Active Clustering Rule for LEACH Model in Wireless Sensor Network
V.S. Anita Sofia,
P. Ranjit Jeba Thangaiah
Wireless Sensor Network (WSN) is an assortment of spatially
distributed and dedicated autonomous nodes with limited resource which monitors
the physical and environmental conditions. Though sensor nodes of WSN have confinements
in memory, energy and computations, it has been widely used in numerous real
time applications at present scenario for sensing the environment. Clustering
of dispatch nodes and data ensemble technique affords energy conservation and
resource utilization. Aggregating the data sent by the cluster members comprehend
in draining network load and amending the bandwidth. In order to minimize the
energy dissipation of sensor nodes and optimize the resource utilization, cluster
head is elected for each cluster. The cluster heads contribute for adequate
communication with the base station. Active Clustering Rule for LEACH model
(ACR-LEACH) endeavors on the cluster formation and cluster head selection, contemplating
the factors such as energy, concentration and centrality. The approach paves
a way for increasing the lifetime of the network by adept cluster head selection
schema. The primal authority algorithm called base station control algorithm
elects the optimal cluster-heads that acquaintance with the base station. The
proposed methodology is an immense method of cluster head selection on the resource
Received: April 11, 2013;
Accepted: September 10, 2013;
Published: January 25, 2014
Wireless sensor networks are a progression of the past few years which involves
in the deployment of a large number of small sensor nodes for sensing the environment
and reporting to the base station via pliable network architecture. The randomly
dispersed sensor nodes of wireless sensor network provide the essential functionalities;
ability of monitoring the physical and environmental conditions in real-time
based on the factors such as temperature, pressure and relative humidity, ability
to consummate devices like switches, actuators and motors that control the environmental
conditions and the ability to afford adept and reliable communication over wireless
networks (Chengfa et al., 2005; Tufail,
Power management, energy management and resource management are most substantial
factors in WSN. Henceforth a methodology called ACR-LEACH is developed for conserving
the above-mentioned factors with a data ensemble (Maraiya
et al., 2011; Katiyar et al., 2011).
The data aggregation process reduces the number of message exchange between
the nodes and the base station and saves some energy. In order to combine the
data from dispersed sensor nodes, clustering is formed. The deployed sensor
nodes are clustered in accordance with the distance between them and the hop
count. For aggregating the data from various sensor nodes, there is a requirement
of aggregation point. While considering the aggregation point, it is apparent
to have heads for each cluster named cluster heads which provides communication
between the cluster members and the base station. In this affirmed work, a schema
is defined for cluster head selection with energy efficiency for an adequate
attainment of WSN (Wang and Ramamurthy, 2007; Singh
et al., 2010).
The sensor nodes of WSN allow arbitrary deployment in inaccessible terrains
which means the self-organized protocol feature and the cooperative effort of
the nodes in wireless sensor network. Herewith, the base station acts as an
interface between the internet and the user. As is well known, the nodes of
wireless sensor network acquire limited energy, lower power, dynamic network
topology and mobility support. It needs multi-hop routing and large scale dispersal,
since it operates with low power and energy. In Fig. 1, the
architecture of wireless sensor network is shown with clustering. With a relevant
hop count, sensor nodes are grouped to form clusters which provide minimum energy
and power consumption. CM represents the cluster members of a particular cluster
whereas CH represents the cluster heads that is responsible for data ensemble
and communication with the base station.
|| Architecture of WSN
Thus, the cluster head of a cluster in WSN has to be elected in an adept manner
with the consideration of energy, concentration and centrality.
One of the popular and efficient protocols which induce the nodes to minimize
the energy consumptions in the networks is LEACH (Low Energy Adaptive Clustering
Hierarchy). This protocol arranges the nodes into groups, so that each cluster
acts a cluster-head for a specific period for its own cluster. LEACH randomly
elects the cluster-head in each round by which the energy will be evenly distributed.
In this approach the base station is fixed and other nodes are energy constrained
Clustering provides an adequate way of perpetuating the network lifetime in
WSN. Basically the clustering algorithm is needed for electing cluster heads
with more surplus energy and whirling the cluster heads periodically. There
should be balance energy and load maintenance among clusters. The network routing
protocol of WSN should involve in providing inter-cluster communication of sensor
nodes to prolong the lifetime of the network. Hence, the appropriate selection
of cluster heads minimizes the energy consumption and increases the network
lifetime in WSN, a fuzzy logic approach had been proposed for considering the
energy, deliberation and centrality (Gupta et al.,
2005). The modification of the shape of each fuzzy set, the network lifetime
can be further increased. This proposal focuses on the efficient cluster head
selection schema which supports the WSN by consuming less energy and involves
inadequate data ensemble to forward that to the base station.
