A Fuzzy Logic-Based Adaptive Handoff Management ... - CiteSeerX

JOURNAL OF NETWORKS, VOL. 4, NO. 10, DECEMBER 2009

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A Fuzzy Logic-Based Adaptive Handoff Management Protocol for Next-Generation Wireless Systems Presila Israt, Namvi Chakma, and M. M. A. Hashem Department of Computer Science and Engineering Khulna University of Engineering and Technology (KUET) Khulna-9203, Bangladesh Email: {[email protected], namvi [email protected], mma [email protected]}

Abstract— In the integrated next-generation wireless systems (NGWS), users are always connected to the best available networks and switch between different networks based on their service needs. It is an important and challenging issue to support seamless handoff management in NGWS. The objective of this paper is to develop a seamless handoff management protocol for NGWS. In this work, a fuzzy logic-based adaptive handoff (FLAH) management protocol is developed which is then integrated with an existing cross layer handoff protocol. Afterward, the handoff performance comparison of the existing protocol and our proposed protocol is carried out. The simulation results exhibit that, proposed fuzzy logic-based handoff management protocol has much better performance than conventional protocols for both intra and intersystem handoffs. Index Terms— Call Admission Control, Fuzzy Logic, Handoff Management, Intelligent Systems, Mobility Management, Wireless Networks.

like wireless local area network (WLAN), cellular, and satellite network [1]. Call admission control or handoff management of a mobile user while roaming through different wireless network architectures of NGWS is one of the important issues that needed to be solved. Mobility management contains two components: location management and handoff management [2] . Location management enables the system to track the locations of mobile users between consecutive communications. On the other hand, handoff management is the process by which users keep their connections active when they move from one Base Station (BS) to another. Fig. 1 shows a typical handoff scenario in the NGWS [2] where two types of handoff scenarios may arise: horizontal handoff and vertical handoff [3], [4]. •

I. I NTRODUCTION Next Generation Wireless System (NGWS) consists of different wireless architectures ranging from cellular wireless networks to satellite networks. NGWS provides wide range of services, varies from high rate data traffic, real time multimedia traffic, handoff management, to high secure communication. None of the existing wireless networks can simultaneously satisfy the high data rate, low latency, and ubiquitous coverage needs of the mobile users’ service demands. On the other hand, since these wireless networks are complementary to each other, their integration and coordinated operation can provide ubiquitous “always best connection” quality mobile communications to the users. In the integrated NGWS, users are always connected to the best available networks and switch between different networks based on their service needs, each of which is optimized for some specific services and coverage area to provide ubiquitous communications to the mobile users. In NGWS, mobile user can roam between a diverse set of wireless architectures This paper is based on “A Fuzzy Logic-Based Adaptive Handoff Management Protocol for Next-Generation Wireless Systems,” by P. Israt, N. Chakma, and M. M.A Hashem, which appeared in the 11th IEEE International Conference on Computer and Information Techc 2008 nology (ICCIT), 2008, Khulna, Bangladesh, December 2008. ° IEEE.

© 2009 ACADEMY PUBLISHER doi:10.4304/jnw.4.10.931-940

•

Horizontal Handoff: Handoff between two BSs of the same system. Horizontal handoff can be further classified into Link-Layer Handoff: Horizontal handoff between two BSs that are under the same Foreign Agent (FA). e.g., the handoff of a Mobile Terminal (MT) from BS10 to BS11 in Fig. 1 Intrasystem Handoff: Horizontal handoff between two BSs that belong to two different FAs and both the FAs belong to the same system and hence, to same Gateway Foreign Agent (GFA), e.g., the handoff of hte MT from BS11 to BS12 in Fig. 1. Vertical Handoff (Intersystem Handoff): Handoff between two BSs that belong to two different systems and, hence, to two different GFAs, e.g., the handoff of the MT from BS12 to BS2 in Fig. 1.

