Communications and Network, 2013, 5, 8-14
doi:10.4236/cn.2013.52B002 Published Online May 2013 (
Survey of Clustering Schemes in Mobile Ad hoc Networks
Abdelhak Bentaleb1, Abdelhak Boubetra1, Saad Harous2
1Department of Computer Science, University of Bachir el Ibrahimi, Bordj Bou Arreridj, Algeria
2Faculty of Information Technology, United Arab Emirates University, P.O Box 17551 Al Ain, UAE
Received 2013
Mobile ad-hoc networks (MANETs) are a specific kind of wireless networks that can be quickly deployed without pre-
existing infrastructures. They are used in different contexts such as collaborative, medical, military or embedded appli-
cations. However, MANETs raise new challenges when they are used in large scale network that contain a large number
of nodes. Subsequently, many clustering algorithms have emerged. In fact, these clustering algorithms allow the struc-
turing of the network into groups of entities called clusters creating a hierarchical structure. Each cluster contains a par-
ticular node called cluster head elected as cluster head according to a specific metric or a combination of metrics such
as identity, degree, mobility, weight, density, etc. MANETs has drawbacks due to both the characteristics of the trans-
mission medium (transmission medium sharing, low bandwidth, etc.) and the routing protocols (information diffusion,
path finding, etc.). Clustering in mobile ad hoc networks plays a vital role in improving resource management and net-
work performance (routing delay, bandwidth consumption and throughput). In this paper, we present a study and ana-
lyze of some existing clustering approaches for MANETs that recently appeared in literature, which we classify as:
Identifier Neighbor based clustering, Topology based clustering, Mobility based clustering, Energy based clustering,
and Weight based clustering. We also include clustering definition, review existing clustering approaches, evaluate their
performance and cost, discuss their advantages, disadvantages, features and suggest a best clustering approach.
Keywords: Clustering; Mobile Ad hoc Networks; Routing Protocol
1. Introduction
A Mobile Ad hoc NETwork (MANET) consists of a
group of mobile nodes that self-configure to form a tem-
porary network without the aid of a preset infrastructure
or centralized management. Such networks are charac-
terized by: dynamic topologies, existence of bandwidth
constrained, variable capacity links, and energy con-
strained operations and highly prone to security threats.
Due to all these features routing is a major issue in mo-
bile ad hoc networks [1,2].
Routing in a network is the process of selecting paths
to send network traffic. Routing can take place either in a
flat structure or in a hierarchical structure [3]. In a flat
structure [4,5], all nodes in the network are in the same
hierarchy level and thus have the same role. Although
this approach is efficient for small networks, it does not
allow the scalability when the number of nodes in the
network increases. In large networks, the flat routing
structure produces excessive information flow which can
saturate the network [6,7]. Hierarchical routing protocols
[8] have been proposed to solve this problem among oth-
ers. This approach consists of dividing the network into
groups called clusters. This results in a network with
hierarchical structure. Different routing schemes are used
between clusters (inter-cluster) and within clusters (intra-
cluster). Each node maintains complete knowledge of
locale information (within its cluster) but only partial
knowledge about the other clusters. Hierarchical routing
is a solution for handling scalability in a network where
only selected nodes take the responsibility of data routing
[9,10]. However, hierarchical approaches undergo con-
tinual topology changes. Thus, topology management
plays a vital role prior to the actual routing in MANET.
Cluster based structure (hierarchical structure) in net-
work topology has been used to improve the routing effi-
ciency in a dynamic network [11,12].
Structuring a network is an important step to simplify
the routing operation in MANETs. Several algorithms
based on clustering techniques have been proposed in the
literature [4,5,8,13]. The clustering consists of dividing
the network into a set of nodes that are geographically
close. It is an efficient solution to simplify and optimize
the network functions. In particular, it allows the routing
protocol to operate more efficiently by reducing the con-
trol traffic in the network and simplifying the data rout-
ing. Several clustering schemes have been proposed.
These schemes have different characteristics and are de-
signed to meet certain goals depending on the context in
Copyright © 2013 SciRes. CN
which the clustering is used (routing, security, energy
conservation, etc.) [11,14,16].
The rest of the paper is organized as follow: we start
by introducing different clustering approaches. Then, we
present their advantages and disadvantages. In section 3
we present some existing works on survey of clustering
in MANETs. In section 4, we review some clustering
schemes for MANETs. Then we compare the clustering
schemas that already present. Finally, in section 5, we
conclude the paper.