Sensor nodes are compactly deployed over the wireless sensor network that provides
the sensing results with very similar data. The transmission of such data to
the base station may constitute some redundancy (Al-Karaki
and Kamal, 2004). While clustering, the sensor nodes combine and condense
the data together and forward only the compact data to the base station with
efficient data ensemble process (Patwari et al.,
2005). Thus, diminish the localized traffic in individual group. Since wireless
sensor network is an energy constraint network, in requires multi-tier architecture
for data forwarding (Gupta and Younis, 2003). There
followed a hierarchical mechanism for data transmission. When there is a failure
occurred at the highest level of hierarchy, there arises a limitation in accessibility
of nodes under their supervision. Hence, they proposed an efficient mechanism
to recover sensors from the failed part. The study composed two phases namely,
detect and recover the fault forbearance from the failed clusters without re-clustering
the network. The future enhancement of this approach based on the accumulation
of bootstrapping and energy-routing mechanisms.
A proposal illustrated a constant time clustering algorithm that provides minimal
redundancy of exchanges messages and reducing the size of the routing table
(Hesong and Jie, 2005). Ahmed et
al. (2008) explained a Hybrid Energy Efficient Distributed Clustering
(HEED) algorithm using some extended probabilistic determinations. The mechanism
was developed with the assumption that the sensor nodes are deployed in a rectangular
area in the network.
Layered Clustering Hierarchy (LCH) communication protocol was proffered in
which the sensor nodes are formulated as a layered cluster structure. The process
based on the randomized rotation of cluster head selection in each layer for
the distribution of energy load evenly throughout the sensor network. The LCH
constitutes two stages namely initialization stage and distributed clustering
protocol stage (Liu, 2012). The below mentioned stage
was further divided into two phases called cluster formation and data transmission.
As future work, the performance evaluation of LCH could be made with different
LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the prominent clusters-based
structures in wireless sensor network. Conventionally LEACH uses TDMA based
MAC protocol for balanced energy consumption, the proposed work by Yang
and Sikdar (2007) stated that they applied a sleep-wake up dependent decentralized
protocol to LEACH, following that they framed an analytic composition for optimal
cluster head selection. Their proposed methodologies for efficient probabilistic
evaluations for obtaining appropriate cluster heads in small and large sensor
A mechanism by Ahmed (2008) demonstrated that the cluster
head can be elected using decision trees in WSN. In the prescribed methodology,
in order to increase the lifetime of the network (Hamzeh
et al., 2008), the sensor nodes form into distinct clusters with
the gateway (Low et al., 2007), represented here
as high energy nodes called cluster heads (Narwal and Tyagi,
2011). An adaptive method for cluster head selection was described by Nam
et al. (2008) stated, cluster heads of each cluster involve in reducing
the energy consumption and collects the sensing data from the neighborhood sensor
node. The data gets aggregated and transmitted to the sink node (base station).
According to the proposal, the cluster head selection is based on the reorganization
of the clustering and by the consideration the position between the cluster
members and the cluster heads in a particular cluster of WSN.
A study describes the cluster head selection based on the fuzzy logic in cluster
routing (Hu et al., 2009). The cluster head selection
methodology defined here was based on random probability model rather than the
consideration of distribution of sensor nodes and the enduring energy of sensor
nodes. They compared their efficiency with the LEACH algorithm. There was a
description about the cluster head selection on the homogeneous wireless sensor
network by Koucheryavy and Salim (2009). The concept
based on the Voronoi diagram which favors the node deployment in densely distributed
networks as better active sensor nodes, cluster heads and routers. Though cluster
heads involved in minimizing energy consumption, it is vulnerable to attack
and was being as the attractive destiny of attacks (Buttyan
and Holczer, 2009). Commencement of private cluster head selection method
in this paper avoids the leakage of information about the cluster heads during
the cluster head selection process from the attackers.