However, seamless support of handoff management in NGWS is still an open research issue. The existing handoff management protocols are not sufficient to guarantee handoff support that is transparent to the applications in NGWS. Mobility management protocols operating from different layers of the network protocol stack (e.g., application layer [5], transport layer [6], network layer

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HA

GFA1 FA10

HA: Home Agent GFA: Gateway Foreign Agent FA: Foreign Agent MT: Mobile Terminal BS: Base Station

Internet

GFA2 FA20

FA11 System B

System A

BS11 BS2

0

BS10

BS12

Figure 1. Handoff in the integrated NGWS architecture.

and link layer [7], [8]) are proposed to provide seamless services. However, seamless support for intra and intersystem handoff is still an open issue [3]. Therefore, recently the design of cross-layer mobility management protocols has gained significant attention [2]. Recently, new handoff algorithm are emerging based on advanced techniques like pattern recognition [9], neural networks and fuzzy logic system (FLS) [10]–[12]. These complicated algorithms are necessitated by the complexity of the handoff problem and dynamic conditions of wireless networks. In this paper, a fuzzy logic based handoff management protocol is introduced which is integrated with an existing Cross-Layer Handoff Management Protocol (CHMP) [2]. The simulation results reveal that, fuzzy logic based handoff management protocol has much better performance than conventional algorithms as well as CHMP for both intra and intersystem handoffs. The remainder of the paper is organized as follows: We will start by presenting some related works in section II. We present a handoff scenario and analyze the relationship among different work in section out in section conclusion. II. R ELATED WORKS Handoff management protocols operating from different layers of the TCP/IP protocol stack (e.g., link layer, network layer, transport layer, and application layer) are proposed in the literature [3]. Mobile IP [13] that operates from the network layer is proposed to support mobility management in IP-based networks. It forwards packets to mobile users that are away from their home networks using IP-in-IP tunnels [13]. Mobile-IP-based handoffs have significant handoff latency [3]. Transport layer mobility management protocols are proposed to support handoff management between different networks. These protocols eliminate the need for tunneling of the data packets. An architecture called MSOCKS is proposed in [6] for transport layer handoff management. © 2009 ACADEMY PUBLISHER

MSOCKS implements transport layer handoff using a split-connection proxy architecture and a new technique called TCP Splice that gives split-connection proxy systems the same end-to-end semantics as usual TCP connections [6]. The proposed hierarchical Mobile IP and micromobility solutions [14]–[16] particularly achieve reduction in registration signaling delay, but fail to address the problem of handoff requirement detection delay [7]. Therefore, recently, the use of link layer information to reduce the handoff requirement detection delay has gained attention [3], [17], [18]. The basic idea behind this approach is to use the link layer information to anticipate the possibility of an intra or intersystem handoff in advance so that the handoff procedures can be carried out successfully before the MT moves out of the coverage area of the serving base station (BS). The use of link layer information significantly reduces the handoff latency and handoff failure probability of handoff management protocols [3]. A generic link layer technique is used in [18] to improve the handoff performance of Mobile IP. However, it does not specify any particular mechanism for obtaining the link layer triggers. Different link-layer-assisted handoff algorithms that use received signal strength (RSS) information to reduce handoff latency and handoff failure probability are proposed in [7], [19]. However, these studies are limited to handoff between third-generation (3G) cellular networks and WLANs. There are some other studies such as S-MIP [5] that use RSS to track the MTs and then use their trajectory information to support low latency Mobile IP handoff. The above linklayer-assisted handoff protocols implicitly assume that the handoff latency of the intra and intersystem handoffs are constant. Based on this assumption, these protocols initiate a handoff when the RSS of the serving BS drops below a predefined fixed threshold value. However, in a real scenario, signaling delay of the intra and intersystem handoffs depends on the traffic load in the backbone network, wireless link quality, and distance between a user and its home network at the handoff instance [2]. Therefore, the protocols that are designed assuming a fixed signaling delay for intra and intersystem handoffs have poor performance when the handoff signaling delay varies. Moreover, the existing link-layer-assisted handoff protocols do not consider the influence of users’ speed on the performance of the handoff protocols. In addition, to the best of our knowledge, there is no existing work that determines how the link layer information can be used to guarantee desired performance in terms of handoff latency and handoff failure probability. Several handoff management protocols have been introduced for handoff management in NGWS. In [20], a scheme for vertical handoff management protocol for reducing handoff delay using the concepts of Received Signal Strength (RSS) and thresholds management is introduced and then a new secured IP assignment protocol is proposed to guarantee QoS. In [21], a new wireless network architecture for mobility management


