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International Journal of Computer Science and Telecommunications [Volume 3, Issue 5, May 2012] 80
An Innovative Clustering Method for MANET Based on
Cluster Convergence
Nima Karimi and Mohammad Shayesteh
Abstract—Mobile Ad hoc Networks have special properties
In wireless ad hoc network applications, such as
and one of the most significant of those properties is nodes
outdoor teaching and the communications in the disaster
mobility which in part plays a considerable role in network's
area (the scenes of a fire, the flood, the earthquake and so
different parameters. Many efforts were made to make an
on), a number of mobile hosts (MHs) are organized into
infrastructure for these networks. In these infrastructures or
several disjointed communication groups, which may
clusters, a main node called clusterhead has a vital role in
move together and overlap with each other. Members
keeping the structure, routing and enhancing the efficiency of the network. In this article, an innovative clustering algorithm in
within the same group have similar mobility patterns and
mobile ad hoc networks is presented which has emphasis on a
can directly communicate with each other. Members of a
parameter that represents the convergence of the cluster with
group communicate with other nodes outside its group
predict mobility. In this method, three parameters ,Relative
through the group clusterhead, which serves as a gateway
speed, Ndm and battery power are used to calculate the primary
to other groups. In the group mobility, the clusterhead
weight of nodes, then a convergence coefficient can be achieved
equips with two network interfaces, one is used for local
using the strenght of received signals of neighbors and predicting
networks and the other is used for external networks. The
the nodes mobility . This value will be multiplied to the primary
local networks mean wireless ad hoc networks that are
weight of the nodes afterwards, so that the final weight can be
used in a group or between overlapped groups. The
computed. In simulation, the proposed algorithm has been compared with WCA, MOBIC and the Lowest_ID algorithm.
external networks denote the Internet, 2G, GPRS, and
The results of simulation reveal that the proposed algorithm
achieves the goals.
Clustering algorithms can be performed dynamically to
adapt to node mobility [2]. MANET is dynamically
Index Terms— Clustering Algorithm, Convergence, Cluster-
organized into groups called clusters to maintain a
head and Mobile Ad Hoc Network
relatively stable effective topology [1]. By organizing
nodes into clusters, topology information can be aggregated. This is because the number of nodes of a
cluster is smaller than the number of nodes of the entire network. Each node only stores fraction of the total
MANET is a multi-hop wireless network in which
network routing information. Therefore, the number of
Amobile nodes can freely move around in the network, routing entries and the exchanges of routing information
leave the network and join the network. These mobile
between nodes are reduced [3]. Apart from making large
hosts communicate with each other without the support of any
networks seem smaller, clustering in MANETs also makes
preexisting communication infrastructure. Typically, if two
dynamic topology appear less dynamic by considering
nodes are not within mutual transmission range, they
cluster stability when they form [2]. Based on this
communicate through intermediate nodes relaying their
criterion, all cluster members that move in a similar
pattern remain in the same cluster throughout the entire
infrastructure is provided by the nodes themselves.
communication session. By doing this, the topology
Through the nature of MANET, we have many
within a cluster is less dynamic. Hence, the corresponding
challenges. The most important challenges are stability,
network state information is less variable [3]. This
routing and scalability. Clustering is the most way to
minimizes link breakage and packet loss.
improve the stability, routing and scalability. Have
Clustering is usually performed in two phases:
knowledge about the changes of node's status, can present
clustering set-up and clustering maintenance. In the
useful information about the stability of it between its
clustering set-up phase, clusterheads are chosen among
neighbors. This information is effective in clustering
the nodes in the network. The roles of clusterheads are
approach and in cluster head selection.
coordinators of the clustering process and relaying routers
in data packet delivery. After electing clusterheads, other
nodes affiliate with its neighbor clusterhead to form
Nima Karimi is with Islamic Azad University of Bandar-abbas Branch,
clusters. Nodes which are not clusterheads are called
Iran (e-mail: nimakarimi@gmail.com)
Mohammad Shayesteh is with Islamic azad university of Banddar-abbas
ordinary nodes. After the initial cluster set up,
Branch, Iran (e-mail: shayesteh.au@gmail.com)
reaffiliations among clusterheads and ordinary nodes are
Journal Homepage: www.ijcst.org
Nima Karimi and Mohammad Shayesteh 81
triggered by node movements, resulting reconfiguration of
weight is. And the node with highest weight will be
clusters. This leads to the second phase, the clustering
elected as clusterhead.
