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Abstract

In this paper, we propose Regional Energy Efficient Cluster Heads based on Maximum Energy (REECH-ME) Routing Protocol for Wireless Sensor Networks (WSNs). The main purpose of this protocol is to improve the network lifetime and particularly the stability period of the network. In REECH-ME, the node with the maximum energy in a region becomes Cluster Head (CH) of that region for that particular round and the number of the cluster heads in each round remains the same. Our technique outperforms LEACH [1] which uses probabilistic approach for the selection of CHs. We also implement the Uniform Random Distribution Model to find the packet drop to make this protocol more practical. We also calculate the confidence interval of all our results which helps us to visualize the possible deviation of our graphs from the mean value.
J. Basic. Appl. Sci. Res.
, 4(1)200-216, 2014
© 2014, TextRoad Publication
ISSN 2090-4304
Journal of Basic and Applied
Scientific Research
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*Corresponding Author: Nadeem Javaid, COMSATS Institute of Information Technology, Islamabad, Pakistan,
www.njavaid.com.
REECH-ME: Regional Energy Efficient Cluster Heads based on Maximum
Energy Routing Protocol with Sink Mobility in WSNs
A. Haider1, M. M. Sandhu1, N. Amjad1, S. H. Ahmed2, M. J. Ashraf1, A. Ahmed1, Z. A. Khan3,
U. Qasim4, N. Javaid1,*
1COMSATS Institute of Information Technology, Islamabad, Pakistan
2SCSE, Kyungpook National University, Daegu, Korea
3Internetworking Program, FE, Dalhousie University, Halifax, Canada
4University of Alberta, Alberta, Canada Received: November 25 2013
Accepted: December 20 2013
ABSTRACT
In this paper, we propose Regional Energy Efficient Cluster Heads based on Maximum Energy (REECH-ME)
Routing Protocol for Wireless Sensor Networks (WSNs). The main purpose of this protocol is to improve the
network lifetime and particularly the stability period of the network. In REECH-ME, the node with the maximum
energy in a region becomes Cluster Head (CH) of that region for that particular round and the number of the cluster
heads in each round remains the same. Our technique outperforms LEACH [1] which uses probabilistic approach for
the selection of CHs. We also implement the Uniform Random Distribution Model to find the packet drop to make
this protocol more practical. We also calculate the confidence interval of all our results which helps us to visualize
the possible deviation of our graphs from the mean value.
1. INTRODUCTION
WSNs are used in environmental monitoring, security, medical applications, etc. The sensor nodes are
usually randomly deployed in a specific region. These sensor nodes collect their data and send it to the Base Station
(BS) via some routing protocol. These nodes cannot be recharged from time to time to keep them alive. They must
follow a protocol which must ensure the efficient use of their power, so that those nodes may serve as long as
possible without any external assistance. A routing technique plays a key role in their energy consumption. Many of
the routing protocols use clustering as their routing technique. So clustering plays a very important role in
prolonging the stability period and network life time. The Cluster Heads (CHs) collect the data from all the nodes in
their cluster, aggregate it and then finally send it to the BS. These sensor nodes must follow a certain routing
protocol to send their data efficiently to the BS. The main objective of all routing protocols is to minimize the
energy consumption so that the network lifetime and particularly the stability period of the network may be
enhanced. By network lifetime we mean the time duration from the start of the network till the death of the last
node, whereas, stability period means the time duration from the start of the network till the death of the first node.
A network can be reactive or proactive. In proactive network, nodes send their data to the BS or CH only
when they detect a change and keep the transmitter off when they do not detect any change in the environment. Our
proposed protocol is proactive. This approach is more energy efficient as compared to the reactive protocols. As in
reactive protocols, nodes keep sending the data to the BS all the time. So, they quickly consume their energy as
compared to the proactive protocols. In proposed protocol, the BS is at the centre of the field, i.e., if the area of the
network is 100mx100m, the BS would be at a position (50m,50m) .
By the term homogenous, we mean that initially all nodes in the network have the same amount of energy.
Similar to LEACH [1], REECH-ME is also based on the homogenous set of nodes. It all depends on the routing
technique that how efficiently it consumes this energy to increase the life time and particularly the stability period of
the network.
Clustering may be static or dynamic. In Static Clustering the clusters are not changed throughout the
network life time. Whereas, in Dynamic Routing, the clusters change depending on the network characteristics.
LEACH uses Dynamic Clustering and its CHs are chosen on probabilistic basis. So the number of its CHs and the
size of the clusters may change after every round. That is why its number of CHs is not optimum. So the number of
packets sent to the BS is also not fixed as they depend upon the number of the CHs.
In the proposed scheme, the total area is divided into 9 regions. These are named as R1, R2, R3, ... , R9
as shown in fig. 2. The region R1 is closest to the BS and uses Direct Communication as its routing technique. In
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Direct Communication, every node sends its data directly to the BS. All other regions, i.e. R2 - R9, do not use Direct
Communication. Instead, they form CHs to send their data to the BS. REECH-ME uses Static Clustering, so clusters
throughout the network lifetime remain the same. Each region except R1 is called a cluster and each cluster has only
one CH for a particular round. Other nodes of regions R2-R9 send their data to the BS via CH of their region. In our
protocol, the CH is chosen on the basis of maximum energy. It means that in any round the node having the
maximum energy becomes the CH. So the energy utilization becomes very efficient as well as the number of the
CHs in a round becomes fixed. As there are 8 regions which form clusters, so there would be 8 CHs in each round
which is the optimum number.
As in any real case scenario, the number of packets received at the BS is never equal to the number of
packets sent to the BS. This is because some packets are lost due to certain factors. Those factors may include
interference, attenuation, noise, etc. That is why we use the Uniform Random Distribution Model [5] for the
calculation of packets drop. This makes REECH-ME more practical. We also calculate the Confidence Interval of
all our results. It helps us to visualize the deviation of the graphs from the mean value. Where, the mean value is
calculated by taking the results of 5 simulations, and then taking their mean.
