a stable clustering algorithm based on battery power for mobile ad hoc networks
TRANSCRIPT
A Stable Clustering Algorithm Based on Battery
Power for Mobile Ad Hoc Networks
Pi-Rong Sheu* and Chia-Wei Wang
Department of Electrical Engineering, National Yunlin University of Science & Technology,
Touliu, Yunlin 640, Taiwan, R.O.C.
Abstract
Recently, extensive research efforts have been devoted to the design of clustering algorithms to
organize all the hosts in a mobile ad hoc network into a clustering architecture. However, due to the
dynamic nature of the mobile hosts, their association with and dissociation from clusters disturb the
stability of the network, making reconfiguration of cluster heads unavoidable. Re-computation of
cluster heads and frequent information exchange among the participating hosts will suffer high
computation overheads. Therefore, it is obvious that a more stable clustering architecture will directly
lead to the performance improvement of the whole network. In this paper, we will propose an efficient
clustering algorithm that can establish a stable clustering architecture by keeping a host with weak
battery power from being elected as a cluster head. Computer simulations show that the clustering
architectures generated by our clustering algorithm are more stable than those generated by other
clustering algorithms.
Key Words: Ad Hoc Network, Battery Power, Clustering Algorithm, Clustering Architecture
1. Introduction
A mobile ad hoc network (MANET) is formed by a
group of mobile hosts (or called mobile nodes) without
an infrastructure consisting of a set of fixed base stations
[1]. A mobile host in a MANET can act as a general host
as well as a router; i.e., it can generate as well as forward
packets. Two mobile hosts in such a network can com-
municate directly with each other through a single-hop
route in the shared wireless media if their positions are
close enough. Otherwise, they need a multi-hop route to
finish their communications. In a multi-hop route, the
packets sent by a source are relayed by multiple interme-
diate hosts before reaching their destination. MANETs
are found in applications such as short-term events, bat-
tlefield communications, disaster relief activities, and so
on. Undoubtedly, MANETs play a critical role in situa-
tions where a wired infrastructure is neither available nor
easy to install.
The research of MANETs has attracted a lot of atten-
tion recently [1]. Since host mobility causes frequent un-
predictable topological changes, efforts have been devo-
ted in particular to the design of clustering strategies to
organize all the hosts in a MANET into a clustering ar-
chitecture. This way, the transmission overheads for the
update of routing tables after topological changes can be
reduced [2�5]. In fact, research has demonstrated that
routing on top of clustering architectures is much more
scalable than flat routing [2�5]. In addition, a clustering
architecture can facilitate spatial reuse of resources to in-
crease network capacity [6,7]. For example, under a non-
overlapping clustering architecture, two clusters may use
the same frequency or code set if they are not adjacent.
Furthermore, in a clustering architecture, when a mobile
host changes its position, it is sufficient only for the hosts
within its cluster to update their topology information,
but not for all the hosts in this network.
In appearance, a clustering architecture is similar to a
Tamkang Journal of Science and Engineering, Vol. 9, No 3, pp. 233�242 (2006) 233
*Corresponding author. E-mail:[email protected]
single-hop cellular architecture [6,7]. Figure 1 shows a
clustering architecture for a MANET. There exists a link
between two nodes if the two nodes are within the trans-
mission range of each other. The black nodes denote the
cluster heads of the clustering architecture. Nodes within
a circle belong to the same cluster. Each node in a MANET
is assigned a unique identifier (ID) that is a positive inte-
ger. We assume a cluster’s ID to be the same as its cluster
head’s ID. For example, the ID of the cluster with node
15 as its cluster head is C15. Nodes 3, 8, 13, 15, and 16 all
belong to cluster C15. A cluster head in each cluster acts
as a coordinator to resolve channel assignment, perform
power control, maintain time division frame synchroni-
zation, and enhance spatial reuse of bandwidth. The ma-
jor characteristics of a clustering architecture are as fol-
lows. Firstly, there is only one cluster head in each clus-
ter. Secondly, each node in a clustering architecture is ei-
ther a cluster head or adjacent to one or more cluster he-
ads. A node belonging to two or more clusters is called a
gateway. Communication between any two adjacent clu-
sters has to rely on their common gateway. Thirdly, any
two cluster heads are not adjacent to each other. Finally,
any two nodes in the same cluster are at most two hops
away from each other.
