IJECET - Performance Analysis of Clustering Protocols for Wireless Sensor Networks

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
INTERNATIONAL JOURNAL OF ELECTRONICS AND
6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME

COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 6, November - December, 2013, pp. 107-116
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IJECET
©IAEME

PERFORMANCE ANALYSIS OF CLUSTERING PROTOCOLS FOR
WIRELESS SENSOR NETWORKS
Sachin Gajjar1,
1

Mohanchur Sarkar2,

Kankar Dasgupta3

Department of Computer Science and Engineering, Institute of Technology, Nirma University,
S.G. Highway, Ahmedabad-382481, India
2
SATCOM and Navigation Applications Area, Space Application Centre (SAC),
Indian Space Research Organisation (ISRO), Ahmedabad - 380015, India
3
Indian Institute of Space Science and Technology, Thiruvananthapuram - 695547, India

ABSTRACT
The advances in VLSI technologies have made it possible to have integrated devices with
processing, storage, sensing, and communication capabilities. These small-scale, low-cost devices
when wirelessly connected to form the so called wireless sensor networks open the door of a plethora
of commercial and military applications. Extended network lifetime, scalability, and traffic balancing
among the nodes in the network are significant requirements for wireless sensor networks. Clustering
is one of the effectual techniques for achieving these requirements. This paper surveys, simulates and
analyzes well referred clustering protocols which significantly control lifetime of the network. These
protocols include Low Energy Adaptive Clustering Hierarchy, Energy Efficient Hierarchical
Clustering, clustering protocol by Indranil et. al., Hybrid Energy Efficient Distributed clustering and
Distributed Weight based Energy efficient Hierarchical Clustering. The quantitative and qualitative
performance metrics presented in the paper for comparison of the clustering protocols can be used to
analyze other clustering protocols. These metrics can also be used as design guidelines for
developing new wireless sensor networks clustering protocols.
Keywords: Wireless Sensor Networks, Clustering Protocols, Survey, Simulation, Analysis.
1. INTRODUCTION
The nodes in Wireless Sensor Networks (WSN) have limited computational, storage and
power capabilities. They are densely deployed and most of the time left unattended in hostile
environments where they may fail, die due to lack of power, physically damaged or may face
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environmental interference. Most of the time it is difficult or impossible to refill or replace their
batteries. This demands energy efficient techniques to be used at all the stages of WSN design. One
of the important techniques to guarantee prolonged network lifetime is to use clustering. In a
clustering protocol the geographically adjacent nodes in a WSN are separated into virtual groups
called clusters. A CH works as a local coordinator for its cluster and makes arrangement for intracluster transmission, data aggregation (combining several correlated data signals into a smaller set of
information maintaining content of original signals), data forwarding to Base Station (BS), and so
on. Clustering offers following advantages: reduces transmission collision by transmission co
ordination of CH, offers load balancing by rotating CH, does efficient node resource utilization
(when a node dies or joins a cluster, only the cluster members need to update their member
information reducing information to be stored and processed), offers good scalability and spatial
reuse (non-overlapping and non-neighbor clusters may use same frequency or code set for
transmission). This paper surveys, simulates and analyzes well referred clustering protocols which
significantly control lifetime of the network. The remainder of paper is organized as follows:
Section 2 discusses clustering protocols analyzed in paper, Section 3 presents qualitative and
quantitative metrics used to analyze the clustering protocols, Section 4 discusses simulation and
analysis of clustering protocols, and finally Section 5 gives conclusion of paper.
2. CLUSTERING PROTOCOLS FOR WIRELESS SENSOR NETWORKS
This section introduces the clustering protocols simulated and analyzed in the paper.
2.1 Low Energy Adaptive Clustering Hierarchy (LEACH): LEACH is a distributed hierarchical
clustering protocol for WSN [1]. The distributed cluster formation technique enables self
organization of nodes and data aggregation at Cluster head (CH) saves communication resources. CH
in LEACH are probabilistically selected and CH role is rotated amongst the cluster members (CM) to
evenly distribute the energy dissipation across the cluster. The operation of LEACH is divided into
rounds consisting of: set up phase where CH selection with cluster formation is done and steady state
phase in which nodes send their data to CH which further sends it to BS. During setup phase each
node chooses a Random Number (RN) between 0 and 1 and then calculates Threshold T(n) as,
P

,if n ∈ G

1

T ( n) = 1 − P * (r mod )
P

,otherwise
0


(1)