is proposed to integrate the cellular system and Wireless Local Area Network (WLAN) of various providers that may not necessarily have direct service level agreement (SLA) among them. This algorithm considers traffic load as an important factor for initiating handoff. New mobility management protocols are also developed to support vertical handoff between different wireless systems by using the concept of the dynamic boundary area. The dynamic boundary area is determined based on the speed of the user and handoff signaling delay information.In [22] a fuzzy logic rule base is created based on the known sensitivity of handoff algorithm parameters (e.g., RSS threshold and RSS hysteresis) to interference, traffic, etc. To keep the algorithm general and simple, RSS, Signal-to-Interference Ratio (SIR), traffic, and velocity are used as handoff criteria, and transmit power and distance are excluded. In [23] fuzzy logic is used to deal with imprecise handover criteria and user preference. For handover decision, imprecise data are first converted to crisp numbers, and then, classical multiple attribute decision making (MADM) methods are applied. The use of fuzzy logic concepts to design an adaptive multicriteria vertical handoff decision algorithm is presented in [24]. In summary these works have some drawbacks. Some of them promised to support mobility management in IP-based network but have significant handoff latency. Some of them are simple to implement but has several shortcomings such as high global signaling load and high handoff latency. The existing algorithms fail to consider the behavior of dynamic cellular environment and to provide a systematic procedure for the adaptation of handoff parameters to the dynamic cellular environment. Considering the existing literature, we approach our work with the following goals, • Invoking a new handoff management protocol based on fuzzy logic for integrated heterogeneous networks so that the handoff procedures can be carried out successfully before the MT moves out of the coverage area of the serving base station (BS). • Reducing the handoff latency significantly in the network and hence enhance the performance of handoff management. • Limiting the handoff failure probability and at the same time, to reduce unnecessary load on the system that arises because of false handoff initiation. • Simulating the proposed approach and evaluate its performance against existing efficient handoff management protocols, which proves the proposed method a one step ahead of the existing handoff management protocols.

III. OVERVIEW OF H ANDOFF We define the following notations with reference to Fig. 2, which shows a handoff from the current BS, referred as old BS (OBS), to the future Bs, referred as new BS (NBS). © 2009 ACADEMY PUBLISHER

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Figure 2. Analysis of handoff process.

•

• •

Sth : The threshold value of the RSS to initiate the hierarchical mobile IP (HMIP) handover process. Therefore, when the RSS of old BS (OBS), referred to as ORSS, in Fig. 2 drops below Sth , the HMIP registration procedures are initiated for MT’s handover to the new BS (NBS). Smin : The minimum value of RSS required for successful communication between an MT and OBS. a: The cell size. We assume that the cells are of hexagonal shape.

When a Mobile Terminal (MT) is moving from its serving Old Base Station (OBS) to New Base Station (NBS) with a speed v, the MT may learn about the possibility of moving into another cell when the Received Signal Strength (RSS) of OBS decreases continuously. When the MT discovers that it may enter into the coverage area of the NBS, the next challenge is to decide the right time to initiate HMIP registration procedures with the New Foreign Agent (NFA). The existing link-layerassisted HMIP protocols [24] propose to initiate the HMIP registration when the RSS from the serving BS, e.g., OBS in Fig. 2, drops below a fixed threshold value Sth . During the course of its movement, when the MT reaches the point P (the distance of P from the cell boundary is d) as shown in Fig. 2, the RSS from the OBS drops below Sth . Therefore, when the MT reaches P , it initiates the HMIP registration with the NFA. At this point, the RSS received by the MT from NBS shown as NRSS in Fig. 2 may not be sufficient for the MT to send the HMIP registration messages to NFA through NBS. Hence, the MT may send the HMIP registration messages to NFA through OBS. This is called pre-registration. For a smooth and successful handoff from OBS to NBS, MT’s HMIP registration with NFA and link and MAC layer associations with NBS must be completed before the RSS of OBS drops below Smin , i.e., before the MT moves beyond the coverage area of OBS [2].