Clustering architectures can be classified into two cat-
egories: overlapping and non-overlapping. In overlap-
ping clustering architectures [6], a node which is not a
cluster head may belong to more than one cluster; such a
node is named a gateway. Nodes which belong to only
one cluster are ordinary nodes. For example, in Figure 1,
nodes 8, 12 and 13 are gateways; the other white nodes
are ordinary nodes. In non-overlapping clustering archi-
tectures [8�11], a node which is not a cluster head be-
longs to only one cluster; such a node is named an ordi-
nary node. No gateway exists in this kind of architecture.
In fact, even in overlapping clustering architectures, a ga-
teway is not necessarily existent between any two clus-
ters (e.g., no gateway exists between cluster C2 and clus-
ter C6 in Figure 1). To support the function of a gateway,
the concept of distributed gateway (DG) has been pro-
posed [6]. A DG is a linked pair of ordinary nodes yet be-
longs to different clusters. In Figure 1, for example, the
pair of nodes 9 and 10 forms a DG. One of the advantages
of using DGs is that the hop counts of a route may be re-
duced. Take again Figure 1 for example. The route from
node 2 to node 6 will be {2, 8, 15, 13, 5, 12, 6} if DGs are
not introduced. On the other hand, when nodes 9 and 10
form a DG, the route can be reduced to {2, 9, 10, 6}. To
simplify our discussion, only non-overlapping clustering
architectures are considered in this paper.
Due to the dynamic nature of the mobile hosts, both
their integration and disintegration will disrupt the stabil-
ity of the network, calling for reconfiguration of cluster
heads. This feature needs attention, since re-computation
of cluster heads and frequent information exchange among
the participating hosts will result in high computation
overheads. Worse, frequent cluster head changes adver-
sely affect the performance of other dependent protocols
such as scheduling, routing, and resource allocation. In
fact, in a MANET that uses scalable cluster-based ser-
vices, the network performance metrics such as through-
put and delay are coupled with the frequency of cluster
reorganization. Therefore, it is obvious that a more stable
clustering architecture will directly lead to a better per-
formance of the whole MANET. In light of this, this pa-
per aims to build a stable clustering architecture.
In this paper, we will propose an efficient clustering
algorithm that can establish a stable clustering architec-
ture by keeping a node with weak battery power from be-
ing elected as a cluster head. A clustering architecture is
more stable if it can be held for a longer period of time.
To be more specific, a stable clustering architecture has a
longer clustering architecture lifetime, which is defined
in this paper as the duration from the time when the clus-
tering architecture is constructed until any cluster head in
the architecture runs out of its battery power (Another
metric used frequently to evaluate the stability of a net-
work is the so-called network lifetime, which is defined
in this paper as the duration during which the clustering
234 Pi-Rong Sheu and Chia-Wei Wang
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C5C15
C2
C6
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17Distributed gateway
Figure 1. A clustering architecture.
architecture is constructed until any node in the network
runs out of its battery power. We will also use this metric
to measure our clustering algorithm). Computer simula-
tions show that the clustering architectures generated by
our clustering algorithm are more stable than those gen-
erated by other clustering algorithms.
The rest of this paper is organized as follows. In Sec-
tion 2, related researches are reviewed. In Section 3, our
proposed stable clustering algorithm is presented. In Sec-
tion 4, the performance of our clustering algorithm is eva-
luated by computer simulations. Finally, in Section 5,
some conclusions will be drawn.
2. Related Researches
In this section, the related researches will be summa-
rized.
