International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 5 – Aug 2014
Self-Organizing Clustering Methods for EnergyEfficient Data Gathering in Sensor Networks
Sumit Kumar Chauhan#1, Bhagirathi Pradhan#2
#1
Post Graduate Scholar, Department of Electronics and Communication,
Dehradun Institute of Technology, Dehradun, India
#2
Assitant Professor, Department of Electronics and Communication,
Dehradun Institute of Technology, Dehradun, India
By deploying wireless sensor nodes and
composing a sensor network. One can remotely obtain
information about the behaviour, conditions and positions of
entities in a region. Since sensor nodes operate on batteries,
energy-efficient mechanisms for gathering sensor data are
indispensable to prolong the lifetime of a sensor network as
long as possible. A sensor node consumes energy in
observing its surroundings, transmitting data and receiving
data. Especially, energy consumption in data transmission
scales proportionally to the nth power of the radius of the
radio signal. Therefore, cluster-based data gathering
mechanisms effectively save energy. In cluster-based data
gathering, since each node can save transmission power and
the number of collisions is also reduced, sensor networks
can live for longer period. In clustering, however, we need to
consider that a cluster-head consumes more energy than the
other nodes in receiving data from cluster members, fusing
data to reduce the size and sending the aggregated data to
base station. In this paper, we synthesis existing clustering
algorithms [1] in Wireless Sensor Networks and compare
them in terms of their stable operation period(SOP) and
highlights the challenges in clustering.
Abstract—
Various techniques had been proposed to make network more
energy-efficient.
In this paper, we focuses on balancing energy consumption
within the network and also avoid network wide broadcasting
of control packets. The rest of the paper is organized as follow:
The rate of energy consumption of nodes in a particular region
is approximately equal with this the lifetime of nodes in a
particular region same and is denoted by Ʈ(i). EC algorithm
targets to equalize the lifetime of all regions and to maintain
equal energy levels at all regions throughout the lifetime of
Wireless Sensor Networks.
II. RELATED WORK
A. Analysis of Energy consumption in Cluster-based Datagathering
To prolong the lifetime of a sensor network, Cluster radii
must be carefully determined. For example, if a radius is large
a cluster-head consumes much energy in receiving sensor data
from its members and sending the aggregated data to the next
hoop node. In addition, cluster members consume much
energy in sending their data to the distant cluster-head.
However, at the same time, since the number of nodes in a
cluster increases, a sensor node becomes a cluster-head less
Keywords— WSN, Self-Organizing Clusters, LEACH, HEED, frequently. On the contrary, if a radius is small, the amount of
MRPUC, EEUC, EC algorithms.
energy consumed in data-gathering becomes small at the
sacrifice of frequent rotation of the role of cluster-head. In
I. INTRODUCTION
addition to intra-cluster communications, the distance to a
A Wireless Sensor Network (WSN) consists of autonomous, base station also affects the energy consumption of a cluster.
self-organizing, lightweight sensor nodes, which can monitor If a cluster is close to a base-station, a cluster-head has to
physical or environmental conditions. Wireless Sensor Nodes relay more sensor data from its outside region in multi-hop
are the nodes, which can sense, compute and communicate the communication among cluster-heads. In this section, for each
data. It is possible only due to miniaturization of various cluster-head to independently determine an appropriate radius
component, which is made possible by MEMS technology. A of its cluster, we analytically investigate the relationship
sensor node consists of microcontroller, battery, analog to among the energy consumption, cluster radius and the
digital converter, sensing device. All these components have distance of a cluster-head to the base-station.
their own function. There is some features of WSN which is B. Energy Consumption Model in Cluster-based Datamakes it more reliable for various applications, these factors
gathering
include fault tolerance, scalability, production cost, hardware
To generalize the problem, we consider energy consumed
constraint,
sensor network topology,
environment, in gathering data from cluster members to a cluster-head and
transmission media and power consumption. WSN have sending aggregated data to the base station by multi-hop
various application like military, environmental, health and transmission among cluster-heads. Therefore, since we do not
home applications. The main challenge for WSN is energy consider how clusters are organized, results in this section can
consumption. There is lot of energy consumes while be applied to other cluster-based data-gathering methods. We
transmitting the data but sensor nodes have limited energy. ignore energy consumed in MAC layer processing in carrier
sense, collision detection and retransmission. The energy
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International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 5 – Aug 2014
consumed in transmitting and receiving a k bit message at d,
m is given in equation 2.1 through 2.3
Etransmit(k,d) = k*(Eelec+efs*d2), if d<do
2.2.1
Etransmit(k,d) = k*(Eelec+emp*d4), if d≥do
2.2.2
Erecevice(k) = k*Eelec
2.2.3
A sensor node consumes Eelec(nJ/bit) in transmitter or
receiver circuitry and e(pJ/bit/m2) in transmitter amplifier. The
threshold do is introduced to take into account the effect of
multipath fading.