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When the MT is located at point P , it can move in any direction with equal probability, i.e., the probability density function (pdf) of MT’s direction of motion θ is 1 , −π < θ < π. (1) fθ (θ) = 2π The need for handoff to NBS arises only if MT’s direction of motion from P is in the range [θ ² (−θ1 , θ1 )], where a ). Otherwise, the handoff initiation is θ1 = arctan( 2d a false one. Therefore, the probability of false handoff initiation is Z θ1 pa = 1 − fθ (θ)dθ −θ1

¡a¢ 1 2θ1 . (2) = 1 − tan−1 = 1− 2d π 2π When the direction of motion of the MT from P β²[(−θ1 , θ1 )], the time it takes to move out of the coverage area of OBS is given by d sec β v We know that the Pd f of β is ½ 1 2θ1 , -θ1 < θ < θ1 fβ (β) = 0, otherwise. t=

(3)

(4)

From ( 3), t is a function of β, i.e., t = g(β), where β . Therefore, the pdf of t is given by, g(β) = d sec v X fβ (βi ) (5) ft (t) = |´ g (βi )| where βi are the roots of the equation t = g(β) in [−θ1 , θ1 ]. The equation t = g(β) has two roots in the interval [−θ1 , θ1 ] and, for each of these roots, fβ (β1 ) = 1 2θ1 for i = 1 and 2. Therefore, (5) becomes ft (t) =

1 θ1 |´ g (βi )|

Neighbor Discovery Unit

Handoff Signaling Delay Estimation Unit

HMIP Registration

Fuzzy Logic System

Speed Estimation Unit RSS Measurement Unit

Handoff Execution Unit

Handoff Trigger Unit Link Layer

Figure 3. Proposed fuzzy logic based handoff architecture.

Now, using (9) and ( 10), p 2  a 2  4 +d  τ>  1, vp a2 2 Pf = d 1 4 +d −1 d < τ < ), cos (   v v v τ θ  1 d 0, v ≥τ

(11)

IV. P ROPOSED F UZZY L OGIC BASED A DAPTIVE H ANDOFF A LGORITHM The proposed fuzzy logic based system initiates handoff using fuzzy logic; it uses mobile’s speed and distance as input and received signal strength as output. The proposed algorithm is adaptive to velocity interference and distances, resulting in fewer dropped calls, better communication quality, potentially lower MT transmit power requirements, gives good performance at different MT speeds, and decreases handoff failure probability.

(6) A. Design Procedure

where g´(β)is the derivative of g(β) given by r v 2 t2 d sec β tan β −1 =t g´(β) = d2 v Using (6) and (7), the pdf of t is given by p 2 ( a 2 d 4 +d √ d < t < , 2 2 2 4 v ft (t) = θ1 t v t −d 0, Otherwise.

(7)

(8)

The probability of handoff failure [2] is given by p 2  a 2  4 +d  τ>  1, vp a2 2 (9) Pf = 4 +d  p(t < τ ), vd < τ <  v  d 0, v ≥τ where τ is the handoff signaling delay and p(t p < τ ) is a2

+d2

4 , the probability that t < τ . When vd < τ < v using ( 8), Z p(t < τ ) = ft (t)dt Z d 1 d √ dt ≈ cos−1 ( )(10) = vτ θ1 πt v 2 t2 − d2

© 2009 ACADEMY PUBLISHER

Network Layer

We design an architecture to initiate handoff procedure that is adaptive to the link layer (Layer 2) and network layer (Layer 3) parameters. Then, we develop a handoff management protocol based on the architecture. As the proposed handoff management protocol uses information derived from different layers of TCP/IP protocol stack (e.g., speed information from link layer and handoff signaling delay information from network layer), we call it cross-layer handoff management protocol. The architecture of our proposed FLAH is shown in Fig. 3. Some of these modules collect the link and network layer information useful for handoff management and other modules use this information to decide the appropriate time to initiate and execute the handoff procedures using fuzzy logic system. The functionalities of these units are as follows: The neighbor discovery unit assists the MT to learn about the neighboring BSs. When an MT is served by a BS, it learns about its neighboring BSs using the neighbor discovery unit. By neighboring BSs, we mean the BSs that are the immediate neighbors of the serving BS. Some of these BSs may belong to the serving FA, whereas


