Research Inventy : International Journal of Engineering and Science

Research Inventy: International Journal Of Engineering And Science
Vol.3, Issue 7 (August 2013), PP 06-12
Issn(e): 2278-4721, Issn(p):2319-6483, Www.Researchinventy.Com
6
Context Aware Fuzzy Rule Based Vertical Handoff Decision
Strategies for Heterogeneous Wireless Networks
1
M.SAZEEDA KAUSAR, 2
DHANARAJ CHEELU
1M.Tech student, CSE Department, Ravindra college of Engineering for women, Kurnool, 518004
2 Faculty of engineering, CSE Department, Ravindra college of Engineering for women, Kurnool, 518004
ABSTRACT : The 4th
generation wireless communication systems aim to provide users with the convenience
of seamless continuous connection to access various wireless technologies and to have the connection with the
best network which provides the best Quality of service. To achieve this goal it is necessary to have a good
decision making algorithm which decides the best network for a specific application under which vertical
handoff should be performed. This paper proposes a QoS aware fuzzy rule based vertical handoff mechanism
Using fuzzy logic quantitative decision algorithm (FQDA) is used as an handoff decision criteria to choose
which network to handover among different available access networks. The QoS parameters considered are
available bandwidth, end-to-end delay, jitter, and bit error rate (BER). Simulation results show that compared
to other vertical handoff algorithms, the proposed algorithm gives better performance for different traffic
classes.
KEYWORDS : Heterogeneous networks, Vertical handoff, Fuzzy Rule Based algorithm, Traffic Classes, QoS
parameters.
I. INTRODUCTION
With the development of wireless communication technology, the service of wireless communication
networks is upgrading extremely fast. Presently, there are many kinds of wireless networks available to fulfil
different needs and requirements of mobile users. When a mobile user is switch from one network to another
network or one base station to another BS, the mechanism is called “Handover”. There are two types of
handover; Horizontal handoff (HHO) and Vertical handoff (VHO).Unlike a horizontal handoff that only occurs
within the same network [1] i.e. when the mobile users switching between the networks with the same
technology (WiFi to WiFi) this process called HHO, where as a vertical handoff occurs in the heterogeneous
wireless network when a mobile user changes its connection between different networks i.e. the mobile users
switched in different networks which have different technology (WiMax to WiFi). So in heterogeneous network
vertical handoff decision (VHD) is mainly used for seamless service with the best network which provides the
best Quality of service. [2]
The traditional horizontal handoff research is emphasized on the received signal strength (RSS)
evaluation of the mobile host (MH). However, in the case of vertical handoff, RSS evaluations and comparisons
are insufficient for making an optimized vertical handoff decision. A comparison between different vertical
handoff algorithms has been presented in [3]. The handoff decision may depend on various parameters including
available bandwidth, bit error rate, jitter, average battery lifetime, access cost, transmit power, and end-to-end
delay. A combination of some of the criteria can be considered for making a decision in handoff [4].Vertical
handoff is generally categorized into three phases [5]: system discovery, vertical handoff decision, and vertical
handoff execution. A good decision making algorithm which decides the best network under which vertical
handoff should be performed. During handover there is a need to decide and choose the best network. So the
Vertical Handoff Decision Making is an important research issue. This paper proposes a QoS aware fuzzy rule
based vertical handoff mechanism Using fuzzy logic quantitative decision algorithm (FQDA) is used as an
handoff decision criteria to choose which network to handover among different available access networks.The
FQDA has three steps: fuzzification, quantitave evaluation and quantitative decision. (Sivanandam et al., 2007) ;
(Xia et al., 2008)
The various handoff strategies used for executing handoff may in general be classified into: Mobile-
Controlled Handoff (MCHO), Network-Controlled Handoff (NCHO), and Mobile-Assisted Handoff (MAHO).
In MCHO, the mobile node continuously monitors the signal of access points and initiates the handoff
procedure when some handoff criteria are satisfied. NCHO is a centralized handoff protocol, in which
network makes handoff decision based on measurements of the signal quality of mobile station (MS) at a

Context Aware Fuzzy Rule Based…
7
number of base stations (BS). In MAHO, a mobile node measures the signal strength of surrounding base
stations and then decides whether or not to initiate the handoff procedures. MCHO has a low complexity in
terms of network equipment. However, latency and loss of large number of packets during inter-subnet
handoff can be high. Handoff decision in MAHO is made by the network for coordination among mobile
nodes and global optimization. However, in heterogeneous wireless access networks only the mobile nodes
have the knowledge about the kind of interfaces they are equipped with; so the network dependency on the
mobile node is high. Therefore, MCHO with some assistance from the networks is better suited for
implementing vertical handoff.