2. RELATED WORK
Efficient energy consumption in WSNs is a very active research topic from last few years. Many
researchers have proposed different protocols to improve the network lifetime and stability region in a WSN.
Routing is the backbone of the protocol because the consumption of energy depends upon routing [36].
DR-LEACH [4] was proposed by K. Latif et al.. In this technique, the network area is divided into different
regions. Nodes of the region which is closest to the BS use direct communication of data. All other regions use
clustering for routing of data, in which a CH is responsible for the data aggregation and transmission. During every
round, fixed number of CHs is selected. DR-LEACH also uses multi-hop communication between clusters. In this way,
energy utilization efficiency is also increased and hence, lifetime and stability region of the network is enhanced.
[10] used LP modeling for maximizing throughput in proactive protocols in wireless multi-hop networks.
so that the maximum data will be gathered at the base station. This LP model also resulted in minimizing routing
delay. This delay minimizing problem is very important to be solved because in time critical applications, this is a
quite significant and is needed to be solved as is [25].
Aslam et al. [21] proposed CEEC which uses three energy levels of nodes i.e. normal, advance and super
nodes. In this technique, The network area is divided in three equal regions and every region is homogeneous, i.e.,
contains nodes with same energy. Base Station (BS) centrally selects optimum number of CHs. The regions are
arranged in ascending order from the BS in terms of their initial energy. So, region of normal nodes is the closest
and the region with super-nodes is the farthest. CEEC is 100% better in stability as compared to LEACH [1].
In [22] authors used Error Control Coding (ECC) for efficient energy consumption. Z.Abbas et al. presents
the efficient encoder selection that transmits power with respect to its critical distance which results in energy saving
in WSNs. Encoder selection is performed by using critical distance which is estimated from coding gain of that
encoder. ECC in this context, becomes energy efficient as encoders and their transmit powers are selected
adaptively, that results in energy saving to these particular encoders.
In [20] N. Javaid et al. proposed EDDEEC. It is a three level heterogeneous protocol which assigns
different probabilities to each energy level node to become CH, so, that nodes with high energy become CHs more
frequently as compared to the nodes with less energy. In EDDEEC, authors defined a residual energy level
threshold. Under that threshold, all normal, advance and super nodes have same probability for CH selection.
EDDEEC [20] is adaptive energy aware protocol which dynamically changes the probability of nodes for the
selection of CHs. This is a balanced and efficient way to distribute equal amount of energy between sensor nodes.
Setting a threshold can be very useful in efficient consumption of energy as in [27].
HEER [18] was proposed for both homogeneous and heterogeneous environments. This protocol takes into
account the initial and residual energies of the nodes for the selection of CHs. Data transmission in HEER depends
on two threshold values, i.e., Hard Threshold (HT) and Soft Threshold (ST). In this technique, the nodes sense their
environment repeatedly and if a parameter from the attributes set reaches its HT value, the node switches on its
transmitter and transmits data. The Current Value (CV), on which first transmission occurs, is stored in an internal
variable in the node called Sensed Value (SV). Now the nodes will again transmit the data to their respective CH if:
 (1)
If the CV differs from SV by an amount equal to or greater than ST, only then the nodes transmit their data.
It results in a reduced number of transmissions and hence, network lifetime is improved.
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B. Manzoor et al. [19] proposed Q-LEACH for the efficient energy consumption of nodes. According to
this approach sensor nodes are deployed in a territory. In order to acquire better clustering, the network area is
divided into four regions. Through this division, optimum positions of CHs are defined. Moreover, transmission
load of other transmitting nodes is also reduced. By doing such sort of partitioning, better coverage of the whole
network is achieved.
In [22] authors improved network lifetime and stability region by adapting both open-loop and closed-
loop feedback processes. By using this approach, they divided the whole network area into three logical regions on
the basis of threshold for each region. In this way, the authors were also able to achieve minimized packets
overhead. This protocol proved very efficient in energy consumption of nodes.
H-DEEC and MH-DEEC [32] routing protocol are proposed as energy aware adaptive clustering protocols
for heterogeneous WSNs. In H-DEEC, the network is divided into two parts on the bases of initial and residual
energy. Normal nodes elect themselves as CHs and Beta nodes collect data from CHs and send it to BS using multi-
hopping. Unlike SEP and DEEC, H-DEEC and MH-DEEC perform better in a heterogeneous wireless sensor field.
Moreover, it also considers the problem of locating BS outside the network.
In [29] authors introduce adopted authentication approach for protecting our Ad-hoc wireless network by
even- odd function. In this function, mobile node compute and generates random even or odd number during
signaling process. If first node generates random odd number then next node computes and generates a random even
number. There are number of attacks existing in wireless communication in different application of communication
field. This scheme will secure the whole wireless network from the outsider attack.
Based on the analysis of energy management, the main factors of energy consumption are: sensing the
data, data processing and radio communications where the radio communication is the major part of energy
consumption. In the WSNs, the realization of energy-efficiency could be improved using different energy efficiency
techniques [31].
When the sink is static, the probability of coverage holes is greater [34]. After some rounds, there is a
possibility that the energy of some part of the network becomes low and that results in a coverage hole. Coverage
holes are the greatest enemies of a WSN because we cannot monitor the whole network area because some nodes are
not functioning due to depletion of their energy. This energy hole problem was also solved by [26].