2. Clustering in Mobile Ad hoc Network
2.1. Definition
The process that divides the network into interconnected
substructures, called clusters. Each cluster has a particu-
lar node elected as cluster head (CH) based on a specific
metric or a combination of metrics such as identity, de-
gree, mobility, weight, density, etc. The cluster head
plays the role of coordinator within its substructure. Each
CH acts as a temporary base station within its cluster and
communicates with other CHs [17,18]. A cluster is there-
fore composed of a cluster head, gateways and members
Cluster Head (CH): it is the coordinator of the cluster.
Gateway: is a common node between two or more
Member Node (Ordinary nodes): is a node that is nei-
ther a CH nor gateway node. Each node belongs exclu-
sively to a cluster independently of its neighbors that
might reside in a different cluster.
2.2. Algorithms for Cluster Heads Election in
There are several algorithms in the literature for cluster
heads election in mobile ad hoc networks: Lowest-ID
[20], Highest-Degree [21], Distributed Clustering Algo-
rithm [22], Weighted Clustering Algorithm (WCA) [23]
and Distributed Weighted Clustering Algorithm (DWCA)
3. Related Work
Jane Y.Yu and Peter H.J.Chong [11], have presented a
comprehensive survey of clustering schemes for MANETs.
The authors first provided fundamental concepts about
clustering. Then they classified proposed clustering
schemes into six categories based on their main objec-
tives, which are listed as follows: Dominating-Set-based
(DS-based) clustering, low maintenance clustering, mo-
bility-aware clustering, energy efficient clustering, load-
balancing clustering, and combined metrics-based clus-
tering. They also grouped the clustering cost terms into
five categories: the required explicit control message
exchange, the ripple effect of re-clustering, the stationary
assumption, constant computation round, and communi-
cation complexity.
A. Abbasi and M. F. Younis [12] grouped taxonomy
of relevant attributes into three types: cluster properties,
cluster head capabilities, clustering process. They cate-
gorized the different schemes based on the objectives, the
desired cluster properties and clustering process. They
highlighted their objectives, features, complexity and the
effect of the network model on the presented schemes
and summarized a number of schemes, stating their
strength and limitations. Finally they compared these
clustering algorithms based on metrics such as conver-
gence rate, cluster stability, cluster overlapping, location
awareness and support for node mobility.
B.A.Correa et al [3], discussed the concepts related to
network topology, routing schemes, graphs partitioning
and mobility algorithms. The authors described low-
est-ID heuristic, highest degree heuristic, DMAC (dis-
tributed mobility-adaptive clustering), WCA (weighted
clustering algorithm).
R. Agarwal and M. Motwani [10] examined the im-
portant issues related to cluster-based MANETs, such as
the cluster structure stability, the control overhead of
cluster construction and maintenance, the energy con-
sumption of mobile nodes with different cluster-related
status, the traffic load distribution in clusters, and the
fairness of serving as cluster head for a mobile node.
M. Anupama and B. Sathyanarayana [28], analyzed,
compared and classified some clustering algorithms into:
location based, neighbor based, power based, artificial
intelligence based, mobility based and weight based.
They also presented the advantages and disadvantages of
these techniques and suggest a best clustering approach
based on the observation and the comparison.
4. Clustering Schemes in Mobile Ad hoc
We classify the clustering algorithms based on their ob-
jectives, the cluster heads election criteria and based on
literature review [10, 12 ,28,29] as:
4.1. Identifier Neighbor Based Clustering
In identifier neighbor based clustering, a unique ID is
assigned to each node. Each node in the network knows
the ID of its neighbors. The cluster head is selected based
on criteria involving these IDs such as the lowest ID,
highest ID...etc.
Ephremides et al [20] proposed a clustering algorithm
called Linked Cluster Algorithm (LCA) where each node
is either, a cluster head, an ordinary node or a gateway
node. Initially, all nodes have status of ordinary node;
periodically each node in the network broadcasts its ID
Copyright © 2013 SciRes. CN
and its neighbors IDs. Subsequently, the node with the
smallest ID is selected as cluster head. A node which can
hear two or more cluster heads is a gateway. The process
repeats until every node belongs to at least one cluster.
Nodes with a small ID are more likely to be selected as
cluster heads so they quickly consume their energy.