The QoS aware fuzzy rule based handoff mechanism proposed in this paper assumes MCHO
and some assistance from the network, where a mobile will periodically monitor the available networks and
using a fuzzy rule based algorithm it determines the best network. This information is then communicated to
the current network for executing the handoff. The rest of the paper is organised as follows: the background
and related work on vertical handoff decision are in section II, the proposed algorithm is presented in Section
III, Section IV contains the simulation results and Section V concludes this paper.
II. BACKGROUND AND RELATED WORK
In heterogeneous wireless networks, three phases are available for performing a handoff. The MT will
have a choice of several networks to which it can connect to. But the outcome of the decision phase of the VHO,
which is dependent on several parameters like available bandwidth, battery power status of the mobile terminal,
cost, received signal strength etc will decide on the network to which a connection will be made. RSS has a
great role in the horizontal decision process due to its compatibility between the current attachment point and
that of the candidate attachment points. But in VHO, the RSS are incomparable due to asymmetrical nature of
the heterogeneous networks. However, it can be used to determine the availability as well as the condition of
different networks. If more than one candidate network is available, the MT should associate itself with the one
having the strongest RSS as it does in HHO. Considerable work has been done in literature to determine the
appropriate parameters that can be considered in the decision process for VHO.
In [6], the authors have proposed a vertical handoff decision (VHD) algorithm that maximizes the
overall battery lifetime of the mobile terminal in the same coverage area and also aims at equally distributing the
traffic load across the networks. This algorithm when implemented in multiple Vertical Handoff Decision
Controllers (VHDC) located in the access networks can provide the VHD function for a region covering one or
multiple APs or BSs. In [7], a decision method called ALIVE –HO(adaptive lifetime-based vertical handoff) is
proposed which is based on the Received Signal Strength (RSS).This parameter is used to estimate coverage of
the wireless network and the best network is selected using vertical handoff algorithms. ALIVE- HO algorithm
dynamically adopts to the Mobile Terminals (MT) velocity to decrease the unnecessary number of handoffs and
ping pong effect but the probability of handoff increases with the distance from the AP. It is also established that
the number of unnecessary handoffs using ALIVE handoff algorithm is less than that of algorithms based on
traditional RSS hysteresis. According to the authors, the simplest method to increase RSS is to increase the
transmit power, which needs further investigation, since an increase in transmit power might lead to an increase
in interference leading to a decrease in QoS.
Both QoS parameters and handoff metrics are required for vertical handoff decision [8]. The handoff
metrics and QoS parameters are categorized under different groups (e.g., bandwidth, latency, power, price,
security, reliability, availability). Various vertical handoff decision mechanisms have been proposed recently. In
[9], the handoff decision mechanism is formulated as an optimization problem. Each candidate network is
associated with a cost function. The decision is to select the network which has the lowest cost value. The cost
function depends on a number of criteria, including the bandwidth, delay and power requirement. Appropriate
weight factor is assigned to each criterion to account for its importance. In [10], an Active Application Oriented
(AOO) vertical handoff decision mechanism is proposed. The decision mechanism considers the QoS
parameters required for the applications (e.g., minimum and maximum bandwidth requirement for voice
service). Each candidate network is associated with a utility function. The chosen network is the one which
provides the highest utility value. The utilization function is a weighted sum of various normalized QoS
parameters.
The decision about access network selection in a heterogeneous wireless environment can be solved
using specific multiple attribute decision making (MADM) algorithms such as Technique for Order Preference
by Similarity to Ideal Solution (TOPSIS), Weighted Product Model (WPM), Weighted Sum Model (WSM),
Analytic Hierarchy Process (AHP), and Grey Relational Analysis (GRA). An integrated AHP and GRA

8
algorithm for network selection is presented in [11] with a number of parameters. In [12], Pahlavan et al. present
a neural networks-based approach to detect signal decay and making handoff decision. Stevens et. al have
selected parameters such as bandwidth, delay, jitter and bit error rate (BER) to conduct their comparisons of
some of the prominent decision algorithms in literature, that is, simple additive weighting (SAW), technique for
order preference by similarity to ideal solution (TOPSIS), multiplicative exponent weighting (MEW) and the
grey relational analysis (GRA).Good performance improvement of SAW and GRA over several vertical handoff
decision algorithms has been obtained. The GRA decision algorithm provided a slightly higher bandwidth and
lower delay for interactive and background traffic classes while MEW, SAW and TOPSIS provided almost
similar performance In [13], Chan et al. propose a mobility management in a packet-oriented multi-segment
using Mobile IP and fuzzy logic concepts.