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TABLE I. F UZZY RULE BASE

Intersystem

Intrasystem

Output

Input

Output

Input Velocity

Distance

RSSthreshold

Velocity

Distance

Slowest

Very-short

Near Medium

Slowest

Very-short

RSSthreshold Low

Slowest

Short

Low

Slowest

Short

Low

Slowest

Medium

Low

Slowest

Medium

Low

Slowest

Long

Very Low

Slowest

Long

Very Low

Slowest

Very-long

Very Very Low

Slowest

Very-long

Very Very Low

Slow

Very-short

Medium

Slow

Very-short

Medium

Slow

Short

Near Medium

Slow

Short

Near Medium

Slow

Medium

Near Medium

Slow

Medium

Near Medium

Slow

Long

Low

Slow

Long

Low

Slow

Very-long

Low

Slow

Very-long

Low

Medium

Very-short

Far Medium

Medium

Very-short

Far Medium

Medium

Short

Far Medium

Medium

Short

Far Medium

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Long

Medium

Medium

Long

Medium

Medium

Very-long

Near Medium

Medium

Very-long

Near Medium

Fast

Very-short

Very High

Fast

Very-short

Very High

Fast

Short

Very High

Fast

Short

Very High

Fast

Medium

High

Fast

Medium

High

Fast

Long

High

Fast

Long

High

Fast

Very-long

Far Medium

Fast

Very-long

Far Medium

Fastest

Very-short

Very Very High

Fastest

Very-short

Very Very High

Fastest

Short

Very Very High

Fastest

Short

Very Very High

Fastest

Medium

Very High

Fastest

Medium

Very High

Fastest

Long

Very High

Fastest

Long

Very High

Fastest

Very-long

Very High

Fastest

Very-long

Very High

others may belong to different FAs. When the MT moves into the coverage of a neighboring BS that belongs to its serving FA, the resulting handoff is a link layer handoff. In this case, the MT uses the existing link layer handoff algorithms [8]. When the neighboring BS belongs to a different FA under the serving system, the corresponding handoff is an intrasystem handoff. On the other hand, when the neighboring BS belongs to a different system, the resulting handoff is an intersystem handoff [2]. We use fuzzy logic based adaptive handoff management protocol for both intra and intersystem handoffs. Using the neighbor discovery protocol, the MT learns the details of its neighboring BSs, such as the IP addresses of the FAs that serve these BSs. The handoff signaling delay estimation unit estimates the delay associated with intra and intersystem handoffs. It is difficult to predict which particular BS the MT will move unless the handoff instance is very close. Our objective is to estimate the handoff signaling delay in advance without knowing which particular BS the MT will move. This can be done in many ways. For example, © 2009 ACADEMY PUBLISHER

techniques such as [25], [26] can be used to estimate the delay between different network entities that are involved in the handoff process and, using this information, the handoff signaling delay for intra and intersystem handoff can be estimated. The speed estimation unit estimates mobile’s speed using VEPSD [27]. The maximum Doppler frequency (fm ) is related to speed (v) of a mobile user, speed of light in free space (c), and carrier frequency of the received signal (fc ) through ³c´ fm . (12) v= fc VEPSD uses fm of received signal envelope to estimate speed of a mobile user. It estimates fm using the slope of power spectral density (PSD) of the received signal envelope. The slope of PSD of received signal envelope has maxima at frequencies fc ± fm in mobile environments [27]. VEPSD detects the maximum value of received signal envelopes PSD that corresponds to the highest frequency component (fc + fm ) to estimate fm .

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Fuzzy logic system unit uses speed and handoff signaling delay information to estimate adaptive RSS threshold (Sath ) as discussed earlier in this section. Handoff trigger unit collects information from RSS measurement unit and when the RSS of the serving BS drops below Sath , the handoff trigger unit sends a trigger to the handoff execution unit to start the HMIP handoff procedures. Finally, the handoff execution unit starts the HMIP registration process at the handoff initiation time calculated by the handoff trigger unit.

Fuzzy Rule Base

Crisp point of Velocity and Distance

Fuzzifier

Fuzzy Inference Engine

Slow, Medium, Fast etc in Velocity and Short, Long etc in Distance

COA Defuzzifier

Crisp Point of RSS Threshold

Low, High, Very High etc in RSS Threshold

V. S IMULATION A. Simulation Criteria Fuzzy logic system determines the value of adaptive RSS threshold (Sath ) to initiate the HMIP handoff © 2009 ACADEMY PUBLISHER

Velocity (km/h)

Membership Values

Figure 5. Membership function of fuzzy variable velocity (microcellular system).

Distance (m)

Figure 6. Membership function of fuzzy variable distance (microcellular system).