Chiang et al [30] proposed Least Cluster Change
(LCC), an improved versions of LCA algorithm which
adds a maintenance step to minimize the cost of re-clus-
tering. The reconstruction of clusters is invoked in only
the following two cases:
If two cluster heads are neighbors, then the one with
the highest ID gives up the role of cluster head.
If a non CH node moves outside its cluster and does
not join an existing cluster then it will become cluster
head forming a new cluster.
LCC improves the stability of clusters but it has some
disadvantages e.g. the cost of re-clustering is a bit expen-
Lin and Gerla [31] proposed another protocol called
Adaptive Clustering Algorithm (ACA). In this algorithm,
once the clusters are formed, the concept of cluster head
disappears and all nodes play the same role in the net-
work. The authors’ motivation is that cluster heads can
become bottlenecks and consume their resources faster
than other nodes. The same metric as the LCA (the low-
est ID) is used for the CH selection. In cluster mainte-
nance, each node must know its two-hop neighbors. If
the distance between two nodes in the same cluster be-
comes three hops, than cluster maintenance is invoked.
A heuristic based algorithm [13] called Max-Min D-
cluster builds D-clusters non-overlapping. The node ID is
used for CH election. The algorithm is divided into three
phases. In the first phase, each node broadcasts its ID to
its neighbors within D-hops, collects their IDs and finds
the highest ID which it will broadcast in the second
phase. In the second phase, on receiving the highest IDs,
each node keeps the lowest IDs among the highest. Dur-
ing the third phase cluster head is chosen based on the
IDs saved in the two previous phases. This algorithm
produces a robust structure of clusters. However, the
duration of cluster formation is significant and more in-
formation is exchanged before electing a CH.
Chen et al proposed an algorithm [32] that constructs
k-hop clusters by generalizing the scheme [31]. Nodes
initiate the clustering process by flooding requests for
clustering to all the other nodes. Each node has to know
its k-hops neighbors. All nodes whose ID is lowest
among all their k-hop neighbors broadcast their decision
to create clusters to all their k-hop neighbors and be-
comes CHs. The maintenance phase is similar to the one
used in [31] but it takes into account the cluster radius.
However, the same disadvantages of [31] are still pre-
4.2. Topology Based Clustering
In the topology based clustering, the cluster head is cho-
sen based on a metric computed from the network topol-
ogy like node connectivity. We present below some of
the existing topology based clustering algorithms.
Gerla and Tsai proposed a protocol called High-Con-
nectivity Clustering (HCC) [21] based on the degree of
connectivity to construct clusters. In this protocol the
node with the highest number of neighbors is selected as
the cluster head. If two nodes or more have the same
degree of connectivity, the node with the lowest ID is
elected as a cluster head. HCC generates a limited num-
ber of clusters. In mobile environment, this algorithm
increases the number of re-affiliations of CHs because
their degree changes very frequently.
In [34], Yu and Chong proposed 3-hop Between Ad-
jacent Cluster-heads (3hBAC) which creates a 1-hop
non-overlapping clusters structure with three hops be-
tween neighboring cluster heads by the introduction of a
new node status, named cluster guest. Cluster guest node
is a mobile node that cannot directly connect to any clus-
ter head, but can access some clusters with the help of a
cluster member. During cluster formation, the nodes
having the highest degree are declared as CHs. All one
hop neighbors join as member nodes. The neighbor
nodes of these member nodes that cannot directly join
any cluster will be declared as cluster guest. Cluster
maintenance is performed the same way as in LCC algo-
rithm. This algorithm reduces the number of CHs in the
network. CHs and member nodes keep their status for a
long period. However, this algorithm requires that each
node maintains two tables: a neighbor table and member
table that contain all member nodes of the network.
Guizani et al [35] proposed a new clustering algorithm
named α-Stability Structure Clustering (α-SSCA) that
has three phases. The first phase consists in collecting the
neighbor nodes information necessary for CHs election
by exchanging HELLO message. During the second
phase a score function is used as a metric for CHs elec-
tion. The score function is based on the number of
neighbors whose status has not been decided yet. The
node with the highest score is elected as cluster head.
This technique has the advantage of keeping neighboring
CHs far away from each other which leads to minimal
invocation of the maintenance procedure. This algorithm
increases moderately the number of clusters in the aim of
improving clusters stability, and reducing the overheads.