Handover is separated into initiation, decision and execution phases. MIP is used in the execution
phase, fuzzy logic is applied to the initiation phase, and fuzzy logic and multiple objective decision making
concepts are applied during the decision phase to select an optimum network. W. Zhang, in [14], proposes that
the vertical handoff decision is formulated as a fuzzy multiple attribute decision-making (MADM) problem.
Fuzzy logic is used to represent the imprecise information of some attributes of the networks and the preferences
of the user. In [15], Pramod Goyal, and S. K. Saxena proposes the Dynamic Decision Model, for performing the
vertical handoffs to the “Best” interface at the “best” time moment, successfully and efficiently. They proposed
Dynamic Decision Model for VHO which adopts a three phase approach comprising Priority phase, Normal
phase and Decision phase. In [16], a Markov decision process (MDP) approach for vertical handoff decision
making problem is proposed. This MDP approach takes into account multiple factors such as user preference,
network conditions, and device capability. Although there have been various vertical handoff decision
algorithms proposed, most of them applied through Fuzzy logic theory based quantitative decision algorithm
(FQDA) (Sivanandam et al., 2007) has an advantage over traditional fuzzy logic algorithm which there is no
need to establish a database to store rule bases. In this paper, we presented a vertical handoff scheme satisfying
between user requirement and network conditions and avoiding unnecessary handoffs as well.
III.QOS AWARE FUZZY RULE BASED VERTICAL HANDOFF DECISION ALGORITHM
To process vertical handoff-related parameters, we use fuzzy logic, which mimics the human mind and
uses approximate modes of reasoning to tolerate vague and imprecise data. Fuzzy logic inference systems
express mapping rules in terms of linguistic language. A Mamdani FIS can be used for computing accurately the
handoff factor which determines whether a handoff initiation is necessary among the networks.
A fuzzy logic inference system can be implemented in the MT as a Handoff Initiation Engine to provide rules
for decision making. We use a Mamdani FIS that is composed of the functional blocks.
 A fuzzifier which transforms the crisp inputs into degrees of match with linguistic values;
 A fuzzy rule base which contains a number of fuzzy IF-THEN rules;
 A database which defines the membership functions of the fuzzy sets used in the fuzzy rules;
 A fuzzy inference engine which performs the inference operations on the fuzzy rules;
 A defuzzifier which transforms the fuzzy results of the inference into a crisp output.
Suppose that a MT that is connected to a UMTS network detects a new WLAN. It calculates the handoff
factor which determines whether the MT should handoff to the WLAN. We use as input parameters bandwidth,
end to end delay, jitter and Bit error rate perceived QoS of the target WLAN network. The crisp values of the
input parameters are fed into a fuzzifier in a Mamdani FIS, which transforms them into fuzzy sets by
determining the degree to which they belong to each of the appropriate fuzzy sets via membership functions
(MFs). Next, the fuzzy sets are fed into a fuzzy inference engine where a set of fuzzy IF-THEN rules is applied
to obtain fuzzy decision sets. The output fuzzy decision sets are aggregated into a single fuzzy set and passed to
the defuzzifier (centroid method) to be converted into a precise quantity, the handoff factor, which determines
whether a handoff is necessary. When handoff is required, a mobile calculates the handoff score value for all
available networks with a set of input parameters by using the fuzzy rule based scheme proposed above, and
selects the best one. The information about the best network is then communicated to the current network to
execute the handoff. The block diagram shown in Figure 1 describes the vertical handoff decision algorithm.

9
Fig 1: Block diagram of predicted parameters for vertical handoff decision algorithm.