The total energy Ecluster consumed in one cluster in a single
data collection round(DCR) is given by equation 2.4
Ecluster = Eall m-h + Ehead1 + Ehead2 + Ehead3 + Eproc
2.2.4
Eall m-h corresponds to the total amount of energy consumed
by cluster members in sending their sensor data to a clusterhead. Eproc is energy consumed in data processing at clusterhead. Ehead1 is the energy consumed by a cluster-head in
receiving sensor data from other cluster-heads, which it has to
forward toward the base-station. Finally, Ehead3 stands for the
energy consumed by a cluster-head in sending the aggregated
sensor data to the next hop cluster-head or the base-station.
The amount of energy consumed per sensor node, Enode
averaged over multiple rounds, where the role of cluster-head
is rotated, is given by the following equation.
Enode = Ecluster /n
2.2.5
Where n denotes the number of sensor nodes in a cluster.
When we assume the uniform distribution of sensor nodes, the
following equation hold.
n = ρ * Scluster
2.2.6
where ρ is the density of the sensor nodes and Scluster
corresponds to the area from which a cluster-head gathers
sensor data.
In this , we consider a rectangular monitoring region of
width W and length X. Base-station is located outside the
network. We assume that sensor nodes are uniformly
distributed in the monitoring region with density ρ.
C. A Multi-hop Data Collection Protocol for WSNs
In this section, an energy-efficient multi-hop data routing
solution for WSNs organized as clusters is briefly outlined.
We will use this routing protocol for clustering solutions we
are going to compare.
The routing algorithms is based on two ideas : First,
Reducing the overhead in route discovery, and Second,
Balancing energy consumption among all CHs. To achieve
these goals a simple scheme is used and is known as reactive
routing algorithms. The network region is divided into small
size regions as shown in figure below. Where nodes at
different hop distances to the sink are denoted by different
symbols.
Figure 1 Hop distance to the sink and rectangular regions
The area in which nodes of a particular hop distance i
reside can approximately be represented by a rectangular
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region Ri. The widths of these regions may not be equivalent
and are random variables depending on the node locations and
sensor communication range. However, we can denote the
average region width by a.
A CH node in region R i chooses its next hop towards the
sink in the neighbour rectangular region Ri-1. The CH
transmits a route request packet with a range of (W2+4a2)1/2,
sufficiently large to cover R i-1. Each receiving CH in Ri-1
generates a reply packet and starts a route reply timer with an
expiration time inversely proportional to its residual energy
level. The first node that has an expired timer actually makes
the transmission of a route reply packet back to the requester
CH in Ri, while the rest quietly cancel their timers upon
hearing this reply. This guarantees that a single reply packet is
sent and thus prevents excessive message overhead.
Furthermore, by considering the residual energy levels,
priority is given to nodes with higher resources, a policy
towards balancing energy consumption in the entire network.
III. CLUSTERING ALGORITHMS FOR WSNS
A. Low-Energy Adaptive Clustering Hierarchy(LEACH)
LEACH[2] is a protocol architecture for sensor networks
that combines the ideas of energy-efficient cluster-based
routing and media access together with application specific
data aggregation to achieve good performance in terms of
system lifetime, latency and application perceived quality.
The operation of LEACH is divided into rounds. Each
round begins with a set-up phase when the clusters are
organized, followed by a steady-state phase when data are
transferred from the nodes to the cluster head and on to the
BS, as shown in fig 2.
Set-up
Steady-state
Frame
Round
time
Figure 2 LEACH cluster head selection and data transmission process
1. Cluster dead selection process of LEACH : First of all,
each node will calculate its CH probability for the current
round r +1 and is given by
and this probability is so
chosen so that the average number of cluster heads for this
round is equal to k. where the value of k can be determined
analytically or through simulation.
E[number of cluster heads] =
*1=k
3.1.1
Where N is the total number of nodes in the network.
Where,
k=
Where,
is the distance between CH and base station.
LEACH selection of cluster heads for round r+1 depends
on the information from most recent (r mod ( )) rounds ,
where nodes which have become cluster heads in the most
recent (r mod ( )) rounds are not eligible to become cluster
head for round r+1 in this way the LEACH can uniformly
distribute the load among all the nodes in the network
uniformly. Here we are using the decision function
which will decide that node can become cluster head for the
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current round or not. If the value of
it means the
node have become the cluster head in the most recent (r mod
( )) and is not eligible for this round as given in equation
3.1.2.