3. INTRODUCTION TO BASIC ROUTING PROTOCOLS
In this section, we discuss some of the basic protocols [1, 6, 7, 11] in the field of WSNs:
3.1. LOW ENERGY ADAPTIVE CLUSTERING HIERARCHY (LEACH) [1]:
It is one of the earliest clustering routing protocols for WSNs to increase the lifespan of network. LEACH
is a self-organizing protocol that distributes energy load equally among all the sensors of the network. In LEACH,
nodes form clusters and a CH is elected from each cluster. LEACH chooses high energy sensor node CH and rotates
this role among all nodes of the network.. LEACH also performs data fusion to compress the amount of data being
sent from cluster to base station. Thus LEACH reduces energy dissipation and increases network lifetime. LEACH
has different variants such as [35], [31] and [19].
For each round, sensors elect themselves as CH with certain probability. The status of these CHs is
broadcasted within the network. Each sensor node selects its CH by choosing the one which requires minimum
communication energy to send data to. After the formation of a cluster, CH creates a schedule for the nodes to
transmit data. In this way, nodes transmit data to the CH in their allocated time and are in sleep condition for the rest
of the time. So, the energy dissipation of individual sensor node is minimized in this manner. When the cluster-head
receives all the data from nodes within a cluster, it aggregates that data and sends compressed data to the base
station. In this way, energy dissipation of the whole network is reduced. Similarly, being a CH, the enrgy of that
node drains fast. LEACH has no fixed number of CH and a CH is self-elected in every round. For a node to become
CH, depends on energy of that node. So, node with higher remaining energy acts as CH for that round.
3.1.1 OPERATION OF LEACH:
The operation of LEACH is broken into rounds. Each round consists of two phases, a set-up phase and a
steady-state phase. In set-up phase, the clusters are organized and in steady-state phase data is transmitted to the
base station. Generally steady-state phase is longer than set-up phase to minimize overhead.
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3.1.2. ADVERTISEMENT PHASE:
At the beginning, when clusters are formed, each node decides whether it should become a CH for the
current round or not. This decision is taken by determining the suggested percentage of CH and number of times a
node has been a CH. A node n makes a decision by taking a number between 0 and 1 randomly. If the number is less
than a certain threshold T(n), the node becomes CH for the current round. The threshold is determined as:
() = 
(
)
0ℎ (2)
Where G is set of nodes that have not been selected as CHs in previous 1/P rounds, P is suggested
percentage of CH, r is current round. By using this threshold, each node has the chance of becoming a CH at some
stage within 1/P rounds. During initial round zero (r=0), each node has the probability P of becoming a CH.
Similarly, if a node becomes CH in round zero, it cannot become a CH for the next 1/P rounds. The node that has
elected itself as CH for the current round, broadcasts an advertisement message to all nodes within the network. The
non-CH nodes have to keep their receivers on. This advertisement is received by non-CH nodes. After receiving this
message, each sensor node decides to join a certain cluster for the current round. This decision is taken according to
the strength of received signal. So, the non-CH will join a CH whose received signal strength is larger. In this way,
the energy required for communication between non-CH nodes and CHs nodes is less. In certain cases where
received signal strength is same for more than one CH, a random CH is selected.
3.1.3. CLUSTER SET-UP PHASE:
When a node decides to join a cluster, it must inform the cluster-head that it wants to be a member of that
cluster. During this phase, the CHs have to keep their receivers on.
3.1.4. SCHEDULE CREATION:
After receiving message from all nodes that would like to join that cluster, the CH creates a TDMA
schedule based on number of nodes and informs the nodes when to transmit data.
3.1.5. DATA TRANSMISSION:
Data transmission begins after formation of clusters and allocation of TDMA schedule. Each node sends
data to CH during allocated transmission time. The nodes are in sleep condition for the rest of the time to reduce
energy dissipation. The receiver of the CH must be switched on to receive from all nodes. After receiving data from
all nodes, the CH compresses it to a single signal and transmits it to the base station.
3.1.6. ADVANTAGES OF LEACH:
LEACH is completely distributed, requiring no control information from the base station and the
nodes do not require knowledge of the global network in order for the LEACH to operate.
Node serves as CH once in a round to distribute the load equally.
TDMA prevents CHs from unnecessary collisions. 4. Excessive energy dissipation is prevented by
communicating only in the allocated time.
3.1.7. DISADVANTAGES OF LEACH:
It performs single hop communication which is not applicable to large networks because of
excessive energy dissipation.
Leach does not ensure real load balancing for nodes having different initial energy because CH is
selected by probability and not seeing its initial energy.
The idea of dynamic clustering brings extra overhead.
3.2. TEEN [11] (THRESHOLD SENSITIVE ENERGY EFFICIENT SENSOR NETWORK PROTOCOL):
Routing protocols for wireless sensor networks can be classified into two classes, proactive and reactive
protocols. LEACH protocol is considered as proactive protocol since it sends reports to the base station periodically.
In reactive protocols, when an event of interest occurs, it is reported to the base station. Reactive protocols are
generally used for time critical applications where quick response to changes in the sensed parameters is required.
Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN) is a reactive protocol designed for time
critical applications. In TEEN, nodes are arranged in hierarchical clustering scheme in which certain nodes act as
cluster head (first or second level). After cluster head is elected, the user sets attributes for it. When the cluster head
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receives these attributes, it broadcasts the attributes (Hard Threshold (HT) and Soft Threshold (ST) values) to all
member nodes of the cluster. The Sensor nodes sense the data and transmit only when the sensed data exceeds HT.
HT is the minimum value above which values are noted. Sensed value (SV) is an internal variable which stores the
transmitted sensed value. The sensor again senses data and when its value exceeds the ST, which is the minimum
change in sensed value, it starts transmitting data. In this way, TEEN conserves energy since sensor nodes senses
data continuously but transmits only when data is above HT. ST further reduces transmission, which could have
otherwise occurred due to little or no change to level of sensed attributes.
