International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 4, April 2013
ISSN 2319 - 4847
Distributed Data Aggregation Based Energy
Efficient Cluster Algorithm for Heterogeneous
Wireless Sensor Network
C.Divya1, N.Krishnan2 and P.Krishnapriya3
1
Assistant Professor, Center for Information Technology and Engineering,
Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India
2
Professor and Head, Center for Information Technology and Engineering,
Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India
3
PG Scholar, Center for Information Technology and Engineering,
Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India
ABSTRACT
WSNs are highly affected by the energy dissipation of the nodes. The sensor nodes are usually used to collect and report
application-specific data to the sensor node, known as a sink node. A primary goal in the design of wireless sensor networks is
to extend the network lifetime and to build the energy efficient network. Clustering technique is one of the most efficient
techniques which provide the energy conservation in wireless sensor networks. This paper analyzes the performance of
(DDAEEC) Distributed Data aggregation energy efficient clustering protocol. DDAEEC allows more number of data’s to be
sent from the cluster head to the base station within a particular time interval. It mainly performs the time reduction of data
transmission over the network, thereby increasing the energy efficiency of the network.
Keywords: Heterogeneous environment, Energy-efficiency, Data Aggregation, Network Lifetime, Delay minimization.
1. Introduction
Wireless sensor network composed of hundreds or thousands of low power devices known as nodes which sense the
physical environment in terms of sound, temperature, vibration, pressure, etc. Homogeneous and heterogeneous are the
two types of nodes in WSN. Based on this concept, we classify whether they operate in flat topology or hierarchal
topology. Flat topology is treated as same nodes, hierarchal topology forms different types of nodes which are grouped
together called as clustering [1] [2].
WSN involves so many clustering techniques such as LEACH [3], [8], PEGASIS [15], HEED [4], TEEN [14], EESR
[12], Directed Diffusion and SEP for increasing the energy consumption [13] of the sensor network and its lifetime.
This clustering technique can be used for grouping the sensor nodes. After grouping the nodes, choose the cluster head
among the cluster members. This cluster head provides data communication between sensor nodes and it performs data
aggregation also. Wireless sensor network supports the heterogeneous networks efficiently then homogeneous
networks. The Heterogeneous networks [6], [11] perform environmental watching and sensor nodes watch a
multiplicity of objects.
DEEC [4], [9] is the cluster based algorithm which selects the cluster head on the basis of probability of ratio of
residual energy and the average energy of the network. The heterogeneous network [6] clustering algorithm in the
DEEC extends the lifetime of the network. Distributed Data Aggregation Energy Efficient Clustering protocol also fit
for the multilevel heterogeneous networks.
Each node in the sensor network transmits sensing data to the base station through a cluster-head. The cluster-heads,
which are chosen by certain clustering algorithms, aggregate the data of their cluster members and send it to the base
station, from where the end-users can access the data. DDAEEC algorithm allows a more number of data to send from
cluster head to base station in a particular time interval. Thus the time delay to reach the cluster head was minimized
and thereby increasing the energy efficiency of the network.
Applications of sensor networks are Disaster Relief, Military, Habitat Monitoring, Health Care, Emergency Rescue
operation, Environmental monitoring, Home networks, tracking, area monitoring, traffic controlling etc.
Volume 2, Issue 4, April 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 4, April 2013
ISSN 2319 - 4847
The structure of the paper is organized as follows. Section 1 describes an overview of routing protocols for wireless
sensor network; Section 2 describes the details of DDAEEC algorithm; section 3 shows the simulation results followed
by the conclusion of the work.
2. OVERVIEW OF ROUTING PROTOCOLS
LEACH [3], [8] (Low Energy Adaptive Clustering Hierarchy) is a hierarchical routing protocol which uses the random
rotation of the nodes, the cluster-heads to evenly distribute energy consumption in the network. This protocol mainly
works on homogeneous network. In LEACH protocol the nodes in a sensor network are arranged in a small cluster and
choose one of the node as cluster head among cluster members. CH selected by random, aggregates the data collected
by the nodes and leads to a limit the traffic generated in the network. LEACH uses TDMA communication protocol to
decrease interference between the clusters.