Where P is desired percentage of nodes to be CHs, r is current round, G is set of nodes that
has not been CH in past 1/P rounds. If RN<T(n) node becomes CH and broadcasts a CH
advertisement message using a non-persistent Carrier Sense Multiple Access (CSMA) Medium
Access Control (MAC) protocol [2]. Non-cluster head (NCH) nodes listen to the media and choose
to become cluster member based on largest received signal strength of advertisement message which
implies nearest CH. NCH nodes transmits cluster join request to chosen CH using a non-persistent
CSMA. CH in turn sets up Time Division Multiple Access (TDMA) based transmission schedule for
its cluster members and broadcasts it. Each NCH node sleeps all time except during it’s transmit time
saving its energy. The steady state operation is broken into frames, where NCH nodes send their
data to CH during their allocated transmission slot.

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2.2 Energy-efficient hierarchical clustering (EEHC): EEHC can be considered as an extension of
LEACH with multi-hop intra clusters and a hierarchy of CHs to route data to the BS [3]. EEHC is a
distributed randomized clustering protocol that organizes the nodes into clusters with a hierarchy of
CHs. The operation of EEHC protocol is based on two-stage clustering: single-level clustering and
multilevel clustering. In the single-level clustering stage, each node becomes a volunteer CH with a
predefined probability p and announces it to its neighbor nodes within k hops communication range.
A NCH node receiving this announcement becomes a CM of the closest cluster. Nodes that are
neither CHs nor belonging to a cluster become forced CHs. If a node does not receive a CH
advertisement within a certain time duration t, it assumes that it is not within k hops of any volunteer
CH and becomes a forced CH. At the second stage, the same mechanism is repeated recursively at
each level of CHs. Data transmission to the sink is performed using multi-hop routing through CH
organization in a multi-level hierarchy rooted at the BS. The authors provide methods to compute the
optimal values of p and k parameters that ensure minimum consumed energy.
2.3 Protocol by Indranil et. al.: Indranil et. al. propose CH selection using fuzzy logic [4]. The
operation of protocol is divided into rounds. In each round all the nodes transmit their location and
residual energy information to BS which runs fuzzy logic to decide the CH using node’s residual
energy, centrality with respect to the cluster and concentration of node in a given region as input
parameters. The output of the fuzzy logic is chance of node to become CH. Node with the highest
chance value becomes CH. The BS broadcasts the ID of node that has been selected as CH. The
steady state phase is similar to LEACH.
2.4 Hybrid Energy-Efficient Distributed clustering (HEED): HEED is another distributed
clustering protocol in which CHs are selected periodically based on sensor node’s residual energy
and intra-cluster communication cost which is a function of cluster size [5]. If the power level for
intra-cluster communication is fixed for all nodes and it is required to distribute traffic load amongst
CHs, then cost is proportional to node degree. It is proportional to 1/node degree, if it is required to
create dense clusters. If power levels for intra-cluster communication are variable then cost is
Average Minimum Reachability Power (AMRP) defined as,
M

∑ MinPwr

i

AMRP =

i =1

(2)

M

where MinPwri is minimum power level required by a node vi, 1 < i < M (number of nodes
within cluster range), to communicate with a CH u. AMRP is the mean of minimum power levels
required by nodes within the cluster range to reach the CH. The operation of HEED is divided into:
initialization phase, repetition phase and finalization phase. Clustering is initiated every TCP+TNO
seconds to select new CHs. TCP is time required to form a cluster and TNO is the time gap between
end of TCP and start of a succeeding TNO. In the initialization phase each sensor node sets its
probability of becoming a CH, CHprob, given as,

CH prob = C prob ×

109

Eresidual
Emax

(3)


where Cprob is initial percentage of CHs, Eresidual is residual energy and Emax is maximum
energy of the node. In the repetition phase, every node goes through several iterations until it finds
CH with the least cost. If it does not find a CH it elects itself as a CH and broadcasts message to its
neighbors informing them its status change. In each iteration the node doubles its CHprob value. The
execution of repetition phase stops when CHprob reaches one. In the finalization phase each node
makes a final decision on whether to become a CH based on its CHprob value or to join a cluster.
2.5 Distributed weight-based energy efficient hierarchical clustering protocol (DWEHC): DWEHC is
protocol that constructs multilevel clusters [6]. Nodes in DWEHC are location aware and transmit at
fix power levels. Clusters are multi-level with CH and its first level child node, second level child
node, and so on. TDMA is used for intracluster communication and 802.11 based MAC mechanism
is used by CHs for data transmission to BS [7]. The operation of DWEHC is divided into:
initialization phase, cluster generation phase and finalization phase. During initialization phase, each
sensor node broadcasts its (x,y) coordinates. It also discovers neighbors lying inside its transmission
range.
It then calculates its weight as below,



wweight (s) = 


∑

 u∈Nα ,c( s )