Since cluster-head performs extra computations, its energy consumption is more than other nodes. This
problem is resolved by giving equal chance to every node to act as cluster-head for a fixed cluster period. We can
reset the attributes during every cluster change time. No transmission from nodes to cluster-head occurs if the sensed
value is below HT, so the cluster-head will not be aware of death of a sensor node. By giving smaller value to ST on
cost of high energy due to frequent transmission, a clear scenario of the network can be obtained. Similar to
LEACH, every node in the cluster is given a time slot for data transmission using TDMA schedule. Soft threshold is
used to on or off the sensing node while hard threshold is activated while sensing value is being changed. Here two
level of CH are being used. Depending on the
3.2.1. ADVANTAGES OF TEEN:
On the basis of two thresholds, data transmission can be easily controlled i.e. only the required data be
transmitted. In this way it reduces the energy of transmission. Since TEEN is complement for reacting to large
changes in the sensed attributes, it is suitable for reactive scenes and time critical applications.
3.2.2. DISADVANTAGES OF TEEN:
It is not suitable for periodic reports applications because if the values of the attributes are below threshold,
the user may not get any data at all. There exist wasted time-slots and a possibility that the BS may not be able to
distinguish dead nodes from alive ones, because only when the data arrive at the hard threshold and has a variant
higher than the soft threshold did the sensors report the data to the BS. If CHs are not in the communication range of
each other the data may be lost, because information propagation is accomplished only by cluster-heads.
3.3. SEP [7] (STABLE ELECTION PROTOCOL):
LEACH uses randomized rotation of cluster-head for balancing energy load among all sensors. This
approach is generally applied to homogenous environment. But in real life, sensor node is not able to keep energy
uniformity. Therefore the concept of heterogeneity is introduced. Stable Election Protocol (SEP) heterogeneous
aware routing protocol which is used to extend the time period before the first node in the network dies. In SEP,
each node has weighted probability to become cluster head which depends upon the remaining energy in each node
relative to average energy of the network.
3.3.1. HETEROGENEOUS NETWORK:
In such network, nodes have different amount of initial energy. ‘m’ describes a fraction of total nodes ‘n’,
which have ‘a’ times more energy other nodes. These nodes are called advance nodes and the remaining nodes
having less energy are called normal nodes.
3.3.2. OPTIMAL CLUSTERING:
In case of heterogeneous nodes, LEACH creates a large unstable region. This is because all remaining
advance nodes have nearly same amount of energy, so the process to elect cluster head becomes unstable and no
cluster head is elected and advance nodes become idle. SEP improves the stable region using some fraction of
advance nodes (m) and some additional energy factor (a) to differentiate normal nodes from advance nodes. In SEP,
the advance nodes have more chances to become cluster head than normal nodes. In heterogeneous network with
advance and normal nodes, a priori setting of is not affected but the system’s total energy varies. If is initial
energy of normal node then (1 + ) becomes initial energy of advance nodes. So, initial energy of heterogeneous
system becomes:
(1)+(1+ ) = (1+ .) (3)
So the system’s energy is increased by an amount 1+a.m, if:
• Normal node has the probability to become cluster head once in
(∗.) rounds.
• Advance node has the probability to become cluster head 1+a.m times in
(.)times.
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 is average number of cluster head per round.
3.3.3. HOW TO MAINTAIN WELL DISTRIBUTED ENERGY CONSUMPTION DURING THE STABLE
REGION:
Same threshold cannot be set for both normal and advance nodes. If it were so and normal nodes have
probability of
(.)per round and advance nodes have probability of 1+a.m times in every .
 rounds, there
will be no surety for the number of cluster head to become per round. The reason is that, this number
cannott be maintained per round with probability 1.
3.3.4. SOLUTION FOR WELL DISTRIBUTED ENERGY CONSUMPTION DURING THE STABLE
REGION:
SEP depends upon initial energy of the nodes. In SEP, each node has knowledge of the whole network’s
energy and decides whether to become a cluster head depending on its own remaining energy. A weight is assigned
to , which is equal to initial energy of each node divided by initial energy of normal node.  is weighted
probability for normal node and for advance nodes. For advance and normal nodes, the weighted probabilities
are given as:  =
(.)For advance nodes, probability will be:  =
(.)(). Threshold for normal
nodes () and threshold for advance nodes () are given as:
() = 
∗
 ′ (4)
Where r is current round, ′ is set of nodes that were not cluster head in previous
 rounds. () is
threshold of n(1-m) normal nodes. Now for advance nodes:
() = 
∗
 ′′ (5)
Where ′′ is set of nodes that were not cluster head in previous 1/ rounds. So the total number of
cluster head per round in heterogeneous network is equal to: .(1)+. =.. As
LEACH is sensitive to heterogeneity, it goes to unstable region quickly. SEP is aware of heterogeneity, so it extends
the stable region by assigning weighted probabilities to the nodes for cluster head election. SEP performance does
not depend individually on values of m and ‘a’ but on their product. The advance nodes have ‘a.m’ times extra
initial energy.
In SEP every sensor node elects itself independently as CH, depending on its initial energy and that of all
other nodes. The global knowledge of energy is not required at every round. Here two types of nodes can be seen,
normal nodes and advanced nodes. All nodes having same initial energy are normal nodes, whereas some of the
nodes are assigned some extra energy so that these nodes can become CHs again and again depending on the
residual energy. Advanced nodes have more probability to become CH.
3.4. DISTRIBUTED ENERGY EFFICIENT CLUSTER FORMATION PROTOCOL (DEEC) [6]:
DEEC [6] is designed for Multi-level heterogenous environment. The criteria for selecting cluster head in
DEEC depends upon a probability which is based on ratio between residual energy of every node and average
energy of the whole network. So nodes with high initial and residual energy have more chances to become CH than
nodes with low energy. Thus DEEC can prolong the stability period by heterogeneous aware clustering algorithm.
The total initial energy of two level heterogeneous network is given as:
 =(1)+(1 + ) = (1+ ) (6)
Where is initial energy of the network, m is fraction of advance node which has a times more energy
than normal node, mN is advance node having initial energy (1+ ) and (1-m)N is normal node having initial
energy .