HEED [4] (Hybrid Energy-Efficient Distributed clustering), Four primary goals are in this clustering algorithm:
extending network lifetime, eliminating the clustering process within a number of iterations, minimizing control
overhead, producing well-distributed cluster heads and compact clusters. These algorithms are used to form the nodes
into clusters then choose Cluster head by considering the communication distance and the remaining energy. It does
not require special node capabilities such as location awareness. The advantages of HEED are it does not make
assumptions about node distribution, and operates correctly even when nodes are not synchronized.
TEEN [14] is a cluster based hierarchical routing protocol and it is based on LEACH. Using this protocol the data can
be sensed continuously but transmission of data is not done frequently. TEEN uses LEACH’s approach to form clusters.
Hard threshold (HT) and soft threshold (ST) are two types of threshold mode used in this algorithm. In HT mode the
sensed attribute will be within the range of interest in order to send the data. In ST mode any changes in the value of
the sensed attribute will be transmitted. The nodes sense their environment frequently then store the sensed value for
transmission. If it satisfies the below conditions then only the node transmits the sensed value:
a. If sensed value greater than the hard threshold (HT).
b. If sensed value is not hard threshold and greater than or equal to soft threshold (ST).
In TEEN, cluster head always waits for their time slot for data transmission. Suppose node has no data then time slot
may be wasted.
PEGASIS [15] is a chain-based power efficient protocol performance based on LEACH protocol. All the nodes have
information about all other nodes and each node has the ability of transmitting data to the base station directly. Nodes
have global knowledge of the network; chain can be construed by greedy algorithms. Each node transmits and receives
data from the closest node of its neighbors. In each round, the node is chosen randomly (leader) from the chain. The
aggregated data are sent to the BS using leader. This algorithm eliminates the overhead of the dynamic cluster
information and it reduces the number of transmissions.
Directed diffusion [17], [18] is a data-centric (DC) and application-aware protocol in which data generated by sensor
nodes is known as attribute-value pairs. In the DC protocol, the data coming from different sources are combined and
thus eliminating redundancy, reduce the number of transmissions, saving energy and extending network lifetime. In
directed diffusion, a base station diffuses a query towards nodes in the concerned region. The query or interest is
diffused through the network. Each sensor receives the interest and sets up a gradient toward the sensor nodes. The
intermediate nodes may aggregate their data depending on the attribute-value pairs thus reducing the communication
cost. This algorithm saves energy by selecting the optimal path and processing data in the network.
DEEC [4] (Distributed Energy Efficient Cluster) is used in heterogeneous wireless sensor network. After placing the
nodes, nodes are clustered based on the average energy of the network and the ratio between the residual energy of
each node, the cluster heads are elected. After that sensed data must be transmitted and received between base station
and cluster head. It increases the energy efficiency of network and extend lifetime also.
In multi-level heterogeneous networks [5], the clustering algorithm should consider the discrepancy of the initial
energy. In the heterogeneous network, the reference value of each node should be different to the initial energy. In the
Leach-Heterogeneous system, the energy efficient and life time is increased near to 60% than Leach homogeneous
Volume 2, Issue 4, April 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 4, April 2013
ISSN 2319 - 4847
system; Leach-heterogeneous system considerably reduces dissipation which increases the total lifetime of wireless
sensor networks.
Compared to above clustering techniques this paper proposes a DDAEEC algorithm to save energy consumption and to
extend the lifetime. Placing an N number of nodes in the network and then grouping node is known as clustering.
Mainly Clustering techniques can be used to increase the energy efficiency of the network in order to minimize time
delay in for communication between the nodes. Then select CH among cluster members, cluster head act as a local base
station. Considered here, each node transmits sensing data to the base station through a cluster-head. CH aggregates
the data of their cluster members and sends it to the base station, from where the end-users can access the data.