2.1 Stability in Clustering Algorithms
Because each host in a MANET is mobile and the
MANET works mainly on battery power, the topology
may change dynamically and the power may be exhaust-
ed. In order to reduce the cluster maintenance overheads
and to provide a more stable architecture for upper layer
protocols [11], stability of clustering architectures should
be taken seriously. Like many existing stable routing pro-
tocols, whose objective is to find routes consisting of
links and nodes with higher stability [12�15], clustering
algorithms must also deal with link stability as well as
node stability. In general, a link with higher received pow-
er is considered to have higher link stability. Similarly, a
node with higher battery power is considered to have
higher node stability [16,17]. Due to the limitation of
space, in the following we will only consider node stabil-
ity, which is measured only according to its battery pow-
er. When a node uses up its battery power, the communi-
cation between itself and any other node will be termi-
nated. Similarly, when a cluster head exhausts its battery
and becomes inactive, the cluster which it belongs to will
beak up. Obviously, the battery power of a node is very
important in keeping a MANET functioning. Since a
cluster head acts as a coordinator in a cluster, it will take
more tasks, which in turn will cause its battery power to
consume more rapidly. When a cluster breaks up for this
reason, the clustering architecture has to be reconfigured,
which will bring in high computation overheads. Intuiti-
vely, to reduce such overheads, we should always choose
nodes with larger battery power as cluster heads so that
the lifetime of the clustering architecture may be extend-
ed. However, in our study, we discover that a more effi-
cient way to form a stable clustering architecture is to avo-
id as much as possible choosing a node with little battery
power as a cluster head.
2.2 Related Clustering Algorithms
Before elaborating on our idea, let us first review four
existing clustering algorithms. The first three do not take
the factor of stability into consideration while the last
does. In the lowest-ID clustering algorithm [6], each no-
de is assigned a distinct ID. A node with ID lower than
those of its neighbors will be elected as a cluster head.
The lowest-ID neighbor of a node is its cluster head. In
the highest-connectivity clustering algorithm [6], a node
that has not chosen its cluster head is an “uncovered”
node; otherwise, it is a “covered” node. A node is elected
as a cluster head if it is the most highly connected node
among its “uncovered” neighbors (if there is a tie, the
node with the lowest ID prevails). To reduce clustering
and maintenance overheads, the access-based clustering
protocol (ABCP) [11] relies on the feature of MAC layer
to do the clustering. The ABCP can provide a rapidly de-
ployed clustering architecture for upper layer protocols.
The cluster head election criterion of the ABCP is based
on its multiple-access scheme on control channel. Each
node accesses the control channel to declare its intention
to be a cluster head. A node which successfully sends a
cluster head declaration before its one-hop neighbors do
will become the cluster head.
The distributed and mobility-adaptive clustering al-
gorithm (DMACA) [8�10] is a generalization of the low-
est-ID clustering algorithm. In the DMACA, each node
is assigned a weight, which becomes the criterion of ele-
cting a cluster head. The weight of a node can be given
according to its some qualities such as its mobility, its
processing power, and so on. Obviously, if the weight of
each node is set to its ID (degree), the DMACA will be-
come the lowest-ID (the highest-connectivity) clustering
algorithm. At first glance, the DMACA seems to be able
to successfully solve the stability problem. This is be-
cause if the battery power is taken as the node weight,
nodes with larger battery power will have a higher proba-
bility of becoming cluster heads. Thus, the stability of
A Stable Clustering Algorithm Based on Battery Power for Mobile Ad Hoc Networks 235
the whole clustering architecture seems to become high-
er. However, this is not always true. As an example, let
us consider the MANET in Figure 2. The number within
each node represents its battery power while the number
beside each node represents its ID; e.g., the battery pow-
er of node 1 is 4, the battery power of node 2 is 1, and so
on. When the weight of a node in the DMACA is set to its
battery power, the resulted clustering architecture is shown
in Figure 3, where nodes 1, 2, 3, 7, 8, and 10 have been
elected as cluster heads. Observe that among these clus-
ter heads, nodes 2 and 3 have low battery power. There-
fore, the resulted clustering architecture will be very un-
stable.
3. Our Proposed Clustering Algorithm
In this section, we will propose a new clustering al-
gorithm to form a stable clustering architecture. We de-
fine a bottleneck node to be a node with battery power
lower than a predefined value Ethreshold (the value of Ethresh-
old is determined by computer simulations and will be dis-
cussed in Section 4). When a bottleneck node is elected
as a cluster head, it is named a bottleneck cluster head.
We think that if a clustering architecture has fewer bottle-
neck cluster heads, it may have a longer lifetime. Thus,
the basic idea behind our clustering algorithm is that a
node will have a higher probability to become a cluster
head if it has more neighboring nodes that are bottleneck
nodes.
The outline of our whole clustering algorithm is as
follows. Each node in a MANET broadcasts its beacon
packets periodically to declare its existence. The beacon
packet of a node carries its battery power and cluster ID.