For multi-level heterogeneous network, the total initial energy is given as:
 == 1[(1+ ) = (+
 )] (7)
3.4.1. DEEC PROTOCOL:
DEEC calculates an ideal value for the network lifetime. This value is used to calculate a reference energy
that each node expends during a round. Thus each node needs not to have the global knowledge of the network.
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3.4.2. CLUSTER HEAD SELECTION CRITERIA:
represents the number of rounds to be a cluster head for node . In homogeneous network such as
Leach, each node becomes a cluster head only for a single time for =
 rounds. It is obvious that nodes cannot
have the same residual energy for the whole network. The average probability for cluster head during rounds can
be given as =
. For nodes having equal energy during each round, average probability can be replaced by
. By doing this, we can be sure that there are  cluster heads for each round and that all nodes are
completely energy depleted at the same time. For heterogeneous environment i.e. nodes having different energy, the
nodes with higher energy have more average probability than . The average energy E(r) of the network at
round r can be calculated as:
() = (
)
 () (8)
By taking average energy E(r), average probability can be calculated as:
=[1 ()()
()] = ∗()
() (9)
So the total cluster heads are ensured to be per round. The threshold value for each node to
become a cluster head during each round is given as:
() =

 (10)
Where is set of nodes to become CHs during round r. belongs to G only if it was not a cluster head
during recent rounds. Node becomes CH by choosing a random number between 0 and 1. The node will become
cluster head if the random number is less than threshold (). The number of rounds is the inverse of average
probability and is calculated as:
=
=()
()=()
(()) (11)
3.4.3. IN CASE OF HETEROGENEOUS NODES:
From equations of average probability ,was taken as reference value for . The initial energy of the
nodes are same for homogeneous environment, so nodes generally use as reference of . But the initial energy
of nodes is different in case of heterogeneous environment, the reference value for each node will be different. For
the case of two level heterogeneous environment, we have normal and advance nodes only, so the reference value
is replaced by weighted probabilities as:  =
(), =()
()And the average probability is
given as:
=∗()
()()
()()
()() (12)
Substituting value of directly to equation of threshold, we find a direct relation of threshold with initial
and residual energy of each node. Now for heterogeneous network, the equation is written as:
() = ()
(

 (13)
And the equation of average probability can be written for heterogeneous nodes as:
=()()
((

)()) (14)
So from the above equations, the reference epoch is given as:
=


(()) (15)
This reference value is different for every node having different initial energy. As =
, so depending on
residual energy () , the for each node varies around its reference value . When (>()) we have
<. So from this it is clear that nodes with higher energy have more chances to become cluster heads than nodes
with low energy. In this way, we get a well distributed energy network.
4. MOTIVATION
The main objective of a routing protocol is to efficiently utilize the energy of the nodes. This is because
these nodes are not rechargeable and in order to make them useful for a longer period of time, routing protocols have
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been proposed. Routing protocols improve the lifetime of a network and specifically the stability period of a
network. Protocols [2] , [4] , [8], [10], [13], [21], [24], [17], [18], [19] and [20] are proposed to achieve these goals.
As shown in Figure 1, LEACH uses dynamic clustering. Hence, its clusters change after every round.
Figure 1: Clustering in LEACH Protocol
As the CH selection in LEACH is on the basis of probability, the optimum number of CHs is not achieved.
So the energy is not efficiently utilized. The area coverage in LEACH is also not very efficient. This is because it
treats the whole area as a single area and the nodes are deployed in it at once. So some of the area is left unattended.
To efficiently utilize the energy and to improve the coverage area, many researchers have introduced some effective
approaches [2] , [3] , [4] and [10]. In these approaches, the total area is divided into small regions and these regions
are treated separately for the nodes distribution and it improves the area coverage. In our protocol, we also use the
approach of dividing the total area into smaller areas. We use the direct transmission for the area (R1) closest to the
nodes as shown in fig. 3. We use the static clustering in all other regions. The CH selection is based on the
maximum energy of a particular node in a round. It means that the node with the highest energy is chosen as the CH
for that particular round. So the energy is very efficiently utilized and the area coverage is also improved.
5. RADIO MODEL
REECH-ME assumes a simple first order radio model in which the radio dissipates  = 50 nJ/bit for
powering the transmitter or receiver circuitry and  = 100 pJ/bit/ for the transmitter amplifier to achieve an
acceptable Eb/No. Transmitter circuitry also consumes  = 50 nJ/bit to aggregate the data received by the normal
nodes. We also take in account the energy loss due to channel transmission. Thus, to transmit a k-bit message
distance d the energy is given as:
=
 (17)
if <
(,) = + (18)
if
(,) = + (19)
Reception Energy:
() =  (20)
Where  is the energy dissipated per bit to run the transmitter or receiver circuit,  and  depend on
the transmitter amplifier. A basic radio model is shown in fig. 2.
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Figure 2: Radio Model
It can be seen that same energy is required to switch on the transmitter and receiver circuitry. This is
because both use × energy. So, it can be concluded that this radio model is symmetric such that the energy
required to transmit a bit of data from node A to B is the same as the energy required to transmit a bit of data from
node B to node A for a given SNR. We assume that all sensors are always sensing the environment and sending the
data to their CH or BS. We also assume that nodes transmit fixed number of data to their CH and BS, i.e., is same
at all times.
6. PROPOSED REECH-ME PROTOCOL
An efficient routing protocol is the one which consumes minimum energy and provides good coverage
area. Minimum consumption of energy leads towards better network lifetime and particularly the stability period.
Whereas good coverage area is useful in getting the required information from the whole network area. Because if
the coverage area is not good, then their would be some small areas left unattended in the network. These unattended
areas are referred to as coverage hole. The primary objective of a routing protocol is to achieve minimum energy
utilization and full coverage area. Many researches have addressed such matters as in [2] and [3]. Different
approaches are used to solve this problem, one of which was the division of the network field area into sub areas. In
the proposed technique, we divide the network area into sub areas as explained in the following subsection.