In the proposed QoS aware fuzzy rule based algorithm, the network types assumed are an UMTS network, a
GPRS network, and a WLAN. The parameters like possible data rates for bandwidth vector, and typical delay
and jitter values assumed for these networks are based on the standard values. The simulation is carried out
using Matlab and results are plotted in Fig. 4,5 & 6.
The bandwidth vector for network 1 (UMTS) with [32, 64, 128, 256, 512, 1024, 2048] kbps;
network 2 (GPRS) with [21, 42, 64, 85, 107, 128, 149, 171] kbps;
and network 3 (WLAN) with [1, 2, 5.5, 11] Mbps.
The delay vector for network 1 with [190, 160, 130, 100, 70, 40, 10] ms;
network 2 with [185, 160, 135, 110, 85, 60, 35, 10] ms; and
network 3 with [160, 110, 60, 10] ms.
The three networks use the same vectors for jitter and Bit error rate as: [3, 5, 7, 9, 11] msecs and [0.01, 0.001,
0.0001, 0.00001, 0.000001]
The membership functions for different input parameters are considered as triangular functions with
three different regions: low, medium and high. The output membership function is also assumed to be a
triangular function as shown in Fig. 2.
Fig 2: Fuzzy input and output membership functions
The universe of discourse for the variable Handoff Factor is defined from 0 to 1, with the maximum
membership of the sets “Lower” and “Higher” at 0 and 1, respectively. Since there are four fuzzy input variables
and three fuzzy sets for each fuzzy variable, the maximum possible number of rules in our rule base are 34
= 81.
Due to space limitation we show only 4 rules for each traffic class in Table I.
Conversational
Rule No Bandwidth E2E Delay Jitter BER Handoff factor
1 Low Low Low Low High
25 Low High High Low Low
50 Moderate High Moderate Moderate Low
81 High High High High Low
streaming
1 Low Low Low Low Low
Inputs
Bandwidth
End to End
delay
Jitter
Bit Error Rate
Fuzzifier
Interference
engine
Defuzzifier
Knowledge base
Database Rule
base
Handoff
score is
less
Selects as
the best
network

10
50 Moderate High Moderate Moderate High
81 High High High High Moderate
Interactive
1 Low Low Low Low Moderate
50 Moderate High Moderate Moderate Low
81 High High High High Low
Background
1 Low Low Low Low Moderate
25 Low High High Low Moderate
50 Moderate High Moderate Moderate Moderate
81 High High High High Moderate
TABLE I RULE BASE FOR EACH TRAFFIC CLASS
The crisp handoff factor computed after defuzzification is used to determine when a handoff is
required, then initiate handoff; otherwise do nothing. When handoff is required, a mobile calculates the handoff
score value for all available networks with a set of input parameters by using the fuzzy rule based scheme
proposed above, the handoff factor which is less among the networks will be selected as the best one.
IV.SIMULATION AND RESULTS
We considered four traffic classes defined by 3GPP [10], which are conversational, streaming,
interactive and background. Each traffic class is associated with different QoS parameters. The QoS
parameters considered are available bandwidth, end-to-end delay (E2E delay), bit error rate (BER) and jitter
with the corresponding importance weight for each traffic class computed using the Analytical Hierarchical
Processing (AHP), are shown in TABLE II.
Traffic Class Bandwidth E2E
Delay
Jitter BER
Conversational 0.04999 0.45003 0.45003 0.04999
Streaming 0.03738 0.11381 0.42442 0.42442
Interactive 0.63594 0.16052 0.04305 0.16052
Background 0.66933 0.05547 0.05547 0.21977
TABLE II. IMPORTANCE WEIGHTS OF EACH QOS PARAMETERS FOR DIFFERENT
TRAFFIC CLASSES
The simulation is carried out using Matlab and results are plotted in Fig 3. Results obtained using the
proposed technique show better performance for E2EDelay, availability of the network. The Average available
bandwidth is also obtained for Streaming and background classes. The available bandwidth performance is
moderate while satisfying the E2EDelay and availability requirements. Rules can be more properly tuned to get
even better performance in available bandwidth. Fuzzy rule based approach based on awareness of QoS
requirements of the traffic classes makes clear decision to do the handoff among the networks, which uses the
fuzzy membership functions defined in the Fig.2.The fuzzy membership regions helps to make a clear
distinction among the parameter values of the network and fuzzy rule base will be used to infer the result
of handoff score value.