Thus, each node can obtain the number of non-clustered
neighbors who are bottleneck nodes (we name these ne-
ighbors bottleneck neighbors) by its received beacons.
Next, nodes with more bottleneck neighbors will be ele-
cted as cluster heads. However, different nodes may have
the same number of bottleneck neighbors. In this case,
ties may arise. Therefore, a secondary election criterion,
the battery power of a node, needs to be introduced to
solve such ties. The second election criterion is also help-
ful in maintaining a longer lifetime of the elected cluster
head. Finally, if a tie still happens, the node with lower
ID is preferred.
Before describing our clustering algorithm in detail,
we make the following assumptions, which are common
in designing clustering algorithms for MANETs [6�10]:
1. The network topology is static during the execu-
tion of the clustering algorithm.
2. A packet broadcasted by a node can be received
correctly by all its one-hop neighbors within a fi-
nite time.
3. Each node has a unique ID and knows its degree
(the number of its one-hop neighbors). At the same
time, each node knows the ID and the degree of its
every one-hop neighbor.
Next, in addition to the beacon packet, we will define
three different kinds of packets and two types of tables
used in our clustering algorithm. The most important pa-
cket in our clustering algorithm is the CRITERION (#_
of_bottleneck, battery_power, id) packet, which is broad-
casted by each node. Parameter #_of_bottleneck is the
number of non-clustered bottleneck neighbors of the no-
de, battery_power is the battery power of the node, and
id is the node’s ID. The CH (cid) packet is used by a node
to declare itself as a cluster head. Parameter cid is a no-
de’s cluster ID and is initially zero. The JOIN (cid, id)
236 Pi-Rong Sheu and Chia-Wei Wang
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Figure 2. A MANET with battery power in each node.
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Figure 3. A clustering architecture established by DMACAfrom Figure 2.
packet is used by a node to inform the cluster head which
it wants to join, where cid is the cluster ID of the cluster
which it wants to join and id is its own node ID. Each
cluster head uses a Member_Table to record its cluster
members. Each node uses an NC_Neighbor_Table to re-
cord its non-clustered neighbors. Each time a beacon pa-
cket with cid equal to zero is received, a node will update
its NC_Neighbor_Table.
Our clustering algorithm:
� Each node broadcasts its own a CRITERION (#_
of_bottleneck, battery_power, id) packet to all its
neighboring nodes and receives multiple CRITE-
RION (#_of_bottleneck, battery_power, id) pack-
ets from its neighbors.
� If a node discovers that it has a larger #_of_bottle-
neck than those of the received CRITERION (#_
of_bottleneck, battery_power, id) packets, then it
sets its cid to its own ID and broadcasts a CH (cid)
packet to declare itself as a cluster head. If there is
a tie, then the cluster head will be the one with the
largest battery_power. If there is still a tie, the
node with the highest ID will be the final winner.
� When a node receives multiple CH (cid) packets
and dose not belong to any cluster (i.e., its cid = 0),
it will select the cluster head with the highest bat-
tery_power as its cluster head and set its cid to that
of the received CH (cid) packet from its selected
cluster head. Next the node broadcasts a JOIN (cid,
id) packet to join the cluster.
� When a cluster head receives a JOIN (cid, id) pa-
cket with cid equal to its own cid, it will record the
id in the received JOIN (cid, id) packet in its Mem-
ber_Table.
� When a non-clustered node receives a JOIN (cid,
id) packet, it will remove the node with id equal to
the id of the received JOIN (cid, id) packet from its
NC_Neighbor_Table.
� Each time a non-clustered node removes a node
from its NC_Neighbor_Table, it will check whe-
ther its NC_Neighbor_Table becomes empty or
not. If empty, it sets its cid to its own node id and
broadcasts a CH (cid) packet to declare itself as an
orphan cluster.
� A node will terminate the clustering algorithm when
it has joined or formed a cluster (i.e., its cid � 0).
Now, let us use the MANET in Figure 2 as an exam-
ple to illustrate the operation of our clustering algorithm.