3.1.2
By using probabilities of becoming cluster head of nodes
given by equation 3.1.2 leach ensures that each node can
become cluster head al least once in rounds. Where
represents the total number of nodes in any cluster. After
rounds all nodes will become eligible to become cluster heads.
The total number of nodes which are eligible to become
cluster head for round r+1 are :
E[
] = N-k*
3.1.3
The average number of cluster heads generated for round
r+1 is given by the following expression.
E[no. of CHs] =
*1
= N-k*
=k
3.1.4
The expression for probability of becoming cluster head in
equation 3.1.2 assumes that all nodes have equal energy when
the network is deployed but this is not always the case. The
probability of becoming cluster head when nodes start with
different level of energies can be given as.
3.1.5
Where,
is the residual energy of node and
the total energy in the network.
is
3.1.6
Now, the average number of cluster heads can be
calculated as:
E [no. of CHs]=
*1=
3.1.7
2. Cluster formation process for LEACH : Now in this
process each non-CH node will associate with its closest
cluster head based on received signal strength indicator. For
this purpose each CH node first broadcast a CH-ADV packet
throughout the network. Each non cluster head node will
receive all CH-ADV packets and will associate with the
cluster head which generates the highest received signal
strength. To associate to cluster head the node will send a CHASSO packet to the cluster head to associate with this
message contains the cluster head id and node id. After the
formation of clusters data transmission takes place.
B. A HYBRID OF ENERGY AND COMMUNICATION
COST(HEED)
HEED[3] protocol considers residual energy of nodes as
well as intra-cluster communication cost in finding final
cluster heads, so it generates more balanced and energy
efficient clusters.
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1. Cluster heads selection in HEED: The cluster head
selection process in HEED occurs in number of iterations.
First every node elects itself to be cluster head with
probability
which is not allowed to fall below a
predefined threshold
and is given by equation 3.2.1.the
value of this threshold is inversely proportional in the initial
sensor energy.
=
×
3.2.1
Where,
is the initial percentage of cluster heads to be
selected as tentative cluster heads this value is choose to limit
the initial number of CH –broadcasts and does not create any
impact on the quality of final cluster heads.
is the
maximum energy of sensor node which is equal to fully
charge battery.
Now nodes which have
greater then
will
become tentative cluster heads and will broadcast a CH-ADV
packet in its cluster range. The cluster range is predefined and
is optimally chosen through simulation. Each non-CH node as
well as CH which have tentative status will receive this
advertisement message if they fall in cluster range. It may be
possible that a node may receive two or more CH-ADV
packets in that case the node will associate to cluster head that
results in minimum cost and become cluster member. Now all
nodes will double their
in the next iteration and again
sent CH-ADV packet. A tentative cluster head node can
become a member node if it finds a CH with minimum cost.
The iterations will continue until we find final cluster heads
and all non-CH nodes become covered. A tentative cluster
head will be considered final cluster head if its
reaches a value of 1.The communication cost in HEED
depends on node degree and further depends on the type of
clusters.
C. AN UNEQUAL CLUSTERING PROTOCOL FOR
WSN’S(MRPUC)
This method of clustering generates clusters with unequal
size to resolve the problem of HOT-SPOT which arises in
areas which are close to sink. As nodes in these areas will
void of Energy resources more quickly as compared to nodes
which are far away from base station because these nodes
have to relay their own traffic as well as the traffic coming
from outer regions of the network. This method of clustering
generates clusters with well distribution over the network.
And cluster sizes will be smaller near the sink region.
1. Cluster head selection process of MRPUC[4],[8]: First
of the base station will broadcast a BS-ADV packet which is
received by all nodes in the network. Now based on the
strength of this received BS-ADV message each node will
calculate its approximate distance to base station. With the
help of this distance each node will calculate its cluster range
with the help of equation 3.3.1.
3.3.1
Where
and
are the maximum and minimum
cluster radii which are predefined and their optimal values can
be calculated through simulations.
is the distance of
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node i to base station and
is the maximum distance
between sensor node and base station.
Now after the calculation of these cluster ranges by every
node each node will broadcast a node-ADV message this
message contains node cluster range, node id and residual
energy. The range of this node-ADV massage is same for
every node and is equal to
to ensure that each node must
receive at least one node-ADV message. At the end of this
broadcasting process each node will have a table known as
neighbour information table as shown in table 1.