4) The Distributed Clustering Algorithm (DCA) [8] and
As election of optimal clusterheads is an NP-hard
Distributed Mobility Adaptive clustering algorithm
problem [4], many heuristic clustering algorithms have
(DMAC) [9] are enhanced versions of LID; each node has
been proposed [1]-[10]. To avoid excessive computation
a unique weight instead of just the node's ID, these
in the cluster maintenance, current cluster structure should
weights are used for the selection of clusterheads. A node
be preserved as much as possible. however, any
is chosen to be a clusterhead if its weight is higher than
clusterhead should be able to change its role to an
any of its neighbor's weight; otherwise, it joins a
ordinary node to avoid excessive power drainage. In this
neighboring clusterhead. The DCA makes an assumption
way, the overall lifespan of the system can be extended.
that the network topology does not change during the
The goal of this algorithm is to decrease the number of
execution of the algorithm. Thus, it is proven to be useful
cluster forming, maintain stable clustering structure and
for static networks when the nodes either do not move or
maximize lifespan of mobile nodes in the system. To
move very slowly. The DMAC algorithm, on the other
achieve these goals, we propose a new algorithm. In this
hand, adapts itself to the network topology changes and
algorithm, selection of a clusterhead is done during two
therefore can be used for any mobile networks. However,
stages. In the first, each node calculates its primary
the assignment of weights has not been discussed in the
weight by using a new presented weighted function.
both algorithms and there are no optimizations on the
second, each node predict mobility of its neighborhoods
system parameters such as throughput and power control.
to calculates its convergence coefficient. convergence
5) MOBIC [7] uses a new mobility metric Instead of
coefficient will be multiplied to the primary weight of the
static weights; Aggregate Local Mobility (ALM) to elect
nodes afterwards, so that the final weight can be
clusterhead. ALM is computed as the ratio of received
computed. The result of simulation shows that the
power levels of successive transmissions (periodic Hello
proposed algorithm provides better performance than
messages) between a pair of nodes, which means the
WCA, MOBIC and Lowest_ID in terms of Clusterhead
relative mobility between neighboring nodes.
changes, Clusterhead lifetime and the average number of
6) The Weighted Clustering Algorithm (WCA) [4] is
orphan clusters.
based on the use of a combined weight metric that takes
The rest of this paper is organized as follows. In
into account several parameters like the node-degree,
Section 2, we review several clustering algorithms
distances with all its neighbors, node speed and the time
proposed previously. Section 3 presents the proposed
spent as a clusterhead. Although WCA has proved better
algorithm for mobile ad hoc networks. The simulation of
performance than all the previous algorithms, it lacks a
the proposed algorithm is given in Section 4. Finally,
drawback in knowing the weights of all the nodes before
Section 5 concludes this paper.
starting the clustering process and in draining the clusterheads rapidly. As a result, the overhead induced by
II. RELATED WORK
WCA is very high.
Most of previous algorithms were using only one metric
A large number of clustering algorithm have been
for clustering purposes. Therefore, the resulted clustering
topology fits just in terms of that specific metric [10]. As
characteristic of mobile node in mobile ad hoc network to
Mobile Ad Hoc networks are generally complex and
choose clusterhead. We will give each of them a brief
dynamic networks, existing of only one specific metric
description as follows:
can not reference the whole situation of the network.
