (t0 + Tcos t ) and Tcos t , with which we can define                   and we can calculate the predictive difference time
handoff procedure with L2 trigger:
    In L2:
1) Detects signal strength of surrounding BS. If there is
    at least 2 BS, goto 2), else goto 1).
2) Calculates Ph (t 0 + Tcos t ) in period and pass it to
   L3.
   In L3:
1) Estimates Tcos t and        β
2) Receive Ph (t 0 + Tcos t ) from L2 in period.
3) Calculate Ph (t 0 + Tcos t ) , if Ph (t 0 + Tcos t ) >         β   ,
   goto 4), else goto 1).
4) Starts L3 handoff.

                                                                                   Figure 2 Predictive and Actual handoff
                                                                               For each simulation run, we calculate the ratio of
                                                                          missed detection, the ratio of false alarm, and the mean
prediction interval of handoff time. The mean prediction
interval is the average length of the interval from the
predictive handoff time until the occurrence the actual
handoff event.
      Simulation experiments are performed for
 j ={1s,…,15s}and β ={0.2, 0.3…,0.8}. The results
are averaged over 300 simulation runs for each parameter
setting. We compared experiment results with trigger
scheme proposed by ZB[5] when ahead time is 11s and
15s in figure 3, 4 and 5. Because of Perform mobility
prediction on Zhou’s “current” adaptive model instead of
AR-I, and nonlinear least square to estimate position
instead of EFK, the performance of our handoff trigger
scheme is higher than ZB[5]. In figure 4 we can see when
Tcos t = 2s , mean prediction interval is less than 0.3s,
                                                                        Figure 5 Mean Prediction Interval
which is reliable to ensure the seamless handoff of
Mobile IP.
                                                            4.2 Evaluation of Mobile IP Performance

                                                                  In the mobile IP protocol, the handoff latency may
                                                            directly lead to the interruption of communication, which
                                                            affects both TCP and UDP traffic, especially TCP.
                                                            Because of exponential backoff of TCP timeout timer and
                                                            MTU discover of tunnel on HA, TCP traffic can not
                                                            recover immediately after handoff. So we test TCP
                                                            performance when MN leaves home subnet on dual-link
                                                            Mobile IP base on our handoff trigger mechanism
                                                            (DLT)[3] comparative with standard mobile IP.
                                                                  For the test, the TCP traffic is generated by ttcp
                                                            benchmark. The results are shown in Figures 5 and 6
                                                            (two vertical lines present the beginning and ending of
                                                            handoff respectively).
                                                                  In figure 5, we can see standard Mobile IP Protocol
                                                            will bring on packet lost during the handoff process, so
            Figure 3 Probability of Miss Detection
                                                            the sender will use exponential backoff algorithm to
                                                            resend packet. Because MN can’t receive this
                                                            retransferred packet during handoff, TCP layer of
                                                            receiver has to retransfer this packet again after timeout.
                                                            The last time retransfer is caused by MTU discover
                                                            mechanism. The MTU of the tunnel on HA is 1440 bytes
                                                            while the size of the packets generated by ttcp is 1460
                                                            bytes. So when HA receives this packet with DF (Don’t
                                                            fragment) bit set, it rejects this packet and sends an
                                                            ICMP “Datagram Too Big” message to indicate the
                                                            tunnel’s MTU. After this retransfer failure, due to the
                                                            congestion control mechanism, TCP will enter slow
                                                            restart process.
                                                                  In comparison with the Mobile IP, our system does
                                                            not affect the TCP traffic. From figure 6, we can see that
                                                            the TCP traffic doesn’t interrupt during handoff and there
          Figure 4 Probability of False Alarm               is no packet lost or packet retransfer during the handoff
                                                            process.
                                                                         Support Optimized Handover for IP Mobility, work in
                                                                         progress, draft-manyfolks-l2-mobilereq-02.txt, June 2002.
                                                                  [5]    Z. R. Zaidi and B. L. Mark, A Mobility-aware Handoff
            16000
                                                                         Trigger Scheme for Seamless Connectivity in Cellular
 sequence




            14000
                                                                         Networks, IEEE Vehicular Technology Conference, v 60, n
            12000                                                        5, 2004 IEEE 60th Vehicular Technology Conference,
            10000                                                        VTC2004-Fall: Wireless Technologies for Global Security,
            80000                                                        2004, p 3471-3475
                                                                  [6]    Hong-Ren Zhou, Zhong-Liang Jing, Pei-De Wang,
            6000
                                                                         Tracking of Maneuvering Targets, (Beijing National
            40000                                                        Defence Publishing House,1991, 178-181)
            20000                                                 [7]    H. H. Xia, An analytical model for predicting path loss in
            0                                                            urban and suburban environments, in Proc. PIRMC, 1996
                    0        0.5     1.0    1.5     2.0     2.5   [8]    Tong Liu, Paramvir Bahl, and Imrich Chlamtac, Mobility
                                      time(s)                            Modeling, Location Tracking, and Trajectory Prediction in
                   Figure 5 TCP Performance of Mobile IP                 Wireless ATM Networks,IEEE journal on selected areas in
                                                                         communications,vol. 16, No. 6, Aug 1998
            500000                                                [9]    R.A.Singer, Estimating optimal tracking filter performance
            450000                                                       for manned maneuvering targets, IEEE Trans. Aerospace
            400000                                                       and Electronic Systems,July,1970
 sequence




            350000                                                [10]   Pubudu N.Pathirana. Mobility modelling and trajectory
            300000
                                                                         prediction for cellular networks with mobile base stations.
            250000
                                                                         International Symposium on Mobile Ad Hoc Networking &
                                                                         Computing Proceedings of the 4th ACM international
            200000
                                                                         symposium on Mobile ad hoc networking & computing.
            150000
                                                                         Annapolis, Maryland, USA, 2003. 213-221
            100000
            50000

                    0                 0.5             1.0
                                     time(s)
                        Figure 6 TCP Performance of DLT


5. Conclusion

      We have proposed a predictive handoff approach for
Mobile IP to support fast or smooth handoff. It integrates
handoff of L2 and L3. Using a real-time mobility
estimation to predict a future handoff, based on L2
handoff criterion in cellular networks --the difference of
RSSI between current and target base stations. From the
probability distribution function L3 of MN can start
handoff ahead by comparing missed detection cost and
false alarm cost as a threshold. This paper evaluates
handoff prediction performance. Our simulation results
show that the proposed handoff trigger scheme can
reliably predict future L2 handoff and improve the
Mobile IP performance.


Reference

[1] Tan C. L., Pink S and Lye K M. A Fast Handoff Scheme for
    Wireless Networks[A]. Proc. of ACM/IEEE WoW-MoM[C],
    1999.
[2] Ericsson, Perkins C. Low Latency Handoffs in Mobile
    IPv4 . IETF Internet Draft, Oct.2003.
[3] Da-Wei Zhang, Yi Yao, Lin Tian, "A predictive Handoff
    Approach of Mobile IP on Dual-Link", International
    Conference on Communications, Internet, and Information
    Technology (IASTED CIIT 2005),2005.10.31~11.2
[4] James Kempf, Requirements for Layer 2 Protocols to

				
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Description: Mobile IP is to move the mobile node to maintain its connectivity and design. There are two versions of Mobile IP, namely Mobile IPv4 (RFC 3344, replaces RFC 3220, RFC 2002) and Mobile IPv6 (RFC 3775). Is still widely used in Mobile IPv4.