2. DISTRIBUTED DATA AGGREGATION ENERGY EFFICIENT CLUSTER PROTOCOL
This work proposes and evaluates a new distributed data aggregation energy-efficient cluster algorithm for
heterogeneous wireless sensor network. Here, all the nodes use the initial and residual energy level to define the cluster
heads. It does not require any global knowledge of energy at every election round. This DDAEEC algorithm allows a
more number of data to send from cluster head to a base station in a certain time interval. Thus the time delay to reach
the base station was minimized and in order to increase the energy efficiency of the network. DDAEEC algorithm
involves the following steps,
1. Placing sensor nodes in the network.
2. Grouping the nodes
3. Cluster Head selection,
4. Minimum distance estimation
5. Estimate average energy of node in each round.
The first step is placing sensor node in the network in this work, the heterogeneous network model has been assumed
which exists practically. Let there are N number of nodes which are distributed randomly within a M M square region.
The nodes should have different levels of energy, base station located at the centred of region. The Second step is
clustering; that means, the deployed N nodes in the region should be clustered. Clustering means grouping of N nodes,
in which each node has different levels of energy in the heterogeneous wireless sensor network. The third step is cluster
head creation; Next to select a cluster head, for data communication between sensor nodes and the base station
efficiently. It performs data aggregation of their cluster members and sends it to the base station. At the DDAEEC
algorithm cluster head are elected by initial energy and residual energy of each node.
2.1. Estimating average energy of networks
The average energy E (r ) is used as the reference energy for each node. It is the ideal energy of each node, in such
ideal situation, the energy of the network and nodes are uniformly distributed. Estimate the average energy E (r ) of
each node in rth round as follows
E (r ) 
1
E total
N
*
n
/ r / R
(1)
Here, R denotes the total rounds of the network lifetime. It means that every node consumes the same amount of energy
in each round, which is also the target that energy-efficient algorithms should try to achieve. As a result, the actual
energy of each node will fluctuate around the reference energy
lEt  lERx  lEDA * lE d4 , d  d0

fs
E (l, d)  
16
Tx
lEt  lERx  lEDA * lE d , d  d0

mp
Where Et is the total energy of the network, ERx is the receiver energy, and Efsd4 or Empd16 is the amplified energy that
(2)
depends on the transmitter amplifier model. EDA is energy of aggregation, for aggregate all sensed data this energy
multiplies with energy per bit and energy per area depends on the conditions for increase the energy efficiency of the
network. There are two conditions is used to calculate transmitter energy, suppose if the distance less than the initial
distance use energy per bit and distance greater than or equal to the initial distance use energy per area Each non-
Volume 2, Issue 4, April 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 4, April 2013
ISSN 2319 - 4847
cluster-head send L bits data to the cluster-hea around. Thus the total energy dissipated in the network during a round
is equal to:
E round  L ( 2 NE elec  NE DA  kE mp d 16 toBS  NE fs d 4 toCH )
(3)
Where k is the number of clusters, EDA is the data aggregation cost in the cluster-heads, dtoBS is the average distance
between the cluster-head and the base station, and dtoCH is the average distance between the cluster members and the
cluster-head.
dtoCH
M
=
2 K
dtoBS
=
0.765
M
2
By setting the derivative of Eround with respect to k to zero, we have the optimal number of clusters as
(4)
k opt 
N
2
Efs m
Emp d 16 toBS
(5)
Substituting Esq. (4) and (5) into Eq. (3), we obtain the energy E round dissipated during a round.
2.2. Minimum distance Estimation
This method is used to calculate distance and minimum distance between each node and base station. In order to send
the data from cluster head to base station based on this method evaluate energy efficiency of network and delay
minimization in a heterogeneous network.
E n  E * ( LEt  LE Rx  LE DA * LE mp * ( d 16 ))
(6)
If the distance less than the initial distance,
E n  E * ( LEt  LE Rx  LE DA * LE fs * (d 4 ))
(7)
Now calculate the minimum distance among the resultant distance.