Node ID
Residual Energy
Node State
1
1.2
UNKNOWN
…..
…..
…..
Table 1 showing neighbour information
Now each node will look at its table and will decide that it
can become a cluster head for the current round or not. If node
have residual energy which is higher than the residual
energies of its neighbour which have entries in its table than
the node can become cluster head for this round and sends a
CH-ADV packet to all its neighbouring nodes the
neighbouring nodes will quit the cluster head competition
immediately and become normal nodes.
2. Creation member selection process : After the selection
of cluster heads, each cluster head will transmit a CH-ADV
packet in its cluster range, this packet contains residual energy
of cluster head and its ID. each non-CH node will construct a
table of CH’s as shown in table 3 if the node lies in the range
of corresponding CH. node j will add cluster head i If
d(i,j)< .
Cluster Head ID
Distance to it d
Residual Energy E
1
70
1.9
…..
…..
…..
Table 2 list of candidate CH’s
After the creation of CH’s table the node will decide to
attach with the cluster head which will result in minimum
cost. Where the cost function is given by equation 3.3.2.
3.3.2
Where
Where
is the cost to join cluster head with cluster
range .
is the distance between node j and cluster
head k.
maximum CH energy from candidate cluster
head set and is the weighted factor which provides trade-off
between residual energy of cluster head and distance from it
and it must be optimally selected to generate good clustering
hierarchy.
To associate to the chosen cluster head the node will send a
CH-ASSO packet to CH this packet contains node ID and CHID. Now at the end of cluster formation process nodes which
are neither CH’s nor cluster members will choose a role of
cluster head for themselves. After the cluster formation
process the data transmission begins in a multi-hop fashion
towards the sink.
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D. AN ENERGY-EFFICIENT UNEQUAL CLUSTERING
PROTOCOL FOR WSN’s(EEUC)
This method of clustering[7],[9] is quite similar to
MRPUC the only difference is that it does not perform
broadcasting by all nodes for cluster head selection that’s why
this method of clustering is more energy efficient as compared
to MRPUC. This method of clustering produces well
distributed clusters as compared to HEED and LEACH and
also targets to achieve energy equalization among sensor
nodes.
1.Cluster heads selection in EEUC : As in MRPUC each
node in EEUC will calculate its approximate distance from
base station. After the calculation of distances an initial set of
tentative cluster heads will be chosen and we take this initial
percentage of tentative cluster heads to equal to T. the value of
T is predefined and its value is chosen to limit the initial
number of tentative cluster heads. After the selection of
tentative cluster heads each node will calculate its cluster
range through equation 3.4.1.
3.4.1
Where
is the maximum competition radius its value
is predefined and can be determined through simulation. is
clustering parameter and its value varies between 0 and 1 the
effect of on clustering.
are maximum and
minimum distance between sensor nodes and base station.
After the selection of tentative cluster heads each tentative
cluster head will broadcast a TCH-ADV packet in its
competition range this TCH-ADV packet contains the TCH
residual energy, cluster range and ID. Each TCH node will
construct a table of its neighbour TCH’s. Two TCH’s are said
to be neighbours if the lie in each other’s competition range.
After the creation of neighbour tentative cluster heads table
each TCH will look at its table if its energy is greater than all
TCH in its table it will become CH for this round and send a
CH-ADV packet. The TCH receive this CH-ADV packet will
quit the CH-competition immediately and transmit a QUIT
message to all its tentative CH neighbours. Upon receiving the
QUIT message from any TCH the node will remove the entry
of the corresponding TCH from its table.
2.Cluster member’s selection phase for EEUC : After the
formation of clusters in EEUC cluster head will transmits a
CH-ADV message its cluster range each non-CH node will
join the cluster head based on its distance from the cluster
head. A non- CH node will choose the closest CH to associate
with by transmitting a CH-ASSO message.
After cluster formation the data transmission begins in
multi-hop fashion.
E. ENERGY EFFICIENT CLUSTERING(EC)
EC[6] is another unequal clustering algorithm. The
algorithm begins with dividing the network into number of
regions where the length of regions are random variables here
we are considering the average length for each region and that
is a as shown in fig 3.
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Figure 3 EC divides network area into different regions
1.Cluster heads selection phase : The algorithm begins
with selecting tentative cluster heads among all sensor nodes.
The percentage of tentative cluster heads is predefined and is
T in our case, further the probability of become tentative CH
depends on the node residual energy
and average energy
in the network
so the probability of becoming tentative
cluster head can be calculated as
=T
for node j.