Overlapping multi-hop clusters are formed for providing efficiency in inter-cluster
routing (Youssef et al., 2009), node localization
and time synchronization protocols in WSN (Youssef et
al., 2009). It provided a solution for overlapping cluster problem with
the parameters such as average overlapping degree and cluster size. The investigation
on approximation factor over tight bounds had been left for future work. The
life span of wireless sensor network has been increased by assuring the homogenous
distribution of sensor nodes in the clusters (Singh et
al., 2010). In this study, the cluster head selection is based on the
residual energy of enduring cluster heads, hold-back value and the hop distance
of the nearest sensor node.
Distributed clustering in WSN with energy efficiency, creates hierarchical
network architecture over the flat network (Dimokas et
al., 2010). The cluster formation technique in this proposal is based
on the construction of dominating set in a node cluster and energy considerations.
It provided a protocol, based on the localized metric for the determination
of the value of the sensor node by rebroadcasting. This proposal afforded an
efficient energy balancing over the dispersed sensor nodes in WSN. Maximization
of network lifespan and denigration of energy dissipation is the major concern
for the design of distributed energy efficient clustering protocol (Chamam
and Pierre, 2010). The protocol was based on the three-way message exchange
every cluster and its one hop neighbor. The protocol design for a multiple transmission
range had been departed for future work.
Zone based cluster head selection algorithm explained by Taruna
et al. (2011) in accordance with the residual energy of the existing
cluster heads, hop distance of the nearest nodes. Theyre demonstrating
the evaluation of various network parameters such as energy consumption, network
lifespan and number of channel heads in each round. The proposal of this study
concentrated on homogeneous WSN and could be further extended with heterogeneous
networks. A comparative study about the cluster head selection algorithms was
made by Ramesh and Somasundaram (2011). Selection of
cluster head on rotation basis greatly impacts the energy efficiency of the
network. The parameters they have analyzed are multi hop data forwarding, the
distance between the forwarding cluster head and the intermediate cluster head
and data gathering.
A novel approach for energy optimization by adaptive clustering had been discussed
by Shamroukh et al. (2012) based on the minimization
of interim among first sensor node death and the last one that efficiently saves
the energy. The use of fuzzy logic in clustering minimizes the difficulties
in mathematical calculations. Another approach discussed based on the energy
efficiency (Sharma et al., 2011) in clusters
for wireless sensor network called Energy Efficient Level Based Clustering Protocol
(EELBCP) method. Diwakar and Kumar (2012) articulated
that efficient utilization of sensor node energy provides prolonged network
lifetime. The approach described that the network was partitioned into annular
rings based on the various power levels at base station and each ring might
have various sensor nodes. As future work, the number of levels maintained in
the network can be optimized for efficient energy consumption and increasing
In WSN, the sensor nodes to make autonomous decisions without energy efficiency
and centralized control. In order to avoid energy dissipation of sensor nodes
(Liu, 2012), ACR-LEACH approach is proposed. While considering
the clustering in wireless sensor network (Buttyan and Holczer,
2009), it is imperative to elect a cluster head to unicast the data of cluster
members. Clustering is an adept methodology for data ensemble in WSN, in which
the cluster head is termed as aggregated node performs data accumulated from
the received cluster member data. In this affirmed work, efficient method derivation
for cluster head election than LEACH with active clustering methods is focused.
As is well known from above explanations, Cluster Heads (CH) is obliged only
for aggregation process over the received data from the cluster members and
transmits that to the base station. When there a case arise that the energy
level of the cluster head becomes lower than the cluster nodes energy, the Associate
Cluster Head (ACH) takes the charge of cluster heads to perform its functionalities.
The major inducement of this proposal is to optimize the energy consumption
of sensor nodes and to maximize the network lifespan. In LEACH methodology,
the cluster head will depart earlier than other nodes in the cluster because
of its overloading operations sending, receiving and overhearing the data. When
this situation occurs, the cluster under the supervision of the above CH becomes
ineffective where the gathered data will never reach the sink, thus, the selection
of Cluster Head is much more extensive and that should be acquired greater energy
than the other nodes of the cluster.
A clustering technique reduces the energy consumption of the nodes by allowing
a particular node to communicate with the base station. Furthermore, the bandwidth
can be reused for the optimal resource allocation and power control. LEACH approach
has recently been used for cluster head election in WSN (Kour
and Sharma, 2010). The protocols like LEACH gather the local information
of the nodes in order to select the cluster-heads. While electing such clustering-heads
by their local information, some drawbacks have occurred. We compare the results
of this approach with LEACH methodology.