Membership Values

In the equation 13, µA (xi ) is the area of a membership function modified by the fuzzy interference result (for example, 0.2 or 0.8 ) and xi are the positions of the centroids of the individual membership functions. The numerator is actually the momentum of the particular individual membership function, with respect to the reference point on the horizontal scale. For example, when the value of velocity is “Fastest” and the value of distance is “Short”, this condition indicates that handoff should be encouraged immediately. Hence, output threshold RSS is “Very High”.

Membership Values

Figure 4. Fuzzy logic system unit in details.

B. Proposed Fuzzy Logic System A fuzzy logic rule base is created based on the known sensitivity of handoff algorithm parameters (e.g., RSS threshold, signal to interference, traffic, etc). Fig. 4 shows the fuzzy logic system in details. The task of the fuzzifier is to map crisp value of distance and velocity to the fuzzy variables. For microcellular system membership functions for the input variable distance and output fuzzy variable Threshold RSS are shown in Figure 6 and Figure 7 respectively. The input fuzzy variable speed is assigned to one of the five fuzzy sets, “Slowest”, “Slow”, “Medium”, “Fast” or “Fastest”. This research utilizes the Mamdani FLS. Figure 5 illustrates the membership functions of the input fuzzy variable velocity for microcellular system. We used triangular and trapezoidal membership functions and considered 50% overlap of assigned fuzzy sets. Table I shows the rules used in the FLS for microcellular system. Similarly, we defined such rules for macrocellular system. The nonsingleton fuzzifier and the center of area (COA) defuzzification method are used. The crisp output of the COA defuzzification method is Pn µA (xi )xi ∗ (13) ZCOA = Pi=1 n i=1 µA (xi )

Threshold RSS (dB)

Figure 7. Membership function of fuzzy variable Threshold RSS (microcellular intrasystem).


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procedures using speed and handoff signaling delay information. Then performance is evaluated as follows: first, value of d is calculated for a desired value of probability of handoff failure Pf using (9) where v is MT’s speed, d is MT’s distance from the boundary of the serving BS, and τ is the handoff signaling delay [2]. Then an approximate value of d can be calculated using pf =

cos−1 vdτ a = tan−1 2d

π 2 π 2

−

− vdτ

√ 2d 4d2 +a2

.

(14)

And the value of false handoff initiation probability can be calculated using ( 2). For CHMP, the value of Sath , when the MT is at a d distance from the cell boundary is given by [2] Sath = 10 log10 [Pr (a − d)].

(15)

where Pr (a−d) is the received power at a known distance (a − d), given by [2] d0 (16) Pr (x) = Pr (d0 )( )α + ². x The typical value of d0 is 1 km for macrocells and 100 m for microcells [2]. The numerical value of Pr (d0 ) depends on different factors, such as frequency, antenna heights, and antenna gains. α is the path loss exponent and its typical value ranges from 3 to 4 and 2 to 8 for a typical macrocellular and microcellular environment, respectively. ² is a zero-mean Gaussian random variable that represents the statistical variation in Pr (x) caused by shadowing. Typical standard deviation of ² is 8 dB [2]. We simulate and analyze the relationship among RSS threshold value, mobile’s speed and handoff signaling delay. Then we make comparison between FLAH and CHMP with respect to these relationships.

Figure 8. RSS threshold (Sath ) for different speed when the OBS belongs to microcellular system.

•

This implies that for a MT with high speed, the handoff initiation should start earlier compared to a slow moving MT to guarantee the desired handoff failure probability to users independent of their speed. Fig. 8 and Fig. 9 also show that Sath increases as τ increases. This is because when τ is high the handoff must start earlier compared to when τ is small. The lower and higher values of τ correspond to intra and inter-system handoff, respectively. Therefore, FLS calculates Sath that is adaptive to both v and τ .

B. Simulation Model For simulation the following scenarios and parameters are considered: a macro-cellular system with cell size, a, of 1 km, a micro-cellular system with cell size, a, of 30m, macro-cell reference distance, d0 , of 100m, microcell reference distance, d0 , of 1 m [2]. We assume that the target handoff failure probability, Pf , is 0.02 and the value of Smin is −64 dBm as assumed in [2]. We consider that the maximum values of users’ speed in micro-cellular and macro-cellular system are 14 km/h and 140 km/h, respectively. C. Simulation Results Relationship between Sath and Speed The relationship between Sath and MT’s speed (v) for different values of handoff signaling delay (τ ) is analyzed. Fig. 8 shows the relationship between Sath and v for different values of τ when the serving BS (OBS) belongs to a micro-cellular system. For Macro-cellular system we got similar result. • Fig. 8 and Fig. 9 show that for particular value of τ , the value of Sath increases as MT’s speed increases. © 2009 ACADEMY PUBLISHER

Figure 9. RSS threshold (Sath ) for different speed when the OBS belongs to macrocellular system.