1) Highest degree clustering algorithm [5] uses the
Those types of clustering topologies which are optimal in
degree of a node as a metric for the selection of
terms of just one metric are suitable for particular
clusterheads. The node with highest degree among its
scenarios and have poor performance in other scenario.
neighbors will be elected as clusterhead, and its neighbors
For these reasons, we use different metrics in our
will be cluster members. In this scheme, as the number of
algorithm to select the clusterhead. On the other hand, in
ordinary nodes in a cluster is increased, the throughput
clustering approaches based on weighted functions such
drops and system performance degrades.
as WCA, efforts are concentrated to select the best node
2) The Lowest-Identifier algorithm (LID) [6] chooses
among neighborhood nods by using of available metrics.
the node with the minimum identifier (ID) as a
In these methods, only those nodes are selected as
clusterhead. The system performance is better than
clusterhead which have better properties than other
Highest-Degree heuristic in terms of throughput [4].
neighborhood nodes such as more rest battery power,
However, since this heuristic is biased to choose nodes
having more neighborhoods and less average distance
with smaller IDs as clusterheads, those nodes with smaller
from neighborhoods. but in most of them do not consider
IDs suffer from the battery drainage, resulting short
to the movement model of the clusterhead toward the
lifetime span of the system.
other nodes of the cluster. This factor causes the formed
3) Least Movement Clustering Algorithm [7]. In this
clusters be unstable and increase the overload of cluster
algorithm, each node is assigned a weight according to its
reelection process. While in approaches within move
mobility. The fastest the node moves, the lowest the
sensitive clustering category such as MOBIC, clustering is done
International Journal of Computer Science and Telecommunications [Volume 3, Issue 5, May 2012] 82
approaching/escaping to each other. in this approaches,
• It must require a lower battery power for interaction
the main parameter for clustering is the mobility of nodes
and therefore other parameters such as the energy of the
To meet the first condition, the amount of battery power
battery, the number of neighborhoods and so on, are not
is taken into account as one of the factors for calculation
of weight. To meet the second condition, we can choose a
In the proposed approach we present an optimal method
node as a clusterhead, which has less distance with its
without appearing previous methods problems by
neighbors during neighborhood duration.with using of
combining the useful characteristics of those methods in
convergence coefficient we can achieve this goal. Higher
order to reach to the goal of our algorithm.
convergence coefficient means that in the future the average distance between clusterhead and its neighbors is
III. THE PROPOSED ALGORITHM
smaller than other node. The higher convergence
In the algorithm presented in this paper, selection of a
coefficient means that the less transmission power the
clusterhead is done during the calculation of primary
node requires for interaction and communication with its
weight and final weight. In the first, each node calculates
neighbors and as a result it consumes less battery power.
its primary weight by using a new presented weighted
The second parameter which causes the clusters be
function. second, each node calculates its convergence
unstable is the mobility of nodes. In the proposed
coefficient with predict mobility of its neighborhood.
algorithm for creating stable clusters, in the first, the
Then based on the primary weight final weight is
previous mobility of nodes intended, which is accessible
calculated. Finlay the node with higher weight selected as
by calculating parameter Relative speed. In the second,
A. Setup Procedure
convergence coefficient of nodes.
First, we allocate IDs for the nodes. In the proposed
The used parameters in weighted function for giving a
algorithm, each node Ni (member or clusterhead) is
primary weight to nodes (weightp) include:
identified by a state such as: Ni (idnode , idCH , flag ,
- Battery remaining (Br): every node which wants to be
Weightp), it also has to maintain a ‘node_table' wherein
the clusterhead should have threshold power Bd. A
the information of the local members is stored. However,
clusterhead consumes more energy in a cluster comparing
the clusterheads maintain another clusterhead information
with an ordinary node. In addition, we prefer to choose a
table ‘CH_table' wherein the information about the other
more powerful node to play its role as a clusterhead
clusterheads and member node is stored.
because such a node looses its energy later results in the
In complex networks, the nodes must coordinate
late starting of new clusterhead selection process and
between each other to update their tables. The Hello
therefore increases the stability of clusters. Equation (1)
messages are used to complete this role. A Hello contains the state of the node; it is periodically exchanged either
shows that the each node how to calculate battery
between clusterheads or between each clusterhead and its
remaining of itself.
members in order to update the ‘CH_tables' and the
‘node_tables' respectively.