6.1. NETWORK ARCHITECTURE:
In LEACH, the CHs are elected on probabilistic basis and threshold is calculated for each node. Cluster is
formed on the basis of received signal strength from the CH and its associate nodes. In our protocol, we divide the
area in different regions as shown in fig. 3. First of all, the whole area is divided into two concentric squares. The
inner square is itself a region and is referred to as Region 1 or R1. The outer square is divided into 8 regions, 4 of
which are rectangles and 4 are squares as shown in fig. 3. The boundaries of all regions are taken as:
• R1 - (25 - 75, 25 - 75)
• R2 - (50 - 100, 75 - 100)
• R3 - (0 - 25, 75 - 100)
• R4 - (0 - 25, 50 - 75)
• R5 - (0 - 25, 25 - 50)
• R6 - (0 - 50, 0 - 25)
• R7 - (50 - 100, 0 - 25)
• R8 - (75 - 100, 25 - 50)
• R9 - (75 - 100, 50 - 75)
Each region contains fixed number of nodes. R1 contains 20 nodes, whereas, regions R2-R9 contain 10
nodes each. The BS is located at the center of the field. Fixed number of nodes are randomly distributed in their
defined regions.
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Figure 3: Regions in REECH-ME
6.2. CLUSTER HEAD SELECTION:
Unlike LEACH in which the CHs are selected on probabilistic basis, REECH-ME selects a node as the CH
of that region if it has the maximum energy before the start of that round. Initially, all nodes have the same amount
of energy and any node can become the CH for first round. So, a node is chosen randomly to become the CH of that
region for the first round. All other nodes send their data to CH which receives the data from all the nodes,
aggregates it and sends it to the BS. When the first round is completed, the amount of energy in each node would not
be the same. This is because the utilization of energy depends upon the distance between the node/CH which is
transmitting and the CH/sink which is receiving. The larger the distance, the greater energy is consumed. And
smaller the distance, smaller energy is consumed. As distance for transmission and reception is different for
different nodes, the energy consumption will also be different for different nodes. For every next round, the CH is
selected on the basis of their energies. The node with the maximum energy in a region becomes the CH of that
region for that particular round. All the regions except R1 will follow the same technique of CH selection.
7. SIMULATIONS
In this section, we assess the performance of our protocol using MATLAB. In our protocol total area is
divided into 9 regions. Region 1 uses direct communication as its routing technique. Whereas, all other regions use
clustering which is based on maximum energy of a node in that particular region. The node with the maximum
energy in a particular region becomes the CH of that region. Normal nodes of a region send their sensed data to BS
via CH of their own region. In this way, after every round, a new node which has the maximum energy in that region
is chosen as the CH of its region. The simulation parameters are given in Table 1.
Parameter Value
Network Size 100m x 100m
Node Number 100
Initial Energy of
Normal Nodes 0.5J

50nJ

50nJ

5nJ
Packet Size 4000 bits
Probability of Packet
Drop 0.3
Sink Location (50m,50m)
Table 1: Simulation Parameters
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7.1. PERFORMANCE PARAMETERS:
In the following subsections of performance parameters, we will discuss confidence interval, network
lifetime, throughput, packet drop and scalability analysis.
7.1.1. CONFIDENCE INTERVAL:
The nodes are randomly distributed in a certain region. They may be placed any where in a particular
region. Any new distribution change the location of nodes in network area. In this way, the calculations regarding
their lifetime, stability, instability region, packet drop, etc. slightly vary. So, keeping this fact in mind, we also
calculated the confidence interval of all our results. Confidence interval helps us to visualize the deviation of the
graphs from the mean value. Where, the mean value is calculated by carrying out the simulations for 5 times, and
then taking their mean. We calculate the confidence interval of all our graphs.
7.1.2. NETWORK LIFETIME:
Alive nodes refer to those nodes which have sufficient energy to sense and transmit data. The lifetime of a
network depends upon the number of alive nodes. As long as there is even one alive node in the network, its lifetime
counts. So the lifetime of a network refers to the time period from the start of the network till the death of the last
node. First of all, we compare the lifetime of LEACH with our REECH-ME. The fig. 4 shows the confidence
interval of alive nodes. We calculate the confidence interval because it helps us to visualize the deviation of the
graph from its mean value. Whereas, the mean value is calculated by carrying out 5 simulations and then taking their
mean.
Figure 4: Number of Alive Nodes
Fig. 4 shows the number of alive nodes. It can be seen that the network lifetime of our protocol is 66%
more than that of the LEACH, i.e, around 2500 and 1500 rounds respectively. The stability period is a time duration
from the start till the death of the first node. The stability period of our protocol is 79% better than the LEACH.
REECH-ME uses maximum energy based CH selection. Whereas in LEACH, the clustering is based on the
probability. Maximum energy based clustering helps to utilize the energy of only those nodes which have the
maximum energy in their regions. So the energy of all nodes is very efficiently utilized.
We always obtain the optimum number of CHs in a round, i.e 8 because we divide the whole area into 9
smaller regions. And 8 regions use clustering and each region has only one CH. So the number of clusters and CHs
is always fixed. Whereas in LEACH, the number of CHs is never the same and hence, the energy utilization is not
efficient. The instability period is the time duration between the death instants of the first node and the last node
alive in the network. The instability region in our protocol is 40% more than that of LEACH.
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Figure 5: Number of Packets Sent to BS Per Round
7.1.3. PACKETS SENT TO BS
The average packets sent to the sink in LEACH are less as compared to REECH-ME as shown in fig. 5.