Figure 4 shows the resulted clustering architecture when
Ethreshold = 1.5 and our clustering algorithm is applied to
the MANET in Figure 2. Observe that node 6 has two
bottleneck neighbors, that each of node 4 and node 5 has
one bottleneck neighbor individually, and that each of
the other nodes has no bottleneck neighbors. Thus, node
4, node 5, and node 6 will have higher priorities to be-
come cluster heads. Although both node 1 and node 9
have no bottlenecks, node 1 rather than node 9 is elected
as a cluster head because the former has larger battery
power than the latter. Note that no bottleneck cluster he-
ad exists in Figure 4 while two bottleneck cluster heads
(node 2 and node 3) appear in Figure 3.
4. Computer Simulations
In this section, we will evaluate the stability of our
clustering algorithm and compare it with those of ABCP
and DMACA. We select ABCP as a representative of
clustering algorithms that do not take the stability into
consideration. The choice of ABCP over the lowest-ID
and highest-connectivity clustering algorithms is based
on the fact that the last two clustering algorithms are in-
deed special cases of DMACA. For DMACA, a node’s
battery power is adopted as its node weight.
4.1 Power Model
According to the path-loss model [18,19], the trans-
mission power consumption of a link can be expressed as
A Stable Clustering Algorithm Based on Battery Power for Mobile Ad Hoc Networks 237
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1.5thresholdE �
Figure 4. A clustering architecture established by our cluster-ing algorithm from Figure 2.
a function of the distance between its endpoints. To be
more specific, the power required to transmit a packet
along link (vi, vj) is approximately equal to di j,
2 � �, where
di j,
2 denotes the distance between node vi and node vj, � is
a constant. The path-loss model also indicates that the
power required to receive a packet does not depend on
the length of the link, and can be modeled by a constant
�. In our computer simulations, for simplicity, we only
consider transmitting power and set � to 1. Thus, if node
vi transmits � packets to node vj, it will consume di j,
2 � �
units of power.
4.2 Simulation Environments
The environment of our simulations is assumed to be
a static network. Each mobile host is randomly distrib-
uted in 1200 � 1200 m2 physical area. The transmission
range of each mobile host is the same and fixed value 200
m. Five different network sizes are taken into account:
40-node, 60-node, 80-node, 100-node, and 120-node.
Each numerical value is obtained from an average value
of 100 times of simulations. In the beginning, each node
is given 10000~100000 units of battery power. The dis-
tribution of node’s initial battery power is based on two
different kinds of battery power distributions. In the first
scenario, 25% of the total nodes have battery power low-
er than 50000. In the second scenario, 50% of the total
nodes have battery power lower than 50000.
In a cluster, we assume that each ordinary node vi
will consume � i id� 2 units of battery power to transmit
or relay �i packets to its cluster head, where di denotes
the distance between node vi and its cluster head. The
transmission rate of each ordinary node is between 1 and
10 packets per second. Because a cluster head plays a
role as a coordinator in its cluster, it is feasible to assume
that a cluster head must process more tasks and thus
needs to consume more battery power than an ordinary
node. In our computer simulations, the power consump-
tion of a cluster head is assumed to be � � i id � 2,
where � � i is the number of packets per second from
all its ordinary nodes to the cluster head and di denotes
the average distance between the cluster head and its mem-
bers.
4.3 Performance Metrics
The performance metrics we will observe are 1) the
lifetime of clustering architecture, 2) the lifetime of net-
work, 3) the battery power of each cluster head, and 4)
the number of cluster heads. Recall that the clustering ar-
chitecture lifetime is defined as the duration from the
time when the clustering architecture is constructed until
any cluster head in the architecture runs out of its battery
power. The network lifetime is defined as the duration
from the time when the clustering architecture is con-
structed until any node in the network runs out of its bat-
tery power.
4.4 Determining Proper Values for Ethreshold
It is not hard to see that the stability of our clustering
algorithm largely depends on the value of the bottleneck
battery power Ethreshold. Therefore, before starting our sta-
bility evaluation, we need to determine proper values for
Ethreshold. Figure 5 shows the clustering architecture life-
times obtained by our clustering algorithm as different
values from 10000 to 100000 units are adopted for Ethresh-
old. Figure 5(a) is for our first scenario. We can observe
that when Ethreshold is set to 40000 units, our clustering al-
238 Pi-Rong Sheu and Chia-Wei Wang
Figure 5. Determining proper values for Ethreshold.
gorithm can generate maximal average clustering archi-
tecture lifetimes for all the networks with different sizes.