We define a flag for every node which determine their
- Number of nodes moving towards a node (N
role. The value of flag is 3 if the node is the clusterhead,
is 1 if the node is an ordinary node, is 2 if the node is a
- Relative speed (S): The relative mobility of the node
gateway and is zero if the node has an undetermined
with its neighbors, which means how long node has spent
their time beside the node. A lower relative speed simply
A. Weighted Function
means that the neighbors of a certain node has spent a longer time in its transmission range, we conclude that the
To enhance stability of clusters we must find out
mentioned node has a more stable situation. The relative
problems that cause stability to be decreased and as a
speed is calculated by Eq. (2).
result cause a cluster to disappear. If we know and solve these problems, we can enhance stability of the clusters as
much as possible.
The first parameter which causes clusters to disappear
{n is the number of node's neighbors}
is Excessive battery consumption at a clusterhead. In MANETs, the nodes not only bear the responsibility of
Where SL is the signal strength of last packet reception,
sending and receiving information, but also carry out
SF is the signal strength of first packet reception, TL is
routing for packages. As a result they consume a high rate
the time of the last packet reception and TF is the time of
the first packet reception.
As a result a clusterhead must have the following
Each node uses the above mentioned three parameters
to calculate its primary weight (weightp). The Eq. (3)
• It must have a high existence of battery power.
shows how the nodes calculate weightp.
Nima Karimi and Mohammad Shayesteh 83
N (3)
Table 1: Messages used in the algorithm
In the Eq. (3), ci(s) are the weight factors of
To update the tables of
node , idCH , flag,
B. The calculation of final Weight
Best(idnode , idCH )
Offer the node to be
After Each node calculate its primary weight, to get
To affiliate a cluster
final weight should calculate the convergence coefficient.
The node accepts the
For calculate the convergence coefficient we should first
predict the neighbors mobility. We predict neighbors
The CH accepts a
mobility based on last two received signal power. For
predicting the next signal power received by nodes, we
The CH rejects a
use linear extrapolation method. In this method in order to
obtain the signal power at the time t+1, we use the powers
The CH adds the node as
of signal at the times t and t-1. Eq. (4) shows how to
calculate the power of a signal received at the time t+1:
The CH accepts the
presence of a new CH in
!" P! +$%&'(%&)( * +P
The CH notifies a CH
After predicting the power of the received signal of all
The node leaves the
neighbors at the time t+1, we can calculate the
convergence coefficient of this node by the Eq. (5):
ignore any Join_Request received even if they are in the
/ ∑
transmission range of the new entry node. This allows simplifying the management of the clusters.
Each node use Eq. (6), for calculating the final weight
In the case where the node receives a response
according to convergence coefficient:
(CH_Wel or CH_NWel ), it does not take immediately
WeightF = C*weightp (6)
any decision, this allows the node to be certain that it has received all the responses from all the neighboring
clusterheads. The CH_Wel and CH_NWel messages do
Neighbourhood selected as clusterhead.
not indicate that the clusterhead has added the node to its
In this way, we can select suitable clusterhead for all
table; they just signify that the clusterhead is waiting for a
nodes not only by considering the suitable condition of
Join_Accept in order to add the node to its table. When
clusterhead in the network but also with regarding to the
the node receives multiple CH_Wels, Based on the
node status comparing with the clusterhead. This leads to
primary weight of clusterheads calculate the final weight
better local selection of the clusterhead resulting in more
of them and select the node with highest final weight as
stable clusters creation preventing future cluster reform
the clusterhead. After that, it sends a Join_Accept to the
chosen clusterhead and waits for CH_ACK from this CH.
Table 1 show the messages with its description used in
The CH_ACK has to contain a confirmation that the idnode
proposed algorithm.
has been added to the CH_table. Thus the node can fully-define its state. The reason that we use four ways to
C. New Arrival Nodes Mechanism
confirm the joining procedure is to prevent other clusterheads that they can serve the entry node to add this
Once a wireless node is activated, its idCH field is
node to their tables and cause conflicts.
equal to NULL since it does not belong to any cluster.