Associativity-based Cluster Formation and Cluster
Management [36] use a new metric called associativity
representing the relative stability of nodes in their
neighborhood. Every time, a node u checks its current
neighbors, it increments by one the associability value of
the nodes from the previous period. When a neighbor
moves away, its associativity value is reset to zero. The
Copyright © 2013 SciRes. CN
associativity value is set to one when a neighbor is de-
tected for first time or redetected. At each instant of time,
the associativity of u is the sum of the values associated
to its neighbors. During the cluster formation phase,
each node considers the nodes in its k-neighborhood, the
node with the highest associativity is chosen as cluster
head. When more than one has the highest associativity
value, the node with the highest degree is chosen. This
algorithm produces overlapping k-clusters that remain
stable over a long period of time.
4.3. Mobility Based Clustering
Lowest Relative Mobility Clustering Algorithm (MOBIC)
[38] is based on the LCA algorithm but involves the rela-
tive mobility of nodes as a criterion in the cluster head
selection. The idea is to choose nodes with low mobility
as cluster heads because they provide more stability.
MOBIC uses a similar clusters maintenance procedure as
LCC [30] with an additional rule to minimize the cost of
clusters maintenance. MOBIC uses Cluster Contention
Interval (CCI) to avoid unnecessary cluster head relin-
quishing. If two CHs are neighbors after the CCI time
period has expired, then the one with the highest ID gives
up the role of CH. This mechanism reduces the CHs
maintenance. However, the limitations of LCC algorithm
are not completely eliminated.
A novel clusters algorithm [39] which guarantees
longer lifetime of the clustering structure. The main idea
is to estimate the future mobility of mobile nodes so that
the ones that will exhibit the lowest estimated mobility
will be chosen as CHs. Combining the mobility predic-
tion scheme with the highest degree clustering technique,
the authors proposed a distributed algorithm that builds a
small and stable virtual backbone over the whole net-
work. This algorithm creates clusters highly resistant to
node mobility. The node with the highest weight among its
neighbors is declared as the CH. This algorithm elimi-
nates the problem of frequently changing CH due to node
mobility, by allowing a node to become a CH or to join a
new cluster without starting a re-clustering phase.
Ni et al proposed a mobility prediction-based cluster-
ing (MPBC) scheme [40] for MANETs with high mobil-
ity nodes. The basic information in MPBC is the relative
speeds estimation for each node in the whole network.
During the clustering stage, all nodes broadcast the Hello
packets periodically to build their neighbors lists. Each
node estimates its average relative speeds with respect to
its neighbors based on the Hello packets exchanges.
Nodes with lowest relative mobility are selected as CHs.
During cluster maintenance stage a prediction-based
method is to solve the problems caused by relative node
movements, including the cases when a node moves out
of the coverage area of its current CH, and when two
CHs move within the reach of each other, one is required
to give up its CH role. This approach extends the con-
nection lifetime which results in stable clusters.
Mobility-based d-hop clustering algorithm (MobDHop)
[41] divides the network into d-hop clusters based on
relative mobility metric. The objective of creating d-hop
clusters is to supports larger than one-hop radius clusters
which reduces the number of cluster heads. The relative
mobility is estimated based on the signal strengths of
received packets. The distance between two nodes is es-
timated using the signal strengths of the received packets
exchanged. The cluster formation process is divided into
two stages: Discovery Stage and Merging Stage. During
the discovery stage, mobile nodes with similar speed and
direction are grouped into the same cluster. The merging
phase is invoked in order to either merge clusters to-
gether or join individual nodes to a cluster. The cluster
maintenance process is invoked when a node switches on
and joins the network or a node switches off and leaves
the network.
4.4. Energy based Clustering
The battery power of node is a constraint that affects
directly the lifetime of the network, hence the energy
limitation poses a severe challenge for network perform-
ance. CH performs special tasks such as routing causing
excessive energy consumption. Next, we discuss some
existing energy based clustering algorithms.
A multicast power greedy clustering (MPGC) [15] is
based on heuristic to reduce the energy consumption.