Clustering formation procedure: Formation of clusters in sensor network
depends on the time duration for receiving the neighbor nodes message and the
residual energy of the neighbor node. Thus, the clustering protocol is divided
into rounds where each round is triggered to find the optimal cluster heads
for each sensor node in the network. Assume the sensor nodes exchange beacon
messages with its neighbor that composed the list of neighbors and its residual
energy (Alippi et al., 2009). It is also defined
that two nodes do not transmit data in the same time slot in order to reduce
The time duration of the cluster formation procedure is taken as TCFP
which is triggered every network operation time duration and duration of cluster
formation termed as rounds for selecting new cluster heads (Yu
et al., 2011). Since WSN depends on multi-hop hierarchical network
architecture, the hop distance and the hierarchy level play the vital role in
the cluster formation procedure.
The cluster formation procedure comprises four phases:
||Phase 1: Phase 1 operation involves in gathering the
information about the neighbor nodes by broadcasting the beacon messages.
Then, the respective nodes collect reply messages from the neighboring sensor
nodes for the broadcast beacon messages
||Phase 2: In phase 2, a sorting algorithm is executed to obtain
the list of neighbor nodes regarding its hop distance. The list of neighbor
nodes is enacted in descending order
||Phase 3: When its two-hop neighbor node is not enclosed, analyze
all the members of phase 2 one-by-one and crown any one two-hop neighbor
for being as a candidate for the cluster
||Phase 4: Phase 4 operation handles in the execution of sorting
algorithm based on the residual energy of the neighbor nodes
Each round of cluster formation procedure operates in all the four phases for
effective clustering to provide better communication with the sensor nodes and
the data ensemble.
Active cluster head selection: Generally, the cluster-head node is elected
based on the probabilistic methods, so there possibility of contradiction if
two nodes have same probabilistic value. The cluster-head will be selected based
on the nodes which are located adjacent to the edges in the networks, by this
process other nodes spends a large amount of energy to transmit cluster-head
nodes. Another factor centrality in the network also dissipates
the measurable amount of energy during the node transmission. Whenever the node
is placed central to a network, the other nodes find the energy efficiency to
transmit the data. The concentration of the nodes in a given region also affects
in some way for proper cluster-head election (Yu et
al., 2011). It is more reasonable to select a cluster-head in a region,
where the node concentration (Liu, 2012) should be high
to aggregate the data from the cluster members. The main motive of this approach
is to create an active primal approach to group-based selection. This is based
on the three parameters namely energy, centrality and concentration. A fuzzy
logic system makes real time decisions (Shen et al.,
2007), provided with the smaller information. Based on the network configurations
network lifetime can be calculated by selecting the cluster heads with their
information. The node which is selected by the base station is having the more
chances to be a cluster-head with three parameters, they are the energy consumption
in every nodes and node centrality among every node and minimal energy consumption
which increases the lifetime.
In cluster head selection, every node in the network with high residual energy
becomes the cluster head (Ta et al., 2007) with
same probability P which is the predefined threshold value. The rest of the
nodes in the network will be under sleeping state (Chengfa
et al., 2005), till the cluster head selection process terminates.
Suppose Hi be the tentative cluster head that has the contention
range Rcon function. There is another condition that, if Hi
is the cluster head, then there should not be any other cluster head Hj
within the range of Rcon.
|| Pseudocode for cluster head selection
The cluster head selection process is ultimately based on the energy level
of each node in the cluster which is performed on the basis of rounds. Figure
2 demonstrates the pseudo code for the operation performed with cluster
head selection. Each sensor node will be analyzed for its energy level (μ)
for all the randomly distributed nodes present in the network, ranges from 0-1.