In the case where the node was just receiving
The node continuously monitors the channel until it
CH_NWels, it considers these responses as rejection
figures out that there is some activity in its
messages from the clusterheads. This may occur when the
neighbourhood. This is due to the ability to receive the
clusterheads are saturated and decide to reject the
signals from other present nodes in the network. The node
adhesion of new nodes. When the number of attempts
still has no stable state, thus its state is not full identified.
reaches a certain value, the node prefers not to stay
In this case, it broadcasts a Join_Request in order to join
isolated, thus it declares itself as clusterhead.
the most powerful clusterhead. Thus, it waits either for a CH_Wel or for a CH_NWel.
D. Clusterhead Nodes Mechanism
When the entry node does receive neither CH_Wel nor
CH_NWel . If this persists for certain number of attempts,
A clusterhead has an idnode field is equal to idCH field.
the node declares itself as an isolated node, and restarts by
As a clusterhead, the node calculates periodically its
broadcasting a new Join_Request after a period of time.
weight, thus it sends periodically Hello messages to its
We note that just the clusterheads may response by a
members and to the neighboring clusterheads in order to
CH_Wel or CH_NWel; the ordinary members have to
update the node_tables and CH_tables respectively. The
International Journal of Computer Science and Telecommunications [Volume 3, Issue 5, May 2012] 84
clusterhead must monitor the channel for Leave, Hello
current clusterheads weight, Member node get this
and Join_Request messages. in the proposed algorithm
decision by calculating the final weight of the new
this operation is limited to clusterhead to allow easier
clusterhead. it sends a Join_Request to the clusterhead
management on clusters.
which is Hello's source and continues as a member of the
When the clusterhead receives a Join_Request
current clusterhead until the reception of CH_ACK. In
(idCH=NULL) from a new arrival node or a Join_Request
this case, the node can send a Leave_Request to the last
(full state) from a node which belongs to another cluster,
clusterhead. This method allows us to minimize the
the clusterhead can accept or reject the request basing on
number of the formed clusters in the network.
its capacities. This procedure gives more flexibility to the
When the node member receives a CH_info message as
members by allowing them to leave a weak clusterhead
a result of the re-election procedure, thus it realizes that it
and join another one which seems stronger than the
is going to become the new clusterhead in the cluster.
current clusterhead. It may not be possible for all the
When a node member does not receive any message from
clusters to reach the cluster size λ. We have tried to
its clusterhead, it considers that the clusterhead has gone
reduce the clusters formed by merging the clusters that
brusquely down; in this case, the nodes have no choice
have not attained their cluster size limit. However, in
and must restart the clustering setup procedure.
order not to rapidly drain the clusterhead's power by accepting a lot of new nodes, we define thresholds which
IV. SIMULATION AND RESULTS
allow the clusterhead to control the number of nodes inside its cluster.
In this paper we use GloMoSim tool for simulation. The
The re-election is not periodically invoked; it is
simulation environment is a Mobile Ad Hoc Network
performed just in case of a higher received weight, it
consists of 20 to 100 nodes in an 800*800 area. We
allows minimizing the generated overhead encountered in
assume that each node will be activated by a 2.4GHz radio
previous works As we explained above. the re-election
frequency. The simulated area is considered as a two
may not result a new clusterhead, it depends on the
dimensional square and nodes move freely throughout the
stability of the new node for playing the clusterhead's
area. The movement of nodes has been simulated
according to Random Waypoint model.
In the proposed algorithm clusterhead will check
In order to evaluate the performance and efficiency of
regularly incoming messages from neighboring nodes. if
the proposed algorithm, a set of simulations were operated
clusterhead received a message that contains higher
and duration of them was 1200 seconds. We select a set of
primary weight from his weight, then it check the relative
parameters to show the efficiency of our algorithm. Our
mobility with the desired node, if its relative mobility to
proposed algorithm was compared with WCA, MOBIC
this node were in the first group and all of the cluster
and Lowest_ID method which is the most famous
members exist in neighboring of this node, assign
clustering algorithms. These parameters include:
clusterhead role to the desired node. The node does it with save the ID of this node in its CH_ID field.