This algorithm runs in three consecutive phases: beacon
phase, greedy phase and recruiting phase. During beacon
phase, each node sends a beacon signal with the highest
power in order to inform its neighbors of its presence and
collects information about its neighbors of the beacons
received. During the greedy phase, each node sends a
cluster head declaration with necessary level of power
required to reach its nearest neighbor, and then it in-
creases its power level step by step until it reaches all its
neighbors. During last phase, each node has the value of
the residual power of its neighbors. If a node u has the
highest residual power among all its neighbors, then u is
elected as cluster head. MPCG prolongs network lifetime,
but it requires several steps to construct the clusters
structure which increases network traffic and bandwidth
A Flexible Weighted Clustering Algorithm based on
Battery Power (FWCABP) for MANETs [42] is pro-
posed to maintain stable clusters by preventing nodes
with low battery power from being elected as a cluster
head, minimizing the number of clusters, and minimizing
the clustering overhead. During cluster formation phase,
each node broadcasts a beacon message to inform its
neighbors of its status and builds its neighbors list. The
CHs election is based on the weight values of the degree
Copyright © 2013 SciRes. CN
of nodes, sum of distance to its neighboring nodes, nodes
mobility and remaining battery power. The node with the
smallest value is selected as CH. FWCABA invokes the
maintenance procedure when: a node moves outside its
cluster boundary and/or CH battery power decreases to a
predefined threshold value. FWCABP increases network
traffic during the cluster head election process which
degrades the network performance.
Enhance Cluster based Energy Conservation (ECEC)
algorithm [43] is an enhancement of Cluster based En-
ergy Conservation algorithm (CEC) [44]. The authors
presented a new topology control protocol that extends
the lifetime of large ad hoc networks while ensuring
minimum connectivity of nodes in the network, the abil-
ity for nodes to reach each other and conserve energy by
identifying redundant nodes and turning their radios off.
During cluster formation phase, nodes with the highest
estimated energy values in their own neighborhoods are
elected as CHs. After CHs election process, ECEC then
elects gateways to connect clusters. It is shown in [43]
that ECEC reduces power consumption which leads to a
longer network lifetime. However, this scheme ex-
changes more overhead to elect the CHs and getaways.
4.5. Weight based Clustering
Weight based clustering techniques use a combination of
weighted metrics such as: transmission power, node de-
gree, distance difference, mobility and battery power of
mobile nodes… etc. The weighting factors for each met-
ric may be adjusted for different scenarios. Some of these
algorithms are presented next.
A Flexible Weight Based Clustering Algorithm (FWCA)
uses a combination of metrics (with different weights) to
build clusters. Node degree, remaining battery power,
transmission power, and node mobility are used in CHs
election process. The cluster size does not exceed a pre-
defined threshold value. During cluster maintenance
phase, FWCA uses the clusters capacity and the link life-
time instead of the node mobility because the link stabil-
ity metric affects the election of a CH with the same
weight as the node mobility metric.
Adabi et al proposed Score based clustering algorithm
(Sbca) [46] for MANETs which aims to minimize the
number of clusters and maximize lifespan of mobile
nodes. it uses a combination of the following four met-
rics to calculate the node score: battery remaining, node
degree, number of members and node stability. During
cluster formation, each node calculates its score and
broadcasts it to its neighbors. The node with highest
score is elected as cluster head. Sbca generates fewer
clusters than WCA but has the same limitations.
An efficient weight-based clustering algorithm (EW-
BCA) for MANETs is proposed in [47] aims to improve
the usage of scarce resources such as bandwidth and en-
ergy by producing stable clusters, minimizing routing
overhead, and increasing end to end throughput. Each
node has a combined weight (Number of Neighbors,
Battery Residual Power, Stability and Variance of dis-
tance with all neighbors) that indicates its suitability.
Each node is: NUL, CH, member node, getaway node.
Initially all nodes are in the NUL state. Each node calcu-
lates its combined weight and broadcasts it to its
neighbors. The node with highest combined weight is
elected as CH. Cluster maintenance is invoked when a
node moves outside the boundaries of its cluster and/or
when cluster head consumes most of its battery energy.
5. Comparison of Clustering Schemes
They are many clustering schemes for MANETs avail-
able in the literature. To evaluate these schemes, we have
to decide about the metrics to use for the evaluation.
Based on our review and the work presented in [11,29],
we summarize the comparison in Table 1. We can ob-
serve in Table 1, the total overheads increase when clusters
number is high and CHs change frequently. The weight
based clustering scheme performs better than ID-Neighbor
based, topology based, mobility based and energy based
clustering. The weight based clustering scheme is the
most used technique for CH election that uses combined
weight metrics such the node degree, remaining battery
power, transmission power, and node mobility etc. It
achieves several goals of clustering: minimizing the
number of clusters, maximizing lifespan of mobile nodes
in the network, decreasing the total overhead, minimizing
the CHs change, decreasing the number of re-affiliation,
improving the stability of the cluster structure and ensur-
ing a good resources management (minimize the band-
width consumption) .