The main aim of the algorithm is to equalize the lifetime
of regions and maximize this lifetime. First, the algorithm
calculates the probability of becoming cluster head for every
region and is given by equation
3.5.1
Where is the number of cluster heads in region i.
The probability of becoming cluster head can be calculated
as by approximating the cluster regions with circles.
3.5.2
Where is the cluster range for region i.
Now the lifetime of any region i can be calculated as
=
, where
is the energy consumption in single
data collection round in region
. Now the aim of the
algorithm is to calculate this lifetime value for every region
and equalize them to a maximum value as shown in equation
3.5.3 and determine the corresponding probability values.
3.5.4
Now we have to calculate the probability values for each
individual region with the help of equation 3.5.4.
To calculate the probability values first of all we will
calculate the probability value for the region where is the
total no of regions. The probability value of region
is
independent of the probability values of all other regions in
the network and hence, this region has to transmit only if own
packet. So with the help of
we can calculate
.then with the help of we can calculate
and so on. the
value of lifetime is iteratively increased until we start to get
the negative and imaginary values for
. the iterations of
increasing L is stopped at largest possible value where we get
the positive and real values for
the probability values at
different node density settings is shown in fig 4.
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Figure 4 probability values for different regions at different node density
settings
After the calculation of values each node will calculate
its cluster range and each tentative cluster head will transmit a
CH-ADV message this packet contains the ID of cluster head
and its residual energy in its cluster range. if A tentative
cluster head receives a CH-ADV message which reports
higher residual energy this tentative cluster head will become
normal node. If a tentative cluster head does not reports any
CH-ADV message which has higher residual energy then that
node will become final CH.
2. Cluster member selection process for EC : Each final
cluster head will broadcast a CH-ADV message in its region.
Normal nodes will receive these broadcast messages and will
join the closest cluster head by measuring the received signal
strength and associate with the cluster head by transmitting a
CH-ASSO message and upon reception of CH-CONF
message from the corresponding cluster head.
This unequal clustering algorithm is the most energy
efficient as compared to EEUC AND MRPUC unequal
approaches. Because EC does not assume region wide
broadcasting which saves in energy in cluster formation
process, further it focuses on energy equalization of regions
and determine corresponding probability values for becoming
CH’s.
Now, the data transmission occurs in a multi-hop fashion
in the network.
IV. ADVANTAGES AND DRAWBACKS
A. Advantages : With the help of Clustering we can- reduce
energy consumption, prolong the network lifetime, cluster the
whole network with selected CH, rotate CHs for energy
distribution, Increase bandwidth reuse and thus increases
capacity of the network, Increases scalability of the network
and developers may benefits because these designs usually
reduce the cost of site development and increase the market
price of individual plots in comparison with traditional
subdivisions. These design can benefit rural areas by
reinforcing the policy of maintaining the local rural character
that is included in many comprehensive land use plane.
B. Drawbacks : Perhaps most important, local officials,
developers and the community may be predisposed toward.
Traditional development designs because they are familiar and
well understood. An education effort may be necessary to help
these groups understand the goals and advantages of cluster
development. During the planning phases, lot and home layout
may take extra work to ensure that while homes are –
Cluster/conservation development land use planning. Local
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International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 5 – Aug 2014
community located closer together, they still take advantage
of the open-space goals of the design. Methods to protect and
maintain the open space must be carefully developed,
implemented and monitored. Although not necessarily a
restricting disadvantages the management of waste water must
be carefully, designed for smaller lots. While these
disadvantages should be acknowledges and addressed, none
should preclude the use of cluster development.
V. CONCLUSIONS
Clustering is used for topology control in the network,
where topology control is a mechanism to save energy and
increasing scalability of the network. With the help of
clustering we can make wireless sensor network energy
efficient. In this paper we reviewed some existing clustering
algorithms. Some of which are equal clustering algorithms
and some are unequal clustering algorithms. We see from
simulation results that unequal clustering algorithms shows
better performance as compared to equal clustering algorithms
in multi-hop data collection scenario. Equal size clustering
algorithms suffers from HOT-SPOT problem of the network
which is resolved by unequal size clustering. From simulation
results we can see that EC out performs all clustering
algorithms such as LEACH,MRPUC,EEUC and HEED and
shows better performance in different network scenarios.
In this paper we discussed some challenges faced by
existing clustering algorithms. And these challenges motivates
for the discovery of new clustering approaches.
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ACKNOWLEDGMENT
To present this paper I would like to thanks Assistant Prof.
Bhagirathi Pradhan for his continuous motivation and
guidance.
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