Thus, we will let Ethreshold = 40000 for our first scenario.
Figure 5(b) explains our second scenario. It shows when
Ethreshold is set to 30000 units, our clustering algorithm
can generate maximal average clustering architecture
lifetimes. Thus, we will let Ethreshold = 30000 for our sec-
ond scenario. Note that the clustering architecture life-
times of our second scenario are shorter than those of our
first scenario under the same network size and the same
Ethreshold value. This is expected because there exist more
nodes with battery power below 50000 in the second sce-
nario than in the first scenario.
4.5 Performance Analyses and Comparisons
Figure 6 presents the minimum battery power among
all the cluster heads when a clustering architecture is es-
tablished by the three clustering algorithms, respectively.
It can be observed that the minimum battery power of a
cluster head generated by our clustering algorithm is al-
ways larger than those obtained by the other two clus-
tering algorithms. The results justify our assumption
that keeping a node with weak battery power from being
elected as a cluster head is more efficient than DMACA,
in which nodes with larger battery power will have a
higher probability of becoming cluster heads.
Figure 7 shows the average clustering architecture li-
fetimes obtained by the three different algorithms. Figure
7(a) illustrates the first scenario and Figure 7(b) explains
the second scenario. These curves indicate that the aver-
age clustering architecture lifetime obtained by our clus-
tering algorithm is about 30% longer than that of DMACA,
and about 33% longer than that of ABCP. Thus, the clus-
tering architecture constructed by our clustering algori-
thm is more stable. In other words, it takes longer for our
clustering architecture to reconfigure.
Figure 8 shows that the average network lifetime ob-
tained by our clustering algorithm is about 32% longer
than that of DMACA, and about 44% longer than that of
ABCP. These results demonstrate an important fact that a
A Stable Clustering Algorithm Based on Battery Power for Mobile Ad Hoc Networks 239
Figure 6. Comparisons on the minimum battery power of cluster heads.
Figure 7. Comparisons on clustering architecture lifetimes.
more stable clustering architecture may also lead to a
longer network lifetime. This is because in our clustering
algorithm, most nodes with lower battery power will be-
come ordinary nodes, which have fewer tasks and con-
sume lower battery power. Thus the lifetimes of ordinary
nodes will be longer. On the other hand, in DMACA, no-
des with lower battery power still have a high probability
of becoming cluster heads, which have more tasks and
consume more battery power. This situation will shorten
not only the clustering architecture lifetime but also the
network lifetime.
Finally, during our computer simulations, we disco-
ver an interesting phenomenon that the number of cluster
heads formed by our clustering algorithm is fewer than
those formed by the others. Figure 9 reveals such a phe-
nomenon. In [20], the performance of a clustering archi-
tecture with fewer cluster heads has been demonstrated
to be more efficient. This is because the overheads of
broadcasting task, where packets initiated at a source are
retransmitted by only cluster heads and gateways, can be
significantly reduced when the number of cluster heads
and gateways is decreased. Therefore, the results in Fig-
ure 9 indirectly imply our clustering algorithm can estab-
lish clustering architectures with better performance in
addition to higher stability.
5. Conclusion
In this paper, we have proposed an efficient cluster-
ing algorithm to establish a stable clustering architecture.
In our clustering algorithm, the node with the largest #_
of_bottleneck is first elected as the cluster head. If there
is a tie, then the node with the largest battery_power will
become the cluster head. If a tie still exists, then the node
with the highest ID prevails.
The computer simulations demonstrate the fact that
the stability of our clustering algorithm is better than tho-
se of DMACA and ABCP in terms of clustering architec-
ture lifetime and network lifetime. Furthermore, our com-
puter simulations show that the number of cluster heads
240 Pi-Rong Sheu and Chia-Wei Wang
Figure 8. Comparisons on network lifetimes.
Figure 9. Comparisons on the numbers of cluster heads.
generated by our clustering algorithm is smaller. This
implies our clustering algorithm is capable of building
clustering architectures for better performance and high-
er stability.
Acknowledgements
This work was supported by the National Science
Council of the Republic of China under Grant # NSC 93-
2213-E-224-023.
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Manuscript Received: Jul. 2, 2005
Accepted: Nov. 29, 2005
242 Pi-Rong Sheu and Chia-Wei Wang