Relationship between the Handoff Failure Probability of FLS and Speed We calculate Sath using speed and handoff signaling delay information. Then, we use Sath to determine the handoff failure probability. We investigate the handoff

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1

failure probability of different types of intra and intersystem handoff and compare that with the handoff failure probability of CHMP [2]. • Fig. 10 shows the handoff failure probability of FLS and CHMP, for different values of speed when the serving BS belongs to a micro-cellular system. • It shows that reduction in Pf is achieved in FLS compared to CHMP. • Fig. 11 shows the results when the serving BS belongs to a macro-cellular intersystem. Similarly we also investigated the results when the serving BS belongs to a macro-cellular intrasystem which is shown in Fig. 12. Comparison of the results show that when FLS is used and MT’s speed is known Pf is almost constant as the calculated RSS is adaptive to velocity and distance. On the other hand, for a particular value of fixed RSS threshold the numerical value of Pf is different and depends on the numerical value of Sth . This shows that the handoff protocols need to be adaptive irrespective of the type of handoff. • Fig. 11 and Fig. 12 also show that when the speed of the MT is known, 70% to 80% reduction in handoff failure probability is achieved in FLS compared to fixed RSS based Handoff management system.

0.9

Handoff Failure Probability Pf

0.8 0.7 0.6 0.5 0.4

Fuzzy Sth=−63.82dB

0.3

Sth=−63.64dB Sth=−63.47dB

0.2

Sth=−63.10dB

0.1 0

Sth=−62.92dB 0

20

40

60 80 Velocity v in km/h

100

120

140

Figure 11. Relationship between handoff failure probability and speed when the OBS belongs to a macrocellular intersystem for fixed RSS and FLS based handoff.

0.9 Sath = −62.8 dB (CHMP) 0.8

Sath= −62.14 dB (CHMP) Sath = −63.02 dB (CHMP) Sath = −62.8 dB (Fuzzy)

0.7 Handoff Failure Probability Pf

Sath= −62.4 dB (Fuzzy) Sath = −63.07 dB (Fuzzy)

0.6

0.5

0.4

0.3

0.2

0.1

0

0

2

4

6 8 Velocity v in km/h

10

12

Figure 10. Relationship between handoff failure probability and speed when OBS belongs to a microcellular intrasystem for fixed RSS.

Relationship between the Handoff Failure Probability and Handoff Signaling Delay Figure 13 shows the handoff failure probability of FLS for different values of handoff signaling delay (τ ). The results show that unlike the fixed RSS based handoff protocols; Pf remains independent of τ in case of FLS. This is because FLS considers τ for the tuning membership function of dynamic RSS threshold. Fig. 13 shows that 70% − 80% reduction in Pf is achieved in case of FLS compared to the fixed RSS based handoff protocols. The lower and higher values of τ correspond to intra and intersystem handoffs, respectively. Therefore, by incorporating © 2009 ACADEMY PUBLISHER

14

Figure 12. Relationship between handoff failure probability and speed when the OBS belongs to a macrocellular intrasystem for fixed RSS and FLS based handoff.

the estimated value of τ into dynamic RSS, the Pf is limited to the desired value irrespective of users speed and variation of handoff signaling delay. Relationship between False Handoff Initiation Probability and Speed Fig. 14 and Fig. 15 show the comparison of the false handoff initiation probability of FLS with the fixed RSS threshold based algorithms when the serving BS belong to a micro-cellular system and macro-cellular system, respectively. • The false handoff initiation probability of FLS is 5 percent to 15 percent less compared to the fixed RSS threshold based algorithms.