(R − d )  Eresidual (s)
×
6R  Einitial (s)


(4)

where R is the cluster range fixed for the entire network; d is the distance between node s and
its neighbor node u; Nα,c (s) is the set of the neighbor nodes of s, α is the transmitter power factor
equal to 2 or 4, and c is a constant; Eresidual(s) is the residual energy and Einitial(s) is the initial energy
of the node s. Each node then broadcasts its weight. Node with the largest weight among all its
neighbors becomes a temporary CH. A temporary CH can become an actual CH only if a certain
percentage of its neighbors elect it as their CH. This percentage is 100% in the first iteration and in
subsequent six iterations, it is decreased to (6-i)/6 where i is the number of iterations and i<6. At this
phase, the neighboring nodes are said to be the first-level child members with respect to the CH.
Each node then checks with its NCH neighbor to discover its minimum cost for reaching CH. The
sensor node uses its neighboring distance to decide whether to change its membership level or not to
reach a CH using minimum communication energy. In this process a node may switch to a new CH
different from the original one it had selected. The process goes on until all nodes agree upon the
best energy-efficient intra-cluster topology. In the finalization phase, the cluster generation process is
repeated one more time. The cluster generating process (including finalization) runs at the most
seven times as each sensor node can have a maximum of six neighbors.
3. PERFORMANCE METRICS
3.1 Quantitative metrics
• Goodput: It is the percentage of useful information excluding the control packets delivered to
the BS.
• Throughput: It is the number of bytes per second received at BS.
• Overhead: It is the total number of control packets measured in bytes required for protocol
operation.

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• Network settling time: It is time required for group of nodes to self organize and transmit first
message.
• Latency: It is the time lapse between start of data dissemination from source nodes to its arrival
at BS.
• Network Lifetime: For a network with thin node deployment it is time until first node dies; for
intense deployment it is time till half of nodes are alive, for surplus deployment it is time till last
node dies.
• Residual Energy: Energy available in node at a particular instance of time is called residual
energy.
• Network scalability: The maximum number of nodes that protocol can scale to while preserving
reliable communication.
3.2 Qualitative metrics
• Adaptation to Transmission media: Protocol design should provide solutions for wireless
transmission media problems like hidden, exposed terminal problem, High BER, fading, spatial
reuse.
• Fairness: For BS to have knowledge of entire sensed area, protocols should provide fair
bandwidth allocation and access among all nodes.
• Heterogeneity: Protocol should consider the heterogeneity between BS and sensor node in terms
of power, storage and processing capabilities at the time of work distribution among nodes and
BS.
• Time Synchronization: Time synchronization either strict or loose is required for synchronizing
sleeping cycles of nodes and BS.
4. SIMULATION AND ANALYSIS
All the simulations were carried out using Matlab [8]. Table I shows the basic simulation
settings for the simulations carried out in this paper. To reduce the occasionalism, twenty simulations
with different seeds were carried out for each scenario and average values were adopted as the
results.
TABLE I
Simulation Parameters
Parameter

Description

Nodes

1000

Network size

1000m x 1000m

BS location

(0,0)

Packet length

6400 bytes

Initial energy

0.5 J

Radio amplifier energy
Radio electronics energy

111

10 nJ/bit/m2
50 nJ/bit


4.1 Quantitative analysis: In Fig. 1 the comparison of total residual energy of the network for the
first 260 rounds is presented. The performance of DWEHC is better compared to others. This is
because in DWEHC, source nodes relay messages through their parents to the CH and in other
protocols nodes directly communicate with the CH. In direct communication the energy consumption
increases with the increase in distance between the sensor node and its CH. The total residual energy
rate is least for LEACH protocol because it uses probabilistic method for CH selection which does
not take into account any of the node parameters. As a result there might be cases when nodes with
less residual energy and/or the ones which are far from the BS might be selected as CH. This in turn
increases the energy consumption of the network. Fig. 2 shows the number of dead nodes and Fig. 3
shows aggregated data send to BS for the first 260 rounds. The number of dead nodes is minimum
and aggregated data send to BS is maximum for DWEHC because as explained earlier energy
consumption in DWEHC is minimum. HEED shows an intermediate performance in terms of
residual energy, number of dead nodes and data received at the BS. This is because HEED uses
sensor node’s residual energy and intracluster communication cost for CH selection. However,
HEED’s clustering process requires several iterations and in each iteration a lot of packets are
broadcasted which increases the overhead and degrades its performance compared to DWEHC.
Protocol by Indranil et. al. requires each node to send its location and energy information to the BS
for each round which degrades its overall performance. EEHC uses a probabilistic method for CH
selection and hence shows an inferior performance compared to all other protocols.