Each Hi broadcasts an intact-head-MSG with contention radius and
the residual energy. Each tentative cluster head upholds a set SCH of
its neighborhood tentative cluster heads which is being constructed by the steps
10-13 of the pseudo code. The steps 14-26 involves in the determination of a
cluster head with high residual energy (REnergy) for the particular
contention range. The cluster head is selected in accordance with the distance
between the contention nodes and the base station. Once the cluster head is
selected, the corresponding node broadcasts the final-head-msg to all the contention
nodes. Then, the sleeping nodes wake up and each cluster head sends CH-ADV-MSG
to all distributed nodes in the network. Each prevailing node joins with the
closest cluster head by the largest received signal strength and acquaints the
cluster head by join-cluster-MSG. The approach has come from the MITs
μ-AMPS process which develops an energy optimized solution for wireless
sensor network architecture and protocol when the location of the base station
is far away from the sensor nodes. It also involves in effective local data
correlation for all energy constrained sensor nodes. In this approach the communication
energy for the sensor nodes and the base station is cost higher and so it is
complex for the sensor nodes to gather their data and resend to the base stations.
If the selection of the cluster-heads is non dynamic then the selected nodes
will lose its power and eliminate quickly. Priorities are given to data aggregation
and data fusion technique rather than the electing the cluster-heads. The main
approach of this algorithm is to reduce the unwanted noises and exhibits the
optimal signals. A cluster-heads need regular energy distribution over the networks.
Data ensemble in cluster based wireless sensor network: Data Ensemble
is defined as the methodical accumulation of sensed data from multiple sensor
nodes to be transmitted eventually to the base station for refinement. Since
sensor nodes in WSN are energy constrained, it is inept for all the sensors
to handle on the data directly to the base station. Data generated from neighboring
sensor nodes are often superfluous and highly correlated. In addition, the amount
of data produced in large sensor networks is usually massive for the base station
to process. Hence, there is a need for techniques aggregate the data into high-quality
information on the sensors or transiting nodes which can condense the number
of packets channeled to the base station and ensuing in conservation of energy
and bandwidth. This can be consummated by data ensemble which tends to the elimination
of redundant data transmission. There are so many methods defined for data ensemble
based on network topology, network flow and the quality of services. In cluster
based WSN, the data from various sensor nodes are aggregated by the local aggregator
called cluster heads and transmitted to the base station. There are some distinct
protocols for data aggregation in cloud based wireless sensor networks such
as LEACH, E-LEACH (Energy LEACH), TL-LEACH (Two Level-LEACH), M-LEACH (Multi
hop-LEACH), LEACH-C (LEACH with centralized Algorithm), V-LEACH (Vice-LEACH)
and chain based data aggregation.
In this ACR-LEACH model, the chain based data aggregation in hierarchical network
setup, bonding with LEACH, in order to provide efficient results is followed.
Sensor nodes of WSN transmit data to the cluster head where data ensemble has
been performed. However, if the cluster head is far away from the sensors, they
may disburse indulgent energy in dissemination. Further improvements in energy
efficiency can be obtained if sensors transmit only to close neighbors. In PEGASIS
(Power Efficient Data gathering protocol for Sensor Information Systems), nodes
are linearly ordered to form chain to perform efficient data gathering. The
farthest sensor node from the base station commences chain formation and at
each step whereas the contiguous neighbor of a node is selected as its successor
in the chain structure. In each data congregation round, a node acquires data
from one of its neighbors, combines the data with its own and transmits the
combined data to its other neighbor node along the chain.
Base station control algorithm: The base station is the one which has
the overall knowledge of the network; we use the primal authority algorithm
in the base station which elects the optimal cluster-heads of cluster members.
Obviously Base stations will be having more memory, energy and storage than
that of the sensor nodes. Energy is consumed in order to bring the local knowledge
about the nodes to the base. Wireless Sensor Networks are distributed over the
geographical area for sensing the gathering knowledge. Let us have an assumption
that nodes having minimum mobility and sends the local information on the initial
phase is sufficient. The cluster-head is the node that is responsible for high
energy transmission to the base station, if the cluster-head is elected as a
static one then all the powers would be exhausted rapidly. In our approach,
the cluster-head election is done with rotation based algorithm with their probabilistic
values. Hence, all the nodes are getting chances based on their probabilistic
values and then the cluster-heads are elected. The centrality has been calculated
by summing the square of the distance of other nodes from the given node for
electing suitable cluster-head. The main motto of the approach is how the different
argument nodes affect the election criterion to conserve energy.
Then we use the primal control algorithm inside the base. The base has enough
storage, power and memory which outperforms the resource constrained problem
in the local node clustering. Base station elects the cluster-heads based on
three parameters namely, energy, concentration and centrality. Our approach
based on dynamic centralized domain that overcomes the complexity of the mathematical
model. With the help of the three parameters and joining the rules dynamic centralized
approach produces the optimal results. The energy alone makes the cluster-head
node to form the cluster centrality which gives the minimal energy consumptions.