A. Clusterhead changes
Then send a CH_info message to new clusterhead to
Fig. 1, Fig. 2 and Fig. 3 show that the clusterhead
declare that this node as a new clusterhead selected. Then
changes in the proposed algorithm less than the WCA,
copy their tables in to new clusterhead and send a
MOBIC and Lowest_ID algorithms that leads to long life
CH_change message to neighboring nodes to defines a
of clusters so will have more stable clusters. The reason is
new clusterhead. in This new approach selecting the new
that the proposed algorithm selected such as the node as
clusterhead is based on stability of it in the cluster. In this
the clusterhead that have more Presence and battery
case where a new clusterhead is elected, the procedure is
power therefore more time is left as clusterhead. Fig. 1
soft and flexible in order not perturb the clusters while to
shows the Average number of clusterhead changes against
copying the databases from the old clusterhead to the new
the speed of node, Fig. 2 shows the average number of
clusterhead changes against the transmission range and Fig. 3 shows the count of clusterhead changes against the
E. Member Nodes Mechanism
number of nodes.
After joining a cluster, the node declares itself as a
member of this cluster. Hence, it calculates periodically
B. Clusterhead lifetime
its weight and sends periodically Hello messages to its
Fig. 4 and Fig. 5 show that the clusterhead lifetime in
clusterhead. As a member, this node should just handle
the proposed algorithm higher than the WCA, MOBIC and
the Hello, the CH_change and the CH_info messages.
Lowest_ID algorithms. Fig. 4 shows the average lifetime
This allows optimizing the resources (bandwidth, battery,
of clusterhead against the speed of node and Fig. 5 shows
etc) and minimizing the job of the nodes.
When the node receives a Hello from its clusterhead,
transmission range.
the node has to update its node_table. When the node receives Hellos from the neighboring clusterheads, the
C. The average number of clusters
node has the possibility to migrate to another clusterhead if there is a Hello which has a higher weight than the
As you can see in Fig. 6, the number of formed clusters
is increased by increasing the number of nodes. Fig. 6
Nima Karimi and Mohammad Shayesteh 85
shows the average number of clusters against the number
Fig. 4. Average lifetime of clusterhead vs. the speed of node
Fig. 1. the Average number of clusterhead changes vs the speed of node
40 50 60 70 80 90 100110120130140150160
Transmission Range
40 50 60 70 80 90 100 110 120 130 140 150 160
Fig. 5. Average lifetime of clusterhead vs. the transmission range
Transmission Range
Fig. 2. the Average number of clusterhead changes vs. the transmission
Our Algorithm,λ=20
Our Algorithm,λ=15
Our Algorithm,λ=20
Our Algorithm,λ=15
Fig. 6. Average number of clusters vs. the number of node
Fig. 3. Count of clusterhead changes vs. the number of nodes
D. The average number of orphan clusters
As you can see in figure 7, the number of single node
clusters or orphan clusters in our algorithm is less than
International Journal of Computer Science and Telecommunications [Volume 3, Issue 5, May 2012] 86
WCA, MOBIC and the Lowest_ID algorithms. The reason
C. E. Perkins, editor. Ad Hoc Networking. Addison-Wesley,
is that in our algorithm, the clusterheads are selected from
central safe area.
Chatterjee, M., Das, S., and Turgut, D., "WCA: a weighted clustering algorithm for mobile ad hoc networks," Journal of
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In this paper, we have presented a new clustering
algorithm in Mobile Ad Hoc Network. In this algorithm selection of a clusterhead is done during the weighted function. In the first, each node calculates its primary weight by using a new presented weighted function. In the second, each node calculates its convergence coefficient with predict neighborhoods mobility. A number of parameters of nodes were taken into consideration for assigning weight to a node. The proposed algorithm chooses the cluster-heads based on the information of neighbor nodes, and maintains clusters locally. Also it has a feature to control battery power consumption by switching the role of a node from a cluster-head to an ordinary node. We assumed a predefined threshold for the number of nodes to be created by a clusterhead, so that it does not degrade the MAC function and to improve the load balancing. We conducted simulation that shows the performance of the proposed enhancement clustering in terms of the average number of clusters formation, stability of clusters, and lifetime of a clusterhead. We also compared our results with the WCA, MOBIC and Lowest_ID. The simulation results show that our enhancement
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