6. Conclusions
In this survey, we first presented fundamental concepts
about clustering, including the definition of clustering,
design goals and objectives of clustering schemes, advan-
tages and disadvantages of clustering, and cost of network
clustering. Then we classified clustering schemes into five
categories based on their distinguishing features and their
objectives as: Identifier Neighbor based clustering, To-
pology based clustering, Mobility based clustering, En-
ergy based clustering, and Weight based clustering. We
reviewed several clustering schemes which help organize
MANETs in a hierarchical manner and presented some
of their main characteristics, objective, mechanism, and
performance. We also identified the most relevant met-
rics for evaluating the performance of existing clustering
schemes. Most of the presented clustering schemes focus
on important issues such as cluster structure stability, the
Copyright © 2013 SciRes. CN
Copyright © 2013 SciRes. CN
Table 1. Comparison of clustering schemes
SchemesBased on CHs
Election Cluster
Radius Overlapping
Clusters Clusters
Number CH
Change Cluster
Stability Total
LCA [20] ID-Neighbor Lowest ID One-HopPossible High Very High Very Low High
LCC [30] ID-Neighbor Lowest ID One-HopPossible High High Low High
ACA [31] ID-Neighbor Lowest ID One-HopNo High Moderate Low High
D-cluster [13] ID-Neighbor Node ID K-Hop No High Moderate Low Very High
HCC [21] Topology Highest degree One-HopNo High Very High Very Low High
3hBAC [34] Topology Highest degree One-HopNo Moderate Relatively
High Low Very High
α-SSCA [35] Topology Node degree One-HopNo Moderate Relatively
Low High Low
Cluster [36] Topology Associativity
and node degreeK-Hop Yes Moderate
Low High Relatively
MOBIC [38] Mobility Lowest mobilityOne-HopPossible Relatively
High Low Relatively
High High
mobility prediction [39] Mobility Node stability One-HopYes Relatively
Low Low Relatively
MPBC [40] Mobility Lowest mobilityOne-HopYes Relatively
Low Low High Low
MobDHop [41] Mobility Lowest mobilityK-Hop No Low Low Very High Low
Cross-CBRP [33] Mobility Node ID and
mobility One-HopYes Relatively
High Low
MPGC [15] Energy Highest energy One-HopYes Moderate Relatively
FWCABP [42] Energy Lowest weight One-HopPossible Low Low High Relatively
ECEC [43] Energy Highest energy One-HopYes Moderate Low Relatively
FWCA [45] Weight A combined
weight metric One-HopPossible Low Low High High
Sbca [46] Weight A combined
weight metric One-HopNo Low Low High
EWBCA [47] Weight A combined
weight metric One-HopNo Low Low Very High
total control overhead of cluster formation and mainte-
nance, etc. In addition, the different categories of clus-
tering schemes have different characteristics and objec-
[1] S. Corson, J. Macker., “Mobile Ad hoc Networking
(MANET),”RFC 2501, 1999.
[2] N. Chauhan, L. K. Awasthi, N. chand, V. Katiyar and A.
Chug, “A Distributed Weighted Cluster Based Routing
Protocol for Manets,” World Academy of Science, Engi-
neering and Technology 75, 2001, pp.1167-1171.
[3] B. A. Correa, R.C. Hincapie and Laura Ospina, “Survey
on Clustering Techniques for Mobile Ad Hoc Networks,”
Revista Facultad de Ingenierí
No. 41, 2007.
[4] S. A. Ade and P.A. Tijare , “Performance Comparison of
AODV, DSDV, OLSR and DSR Routing Protocols in
Mobile Ad Hoc Networks,” International Journal of In-
formation Technology and Knowledge Management, Vol.
2, 2010, pp. 545-548.
[5] P. Suman, D. Bisen, P. Tomar, V. Sejwar and R. Shukla,
“Comparative Study of Routing Protocols for Mobile
Ad-Hoc Networks,” International Journal of Information
Technology & Knowledge Management, 2009.
[6] X. Hong, K. Xu, M. Gerla, “Scalable Routing Protocols
for Mobile Ad Hoc Networks,” Onr Minuteman project,
[7] B. Lee, C. Yu, S. Moh, “Issues in Scalable Clustered
Network Architecture for Mobile Ad Hoc Networks,”
Handbook of Mobile Computing, 2004.