Fig. 1. Total residual energy of the network for the first 260 rounds

112


Fig. 2. Number of dead nodes for the first 260 rounds

Fig. 3. Total amount of data received at the BS for the first 260 rounds
For examining the scalability of protocols the number of nodes in the network was varied
from 200 to 1000 and packet received at the BS was measured. Fig. 4 shows that as the number of
source increases aggregated packets received at BS increases. This effect is maximum in DWEHC
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because it uses a multihop cluster architecture and limits the maximum number of child nodes a
parent can have. Scalability of LEACH is minimum because of the two hop architecture for data
transfer to the BS. Table 2 gives the qualitative analysis of the clustering protocols presented in the
paper. LEACH has the best goodput performance among all the protocols because it requires
minimum control packets (CH advertisement and join requests) to be sent by the nodes. DWEHC has
the best throughput performance as the number of dead nodes over rounds is minimum in case of
DWEHC. The network settling time is maximum in Indranil’s protocol since each node requires to
send its parameters to the BS for running the CH selection algorithm. The average data latency is
maximum in EEHC due to its multihop architecture. The network lifetime of DWEHC is maximum
as energy consumption in it is minimum as explained earlier.

Fig. 4. Total amount of data received at BS for varying number of nodes
TABLE II
Quantitative analysis of the protocols

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4.2 Qualitative analysis: Table 3 gives qualitative analysis of the clustering protocols presented in
the paper. To reduce inter-cluster interference LEACH uses direct-sequence spread spectrum (DSSS)
and transmitter-based code assignment in which the BS assigns a unique spreading code to each
cluster [9]. The nodes in the cluster then transmit their data to the CH using this spreading code and
the CH filters all received energy using this spreading code. Use of TDMA for intra-cluster
communication makes DWEHC and LEACH comparatively fair protocols. Inranil’s protocol runs
the computationally intensive algorithm for CH election at the BS in and hence it considers
heterogeneity between the nodes and BS. Use of TDMA and DSSS requires tight global time
synchronization among nodes in LEACH. Indranil too requires tight timing synchronization among
nodes as each node has to periodically transmit its parameters to the BS. DWEHC requires local time
synchronization. The major drawback of DWEHC is that each node requires location information to
run DWEHC which in turn increases node’s energy consumption. Use of AMRP for CH selection in
HEED provides a unified mechanism for CH selection and balances the energy distribution of the
nodes but the periodic neighbor updates increases the protocol overhead. Use of fuzzy logic selects
the best CH in Indranil’s protocol but again each node is required to periodically send its parameters
to the BS which increases the energy consumption of the nodes.
TABLE II
Qualitative analysis of the protocols

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5. CONCLUSION
WSNs due to their versatile applications have attracted attention of researchers in recent
years. There has been much research for addressing issues related to WSN. Clustering plays an
important role in the performance of a WSN, and research associated with it is always in spotlight.
Many clustering protocols have been proposed to solve issues like scalability, load balancing and
spatial reuse. This paper surveys, simulates and analyzes LEACH, EEHC, Indranil’s protocol, HEED
and DWEHC clustering protocols. The paper discusses each clustering protocol in terms of their
objective, operation and analyzes them using qualitative and quantitative metrics. With this survey it
is seen that clustering protocols for WSN still have many important issues to examine which include
stability of the cluster structure, control overhead involved in cluster formation and its maintenance,
traffic load and distribution among different clusters. Through the detailed study of these five
clustering protocols, we can conclude that DWEHC can perform comparatively well because it
provides energy efficiency by creating a minimum power cluster topology, it offers scalability with
multihop cluster architecture and limit on maximum number of child nodes a parent can have. The
weak points of the clustering protocols discussed facilitates researchers to work on overcoming them
and thereby offer more efficient and effective clustering protocols for WSNs and evaluate them using
the quantitative and qualitative metrics presented for comparison framework.
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IJECET - Performance Analysis of Clustering Protocols for Wireless Sensor Networks