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1

0.57 τ= 1 sec (Fixed RSS) τ= 2 sec (Fixed RSS) τ= 3 sec (Fixed RSS) τ = 1 sec (Fuzzy) τ = 2 sec (Fuzzy) τ = 3 sec (Fuzzy)

0.9

False Handoff Initiation Probability Pa

0.56

Handoff Failure Probability Pf

0.8 0.7 0.6 0.5 0.4 Sth = −63.8 dB Sth = −63.5 dB

0.3

Sth = −63.3 dB 0.2

Sth = −63.1 dB Sth = −62.9 dB

0.1 0

2

3

4 5 6 7 Handoff Signaling Delay τ in seconds

8

0.54 0.53 0.52 0.51 0.5

Fuzzy

1

0.55

9

10

Figure 13. Relationship between handoff failure probability of FLS based handoff and handoff signaling delay.

0.49

0

10

20

30

40 50 60 Velocity v in km/h

70

80

90

100

Figure 15. Relationship between false handoff initiation probability (Fuzzy and Fixed RSS) and speed when the OBS belongs to macrocellular system.

0.72 τ= 1 sec (Fixed RSS) τ= 2 sec (Fixed RSS) τ= 3 sec (Fixed RSS) τ= 1 sec (Fuzzy) τ= 2 sec (Fuzzy)

False Handoff Initiation probability Pa

0.7 0.68 0.66

and handoff signaling delay information to enhance the performance of hierarchical mobile IP (HMIP) handoff significantly. Performance analysis and simulation results show that FLS significantly enhances the performance of both intra and inter-system handoffs. It can estimate the right time to initiate handoff more precisely. It also significantly reduces the cost associated with the false handoff initiation because it achieves lower false handoff initiation probability than existing handoff protocols.

τ= 3 sec (Fuzzy) 0.64 0.62 0.6 0.58 0.56 0.54 0.52

ACKNOWLEDGMENT 4

5

6

7

8 9 10 Velocity v in km/h

11

12

13

Figure 14. Relationship between false handoff initiation probability (Fuzzy and Fixed RSS) and speed when the OBS belongs to microcellular system.

The authors acknowledge the Department of Computer Science and Engineering at Khulna University of Engineering and Technology for all the useful resources benefiting this paper and the reviewers for their detailed comments. R EFERENCES

•

•

Thus, FLS achieves up to 15% reduction in the cost associated with false handoff initiation. The use of adaptive RSS threshold initiates the handoff procedures in such a way that just enough time is there for the successful execution of the handoff. Therefore, an adaptive value of RSS threshold (Sath ) avoids too early or too late initiation of the handoff process. The former limits the value of handoff failure probability. The later ensures that handoff is carried out smoothly. Thus, FLS optimizes the false handoff initiation probability and handoff failure probability. VI. C ONCLUSION

In this paper, a new fuzzy logic based adaptive handoff management algorithm for next generation heterogeneous wireless system is introduced. It uses mobile’s speed © 2009 ACADEMY PUBLISHER

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Presila Israt is currently a Masters student in the school of Information Technology and Electrical Engineering at University of New South Wales at the Australian Defence Force Academy, Canberra, Australia. She was born in Khulna, Bangladesh. She received her B.Sc. degree in Computer Science and Engineering (CSE) from Khulna University of Engineering and Technology (KUET), Bangladesh in March, 2008. Her current research interests include handoff management for next generation wireless systems, adaptive antenna array processing, broadband beamforming and their application to communications. Namvi Chakma received her BS degree in computer science and engineering from Khulna University of Engineering and Technology (KUET), Bangladesh, in 2008. She is currently working with Grameenphone Ltd, Dhaka, Bangladesh. Her current research interests include wireless networks, mobile communications, mobility management and Computer networks. M. M. A. Hashem received the Bachelors degree in Electrical and Electronic Engineering from Khulna University of Engineering and Technology (KUET), Khulna, Bangladesh in 1988, Masters degree in Computer Science from Asian Institute of Technology (AIT), Bangkok, Thailand in 1993 and PhD degree in Artificial Intelligence Systems from the Saga University, Japan in 1999. He is currently a Professor in the Dept. of Computer Science and Engineering, Khulna University of Engineering and Technology (KUET), Bangladesh. His research interest includes Evolutionary Computations, Intelligent Computer Networking, Wireless Networking, Soft Computing, Evolutionary Cluster Computing etc. He has published more than 50 referred articles in international Journals/Conferences. He is a member of IEEE. He is a coauthor of a book titled Evolutionary Computations: New Algorithms and their Applications to Evolutionary Robots, Series: Studies in Fuzziness and Soft Computing, Vol. 147, Springer-Verlag, Berlin/New York, ISBN: 3540-20901-8, (2004).