By the experimental results, the centrality level and the efficient energy consumption
of our ACR-LEACH method is obtained.
The affirmed work is compared with the results produced by LEACH methodology
to assess the efficiency of ACR-LEACH methodology using NS2 simulations. In
this simulation, research model performed on 100 nodes which are arbitrarily
deployed and dispersed in a 100×100 m2 area. Sensor nodes restrain
two kinds namely, sink nodes with no energy constraints and the common sensor
nodes with limited energy.
The simulation results are analyzed and compared to LEACH with the parameters
such as throughput, variance of energy, energy consumption, average energy utilization,
End to end delay and Packet Delivery Ratio (PDR).
Table 1 shows the simulation parameters that have taken for
producing simulation results for ACR-LEACH approach. Here, the sensor nodes
are randomly distributed with initial node energy as 2 J. The location of the
Base Station (BS) is assumed as (110, 45) and when the energy of the node becomes
less than or equal to 0, the node is considered as the dead node. With these
parameters simulation results are produced.
Table 2 reveals the comparison between the results of LEACH
and ACR-LEACH methodologies with quality analysis metrics. It is obvious from
the results that the proposed method is more efficient than the existing. Figure
3 exemplifies the correlation between simulation time (milliseconds) and
throughput (Mbps). From the pictorial representation, it is apparent that our
proposed ACR-LEACH model produces higher throughput than the LEACH clustering
|| Simulation parameters
|| Comparison-LEACH and ACR-LEACH
Our adduced method provides improved rate of effective message delivery through
the communication channel than the comparison mechanism.
In order to provide evidence of this proposed methodology that it is designed
by the consideration of energy efficient mechanisms, the simulation result in
Fig. 4 demonstrates the relationship between simulation time
and variance of energy (n J) for the existing LEACH and ACR-LEACH methods. The
graphical representation of Fig. 5 illustrates that our affirmed
methodology consumes less energy than LEACH approach since the eminent conceits
of clustering models and cluster head selection is used. The main concern of
this proposal is to afford a method for optimal energy consumption in wireless
sensor network using energy efficient cluster selection mechanism. The above
mentioned result shows that the average energy utilization is considerably reduced
in this approach by the trasmission of data done with cluster heads.
|| Simulation time (m sec) vs throughput (Mbps)
|| Simulation time (m sec) vs variance of energy (nJ)
||Simulation time (m sec) vs avg energy utilization (nJ)
|| Simulation time (m sec) vs end to end delay (m sec)
|| Simulation time (m sec) vs PDR (%)
In accession to the optimal energy consumption, this approach provides minimized
end to end delay. Figure 6 demonstrates the alliance between
the simulation time and end to end delay which provides an evidence for the
consistency of this proposed work.
The average time delay to transmit the packets to sink node or to the BS is
termed as end-end delay which is significantly reduced in the adduced work than
the existing LEACH.
PDR in WSN is described as the ratio between the numbers of receiving data
packets to the number of generating data packets by sensor nodes. Figure
7 presented below evinces the relation between the simulation time and the
packet delivery ratio that determines the accuracy and reliability of the communication
between the sensor nodes in wireless sensor network. The simulation result provided
beneath exhibits that PDR% of ACR-LEACH is greater than the LEACH results. Thus,
this method produces results with higher precision rate. The Active clustering
rule for LEACH contributes an innovative method for energy efficient cluster
head selection with concentration and the centrality and data aggregation.
In this study, we developed a distinctive approach called Active Clustering
Rule for LEACH. It is extensively focused on energy efficient cluster head selection
by the deliberation of the parameters such as concentration and centrality.
With this proposal, the work towards framing clusters and outperforms the results
of LEACH model. It also educe data ensemble in cluster heads for optimal energy
consumption and to increase the network longevity. The base station control
algorithm of this proposal reduces the complexity over mathematical calculations
made for random cluster head selection of the densely distributed wireless sensor
network. This study can be enhanced by designing a cluster model with both energy
and power saving mechanisms. As for future work, it is intended to extend this
proposal with cyclic vague-off and vague-on of the operations accomplished by
sensor nodes that could save the power and energy in an adept manner.
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