This is because on an average, there would be around 10 CHs (not always exactly 10) in a round. And we know that
the normal nodes do not send their data directly to the sink. Instead, they send their data to the BS via the CH. So on
an average, there would be around 10 packets sent per round. Whereas in our Protocol, 20 nodes are present in the
region which is closest to the sink and they send their data directly to the sink. In all the other 8 regions, 8 nodes
would be CHs in each round. So, on an average there would be 28 packets sent per round. As the number of the dead
nodes increases, the number of packets sent to the BS decreases. In LEACH, the first node dies in approximately
1000 rounds. So, after that round, the number of packets sent to the sink also gradually decreases in correspondence
with the number of dead nodes. Similarly, in our Protocol, the average number of packets received also gradually
decreases with the increase in number of dead nodes.
7.1.4. PACKET DROP:
Ideally when a CH sends its data to the BS, all the packets are received successfully without any loss, i.e,
the number of packets sent to the BS are equal to the number of packets received at the BS. But in reality it does not
happen. Whenever the data is sent to BS from a CH, some of its packets do not reach the destination. This is called
Packet Drop. The reason behind this packet drop may be the interference, attenuation, noise, etc. In our protocol, we
have implemented the uniform random distribution to calculated the packet drop. This makes our protocol more
practical. We used 0.3 as the packet drop probability value. Fig. 5 shows the number of packets sent to the BS per
round, whereas, fig. 6 shows the number of packets received at the BS. Its can be observed that the number of
packets received at the BS is less than the number of packets sent to the BS. Thus, packet drop makes our protocol
more applicable and practical as well.
Figure 6: Number of Packets Received at Sink after Packet Drop
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Ideally, whenever the data is sent to the sink, it reaches without any packet loss. But in real situations this
ideal condition does not exist. That means that when data is sent to the sink from the CHs, some packets are lost. To
show this packet loss, we use the Uniform Random Distribution Model [5] to find the packet drops. We calculate the
packet drop by taking the packet drop probability as 0.3 and then calculate the confidence interval as shown in fig. 5,
6 and 7. Due to the packets drop, the number of packets that is received at the sink would be less as compared to the
number of packets sent by the CHs. So in our protocol, the number of packets received at the BS fluctuates around
20 packets. Whereas in LEACH, the number of packets received at the BS fluctuates around 7. The number of
received packets decreases as the number of dead nodes increases. As the stability region of LEACH is smaller as
compared to our protocol, the number of received packets starts to decrease from around 1000th round. Whereas in
our protocol, this decrement in the number of the received packets starts from around 1800th round.
In REECH-ME the average number of packets that is sent by the nodes to the BS is 28. By applying
packet drop probability of 0.3, the average number of packets which are dropped is around 8. Similarly on an
average the total number of packets which are sent to the BS from the nodes is 10. And 0.3 probability of packet
drop does not allow all the packets to reach the destination , i.e. BS. The average number of packets dropped due to
this probability in LEACH is 3. This behavior can be seen in Figure 7. The number of packets dropped in REECH-
ME is more as compared to the number of packets dropped in LEACH. And the reason behind it is that in REECH-
ME the average number of packets which is sent to the BS is more than that of the LEACH. The 0.3 probability on
both the protocols will result in different number of packets dropped in both protocols.
Figure 7: Number of Packets Packets Dropped
7.1.5. SCALABILITY ANALYSIS:
We also analyze the scalability of our protocol by changing the number of total nodes. We compare the
number of alive nodes and packets sent per round in each scenario. The simulation results are shown in the fig. 8 and
9 given below:
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Figure 8: Number of Packets Packets Dropped
Varying the number of nodes can vary the delay of packets reception. So, by increasing the number of
nodes can increase the delay because if TDMA is used, then most of the nodes will have to wait for their turn to
send the data. This increment in delay is crucial for time critical applications. But, in our case, we have only
considered number of alive nodes and number of packets sent to BS per round. Its can be seen that changing the
number of nodes does not effect the number of alive nodes and number of packets sent per round.
Figure 9: Number of Packets Sent to BS Per Round
7.1.6. SHORT COMPARISON OF PROTOCOLS:
Table 2 shows a short comparison of network lifetime and stability region between TEEN, LEACH, DEEC
and REECH-ME in terms of rounds. We can see that REECH-ME outperforms others in terms of network lifetime
and stability region.
Protocol Average
Stability period
Average
Lifetime Environment Classification
TEEN 1221 1947 Homogeneous Reactive
LEACH 910 1455 Homogeneous Proactive
DEEC 1395 2461 Heterogeneous Proactive
REECH-ME 1950 2510 Homogeneous Proactive
Table 2: Comparison Table
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8. CONCLUSION AND FUTURE WORK
Our proposed technique uses static clustering and CHs are selected on the basis of the maximum energy of
the nodes. This results in fixed number of CHs in each round and the optimum number of CHs is also maintained.
We implemented Packet Drop Model to make our protocol more practical. We also implemented confidence interval
to find the possible deviation of our graphs from the mean value, where mean value is calculated by simulating our
protocol 5 times and then taking its mean. We compare the results of our protocol with that of the LEACH. REECH-
ME outperforms LEACH in network lifetime, stability period, area coverage and throughput. Thus, this scheme
enhances the desired attributes, i.e, minimum energy consumption, maximum stability period, better lifetime and
throughput allot as compared with LEACH.
In future, Routing Link Matrices can also be applied on this proposed technique. Routing can be done by
adapting many different approaches as done in [14], [15] and [16]. Application of Routing Link Matrices on the
proposed scheme can be useful in achieving efficient consumption of energy in the network. We aim to introduce
multiple QoS parameters [23]. Mobility constraints also help to achieve better network lifetime similar to [39, 40]. We
can also use sink mobility to improve the energy utilization efficiency as done in [30, 37, 38]. In future, we also aim to
improve the network energy utilization in the light of wireless ad-hoc networks [29], [30], [33], [34], [35] and [36].