[8] B. Maqbool, M.A.Peer, “Classification of Current Rout-
ing Protocols for Ad Hoc Networks,” IJCA, 2010.
[9] Y.Y. Su, S.F. Hwang, and C.R. Dow, “An EfficientClus-
ter-Based Routing Algorithm in Ad Hoc Networks with
Unidirectional Links,” Journal of Information Science
and Engineering 24, 2008, pp.1409-1428.
[10] R. Agarwal and M. Motwani, “Survey of Clustering Al-
gorithms for MANET,” International Journal on Com-
puter Science and Engineering ,Vol. 1, No. 2, 2009.
[11] J. Y. Yu and P. H. J. Chong. “A Survey of Clustering
Schemes for Mobile Ad Hoc Networks,” IEEE Commu-
nications Surveys and Tutorials, Vol. 7. No. 1, 2005, pp.
32–48. doi:10.1109/COMST.2005.1423333
[12] A. Abbasi and M. F. Younis., “A Survey on Clustering
Algorithms for Wireless Sensor Networks,” Computer
Communications, Vol. 30, No. 14-15, 2007, pp. 2826–
2841. doi:10.1016/j.comcom.2007.05.024
[13] A. Amis, R. Prakash, D. Huynh and T. Vuong. “Max-Min
D-Cluster Formation in Wireless Ad Hoc Networks,” In-
focom, Vol. 1, 2000, pp. 32–41.
[14] S. Chinara, S. Rath, “A Survey on One-Hop Clustering
Algorithms in Mobile Ad Hoc Networks,” Journal of
Network and Systems Management, Vol. 17, No. 1-2,
2009, pp. 183-207. doi:10.1007/s10922-009-9123-7
[15] J. J.Yin Leu, M.H. Tsai, T. C. Chiang and Y. M. Huang
"Adaptive Power-Aware Clustering and Multicasting
Protocol for Mobile Ad Hoc Networks," 2006.
[16] O. Younis and S. Fahmy “HEED: A Hybrid, Energy-
Effcient, Distributed Clustering Approach for Ad Hoc
Sensor Networks,” IEEE Transactions on Mobile Com-
puting, Vol. 3, No. 4, 2004, pp. 366–379
[17] M. Anupama and B. Sathyanarayana “Survey of Cluster
Based Routing Protocols in Mobile Ad hoc Networks,”
International Journal of Computer Theory and Engi-
neering, Vol. 3, No. 6, 2011.
[18] N. Gupta, M. Shrivastava, A. Singh, “Cluster Based on
Demand Routing Protocol for Mobile Ad Hoc Network,”
IJERT, Vol. 1, No. 3, 2012.
[19] H. Wu, Z. Zhong and L. Hanzo.” A Cluster-head Selec-
tion and Update Algorithm for Ad Hoc,” In IEEE Globe-
com, 2010.
[20] A.Ephremides, J. E. Wieselthier and D.J. Baker. “A De-
sign Concept for Reliable Mobile Radio Networks with
Frequency Hopping Signaling,”IEEE, 1987, pp. 56-73.
[21] M. Gerla and J.T. Tsai. “Multicluster, Mobile, Multime-
dia Radio Network. Wireless Networks,” 1995.
[22] S. Basagni, “Distributed clustering for ad hoc networks,”
[23] M. Chatterjee, S.K. Das, D. Turgut, “WCA: A Weighted
Clustering Algorithm for Mobile Ad Hoc Networks,” Vol.
5, No. 2, 2002, pp. 193-204.
[24] W. Choi, M. Woo, “A Distributed Weighted Clustering
Algorithm for Mobile Ad Hoc Networks,” Advanced In-
ternational Conference on Telecommunications, 2006.
[25] C. Johnen and L.H. Nguyen, “Self-stabilizing weight-
based clustering algorithm for ad hoc sensor networks,”
ALGOSENSORS, Italy, Vol. 4240, 2006. pp. 83-94.
[26] U.C. Kozat and L. Tassiulas, “Virtual Dynamic Backbone
for Mobile Ad Hoc Networks,” ICC, 2001.
[27] A. B. McDonald and T. Znat, “A Mobility-based Frame
Work for Adaptive Clustering in Wireless Ad Hoc Net-
works,” IEEE Journal on Selected Areas in Communica-
tions,Vol. 17,No. 8, 1999, pp. 1466-1487.