(a)

11
(c)
Fig 3 : (a) Avg E2Edelay (b) availability (c) Avg. Available Bandwidth
V. CONCLUSION
In this paper, we considered two sets of networks for simulation. The paper proposes QoS aware
fuzzy rule based algorithm with multi-criteria of bandwidth, delay, jitter and bit error rate by considering
different traffic classes. Simulation results show that fuzzy rule based approach gives better delay, availability,
with moderate performance of available bandwidth. Hence, QoS aware fuzzy rule based algorithm gives better
QoS performance for delay sensitive applications like conversational, interactive and live streaming
applications. Our future work will consider minimal fuzzy rule set based vertical handoff algorithms for
heterogeneous wireless networks.
REFERENCES
[1] Q-A. Zeng and D. P. Agrawal, "Modeling And Efficient Handling Of Handoffs In Integrated Wireless Mobile Networking",
IEEE Transactions on Vehicular Technology, Vol. 51, No.6, Nov. 2002, pp. 1469-1478.
[2] N.Nasser, A.Hasswa and H.Hassanein, “Handoff in Fourth Generation Heterogeneous Networks”, IEEE communication
Magazine, Vol.44, pp. 96-103, Oct 2006
[3] Enrique Stevens-Navarro and Vincent W.S. Wong, “Comparison between Vertical Handoff Decision Algorithms for
Heterogeneous Wireless Networks”, IEEE VTC, vol 2, pages: 947-951, 2006
[4] Nirmala Shenoy, Sumita Mishra, "Vertical handoff and mobility management for seamless integration of heterogeneous wireless
access technologies” in 'Heterogeneous Wireless Access Networks: Architectures and Protocols' published by Springer Verlag
2008
[5] W. Chen, J. Liu, and H. Huang, “An Adaptive Scheme for Vertical Handoff in Wireless Overlay Networks,” in Proc. of
ICPAD’04, Newport Beach, CA, July 2004.
[6] SuKyoung Lee, Kotikalapudi Sriram, Kyungsoo Kim, Yoon Hyuk Kim, and Nada Golmie,”Vertical Handoff Decision
Algorithms for Providing Optimized Performance in Heterogeneous Wireless Networks“, IEEE transactions on vehicular
technology, January 2009
[7] Muhammad Amir Latif, Abid Aziz Khan “quality of service during vertical handoff in 3g/4g wireless networks”, Blekinge
Institute of Technology, 2009

12
[8] J.McNair and F. Zhu,“Vertical Handoffs in Fourth-generation Multinetwork Environments,” IEEE Wireless Comm., vol. 11, no.
3, June 2004.
[9] F. Zhu and J.MacNair, “Optimizations for Vertical Handoff Decision Algorithms,” in Proc. IEEE WCNC’04, Atlanta, GA,
March 2004.
[10] W. Chen and Y. Shu, “Active Application Oriented Vertical Handoff in Next Generation Wireless Networks,” in Proc. IEEE
WCNC’05, New Orleans, LA, March 2005.
[11] Q. Song and A. Jamalipour, “Network Selection in an Integrated Wireless LAN and UMTS Environment using Mathematical
Modeling and Computing Techniques”, IEEE Wireless Communications, June 2005, pp. 42-48.
[12] K. Pahlavan et al., “Handoff in Hybrid Mobile Data Networks”, IEEE Personal Communications, April 2000, pp. 34-47.
[13] P. M. L. Chan et al., “ Mobility Management Incorporating Fuzzy Logic for a Heterogeneous IP Environment”, IEEE
Communications Magazine, December 2001, pp. 42-51.
[14] W. Zhang, “Handover Decision Using Fuzzy MADM In Heterogeneous Networks,” in Proc. IEEE WCNC, Atlanta, GA, Mar.
2004, pp. 653–658.
[15] Pramod Goyal, and S. K. Saxena, “A Dynamic Decision Model For Vertical Handoffs Across Heterogeneous Wireless
Networks” proceedings of World Academy of Science, Engineering & Technology Vol. 31 July 2008, pp 677-682.
[16] J.Wang, R. V. Prasad, and I. Niemegeers, “Solving the Incertitude of Vertical Handovers in Heterogeneous MobileWireless
Network Using MDP,” in Proc. of IEEE ICC’08, Beijing, China, May 2008.

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Research Inventy : International Journal of Engineering and Science