For cluster minimum distance, Check the cluster minimum distance~=0, if so then,
If the minimum distance greater than the initial distance (d0),
E n  E * ( LE t  LE Rx  LE DA * LE mp * (min_ dis16 ))
(8)
If the minimum distance less than the initial distance,
E n  E * ( LE t  LE Rx  LE DA * LE fs * (min_ dis 4 ))
(9)
Otherwise, the cluster minimum distance=0, then
If the minimum distance greater than the initial distance (d0),
En  E * ( LEt  LERx  LEDA * LEmp * (min_ dis16 ))
(10)
If the minimum distance less than the initial distance
E n  E * ( LE t  LE Rx  LE DA * LE fs * (min_ dis 4 ))
(11)
After select, among the minimum distance then send data from the CH to BS through the selected minimum distance.
In DDAEEC algorithm is used to increase the energy efficiency of network and perform time reduction of data
transmission, parameters are described in Table 1.
Volume 2, Issue 4, April 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 2, Issue 4, April 2013
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Table 1: Parameters Specification
Value
10 nJ/bit
25 pJ/bit/m2
0.0020pJ/bit/m4
0.7 J
8 nJ/bit/message
Parameter
Eelec
Ef s
Emp
E0
EDA
d0
80m
The above parameters are used in the energy calculations and distance between CH and BS. Table 1 refer from [4]. In
our DDAEEC protocol, used to deliver more number of sensing data to base station thereby achieved energy efficiency
and reduce data transmission time.
3. SIMULATION RESULTS
The performance of DDAEEC protocol using MATLAB is evaluated. Consider a sensor network with R = 50 rounds,
assume the base station is in the center of the region. The performance of DDAEEC compared with other protocols.
The radio parameters used in our simulations are shown in Table 1.
DDAEEC
6000
DDAEEC
5000
y(data)
4000
3000
2000
1000
0
0
5
10
15
20
25
x(time)
30
35
40
45
50
Figure 2: Number of data received in base station for DDAEEC protocol.
Figure 2 shows the detailed performance of DDAEEC algorithm, for time duration 50 Sec, around 5000 number of data
can be transmitted from cluster head to base station..
leach vs DEEC vs DDAEEC
6000
leach
DEEC
DDAEE C
5000
y(data)
4000
3000
2000
1000
0
0
5
10
15
20
25
x(time)
30
35
40
45
50
Figure 3: Comparison of LEACH, DEEC, DDAEEC.
Figure 3 shows that detail views of the performance of clustering protocol such as LEACH, DEEC and DDAEEC.
Comparing to DEEC, DDAEEC algorithm can send 5000 messages at time duration 50 Sec whereas DEEC can send
2500 messages at time duration 50 Sec, LEACH can send 1500 messages at time duration 50sec. This means DDAEEC
algorithm performs delay minimization, to reduce time for data transmission over the network. So this algorithm more
efficient than DEEC and LEACH.
4. CONCLUSION
The proposed DDAEEC protocol can take initial energy and residual energy at the same time and also implement the
same procedure for selection of cluster head based on DEEC algorithm. But it compared to DEEC, DDAEEC algorithm
send more message at particular time duration. Performance of the algorithm for heterogeneous networks in terms of
delay Minimization was performed, that means reducing the time duration for data transmission over the network,
thereby increasing the energy efficiency of the network and result was compared with existing protocol LEACH and
DEEC, the comparison result shows that the DDAEEC algorithm has 40% increase in message transmission when
compared to existing protocols.
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Volume 2, Issue 4, April 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 4, April 2013
ISSN 2319 - 4847
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Author
C.Divya received her M.E degree in Communication systems in the year 2010 from SSN college of
Engineering, Anna University, Chennai. She is currently working as Assistant Professor in Centre for
Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India. Her
Research interests are in wireless Sensor Networks and Remote Sensing. She has published several papers
related to wireless sensor Networks and its Security.
Prof.N.Krishnan is working as Prof. & Head , Centre For Information Technology and Engineering,
Manonmaniam Sundaranar University. He did his M.Tech at Cochin University of Science and Technology
and obtained Ph.d from Manonmaniam Sundaranar University. He has operated several extra mural
research projects funded by DRDO, Govt. of India, CDAC,etc. He has published nearly 100 research papers
both in National and International. He has also authored several books on information technology. He has guided
nearly 20 ph.d scholars. He is the chair for IEEE Podhigai Subsection.
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