[28] M. Anupama and B. Sathyanarayana. “Survey of Cluster
Based Routing Protocols in Mobile Ad hoc Networks,”
International Journal of Computer Theory and Engi-
neering, Vol. 3, No. 6, 2011.
[29] S. J. Francis and E. B. Rajsingh, “Performance Analysis
of Clustering Protocols in Mobile Ad hoc Networks,”
Journal of Computer Science, 2008, pp. 192-204.
[30] C.C. Chiang, H.K. Wu, W. Liu and M. Gerla., “Routing
In Clustered Multihop, Mobile Wireless Networks With
Fading Channel,” SICON, Singapore, 1997, pp. 197-212.
[31] C. R. Lin and M. Gerla., “Adaptive Clustering for Mobile
Wireless Networks,” IEEE Journal on Selected Areas in
Communications, Vol. 15, No. 7, 1997, pp.1265-1275.
[32] G. Chen, F. G. Nocetti, J. S. Gonzalez and I. Stojmenovic.
“Connectivity Based k-Hop Clustering in Wireless Net-
works,” 5th HICSS, 2002.
[33] O. Flauzac, B. S. Haggar and F. Nolot, “Self-stabilizing
Clustering Algorithm for Ad Hoc Networks,” ICWMC,
No. 24-29, 2009.
[34] J. Y. Yu and P. H. J. Chong, “3hBAC (3-Hop between
Adjacent Clusterheads): A Novel Non-Overlapping Clus-
tering Algorithm for Mobile Ad Hoc Networks,” IEEE
PACRIM, 2003.
[35] B. Guizani, B. Ayeb and A. Koukam., “Impact of Stabil-
ity in Cluster Based Link State Routing Protocol for
Self-Organizing Networks,” 7th ICWMC, 2011.
[36] A. Ramalingam, S. Subramani and k. Perumalsamy. ,
“Associativity-based Cluster Formation and Cluster
Management in Ad Hoc Networks,” HiPC, 2002.
[37] F. Li, S. Zhang, X. Wang, X. Xue and H. Shen, “Vote-
Based Clustering Algorithm in Mobile Ad Hoc Net-
works," International Conference on Networking Tech-
nologies, 2004.
[38] P. Basu, N. Khan, and T.D.C. Little. “A Mobility Based
Metric for Clustering in Mobile Ad Hoc Networks,”
ICDCSW, 2001, pp. 413-418.
[39] C. Konstantopoulos, D. Gavalas and G.Pantziou. “Clus-
tering in Mobile Ad Hoc Networks through Neighbor-
hood Stability-based Mobility Prediction,” 2008.
[40] M. Ni, Z. Zhong and D. Zhao. “MPBC: A Mobility Pre-
diction-Based Clustering Scheme for Ad Hoc Networks,”
IEEE TVT, Vol. 60, No. 9, 2011.
[41] A.H. Hussein, A.O. Abu Salem and S. Yousef., “A flexi-
ble Weighted Clustering Algorithm Based on Battery
Power for Mobile Ad hoc Networks,” ISIE, 2008.
[42] A. Fathi and H. Taheri. “Enhance Topology Control Pro-
tocol(ECEC) to Conserve Energy based clustering in
Wireless Ad Hoc Networks,” 3rd IEEE ICCSIT, 2010.
[43] Ya Xu, S. Bien, Y. Mori, J. Heidemann, D. Estrin, “To-
pology Control Protocols to Conserve Energy in Wireless
Ad Hoc Networks,” CENS Technical Report , 2003.
[44] Z. El-Bazzal, M. Kadoch, B.L. Agba, F. Gagnon and M.
Bennani, “A Flexible Weight Based Clustering Algorith-
min Mobile Ad hoc Networks,” International Conference
on Systems and Networks Communications, 2006.
[45] S. Adabi, S. Jabbehdari, A.M.Rahmani and Se. Adabi.
“Sbca: Score Based Clustering Algorithm for Mobile Ad
Hoc Networks,” the 9th ICYCS, 2008.
[46] M.R Monsef, S. Jabbehdari and F. Safaei. “An Efficient
Weight-Based Clustering Algorithm for Mobile Ad-hoc
Networks,” Journal of Computing, Vol. 3, 2011.
[47] R. P. Selvam and V. Palanisamy, “Stable and Flexible
Weight based Clustering Algorithm in Mobile Ad hoc
Networks,” International Journal of Computer Science
and Information Technologies, 2011, pp. 824-828.
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