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... This technique can significantly improve the power consumption of WSNs. Many routing protocols are designed based on this clustering technique such as DEEC [5], HEER [6], and REECH-ME [7]. Fig. 1 shows an example of CH formation of LEACH where the cross mark is the sink node, the circle marks are normal nodes, and the circle-star marks are the CH nodes. ...
... This makes LEACH not optimum. To maintain the number of CHs in each round, REECH-ME [7] was proposed by fixing the number of clustering area. In each area, The maximum residual energy node will be selected as the CH node. ...
Conference Paper
Wireless sensor networks (WSNs) were designed for monitoring environment that is difficult to access. The energy of each node has its limit and cannot be replaced or recharged. All components of WSNs must be an energy efficient component, not only hardware component but also software component. Energy efficient routing protocol can prolong the networks lifetime. Reactive WSNs is addressed in this work. A protocol using static clustering technique with cluster head selection based on maximum residual energy is proposed. Simulation is performed to demonstrate the performance of the proposed protocol. It is shown that the proposed protocol can prolong the network lifetime better than that of the conventional protocols.
... The above-mentioned equation values are put into the threshold equation to achieve the cluster head selection by dividing them into four levels namely normal, advance, super and ultra-nodes. 4.7 REECH-ME The regional energy efficient cluster heads based on maximum energy (REECH-ME) presented in [17] routing protocol provides sink mobility with the threshold energy approach for the election and selection of the cluster-head for increasing the lifetime of the network with optimal utilization of the energy. The heterogeneity is incorporated into this routing algorithm. ...
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The threshold based clustering schemes are a new era of clustering techniques. The threshold based clustering scheme offers a process of optimal cluster formations. The optimal utilization of the energy by using threshold-based clustering scheme network lifetime by process of making clusters. The outset of optimal threshold based clustering is new research area in heterogeneous routing protocols. This study presents the review of the threshold based clustering schemes used in various routing protocols such as soft threshold, hard threshold, hierarchical clustering, two-level threshold and three level threshold schemes. The concept threshold based energy efficient routing schemes provide a variety of routing algorithms. The characteristics and properties of widely known threshold based scheme clustering protocols are compared and scrutinized by several attributes and aspects. Furthermore, challenges and discussion on possible future research areas for threshold based clustering schemes presented in this paper. Moreover, this paper discusses and presents future research, challenges, and issues for threshold based clustering the scheme based routing algorithms for heterogeneous wireless sensor networks.
... Simulations show that their model performs better than traditional models. Authors in [31][32][33] use clustering schemes to efficiently use the energy of nodes in WSNs. Nodes send their data to the CH which further routes it to the sink. ...
Thesis
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In recent years, Wireless Body Area Networks (WBANs) have achieved significant attention due to their potential applications in health care. In these networks, mobility models of human body and routing protocols largely affect the network lifetime. In this thesis, our main contribution is the proposition of a mobility model for the analysis of mobile human body while the other contributions are three proposed energy efficient routing protocols for WBANs. Mobility models play significant role in analysis of WBANs as they provide information about the distance between node and sink at any time instant. The distance between node and sink affects energy consumption, delay and path loss. In subject to more realistic scenarios, we propose mathematical models for five different postures; standing, sitting, walking, running, and laying. Nodes have different movement pattern in all of these postures. Now coming towards the first proposed routing protocol; Forwarding data Energy Efficiently with Load balancing (FEEL), in which a forwarder node is selected which reduces the transmission distance between node and sink, thereby reducing the energy consumption of nodes. In order to minimize propagation delay, Electro Cardio Graphy (ECG) and glucose level measuring nodes directly send their data to the sink. FEEL protocol is applicable for continuous monitoring of patients. However, continuous monitoring of patients is unnecessary in some applications like, temperature monitoring, etc. So, we also propose Reliable Energy Efficient Critical data routing (REEC) for critical data transmission in WBANs. In REEC, two forwarder nodes are selected on the basis of cost function and are used for relaying the data towards sink. In order to overcome the unbalanced load problem on forwarder nodes, the selection of forwarder nodes is rotated in each round. We also propose a novel routing protocol for Balanced Energy Consumption (BEC) and enhancing the network lifetime in WBANs. In BEC, relay nodes are selected based on a cost function. The nodes send their data to their nearest relay nodes to route it to the sink. Furthermore, the nodes send only critical data when their energy becomes less than a specific threshold. In order to distribute the load uniformly, relay nodes are rotated in each round based on a cost function. Simulations show improved results of our proposed protocols as compared to the selected existing protocols in terms of stability period, network lifetime and throughput.
... The main advantages of this network are in SEP doesn't require entire knowledge of the energy in every round. PEGASIS PEGASIS [11] might be derived after the algorithm ofLEACH.This algorithm makes the communication chain between the sender and recipient. Within the cluster one node is decided to send information to the sink. ...
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In the current scenario of data communication, the Wireless sensor networks (WSNs) have remarkably shown its high significance. WSN acts as very significant role within the upcoming wireless communication domain due to its properties viz., intelligence, low costs and small size. Performance like energy efficient, cost efficient, throughput, bandwidth utilization, scalability of sensor network depends both on routing protocols which are application based. Comparing with traditional wireless communication network like cellular system and mobile ad-hoc network (MANET), WSN possess unique features like higher unreliability of sensor nodes, energy consumption and storage constraint which presents many new demanding aspects in the application of WSN. Keeping this into consideration, we have carried out an extensive survey on the routing protocols. The work studies a survey of different routing protocols with their implementation aspects and practicability. Based on the available literature, it is very hard to recommend any particular protocol as standard for implementation, since these are exclusively application dependents. The work can be further extended in terms of Hybrid protocols which may carry the advantages of the respective protocols along with energy efficient criteria for practical implementation. Further, these protocol based studies can be used as in case of cooperative WSN communication, ex: Internet of Things (IoT) node placements, Information Centric Network (ICN), etc.
... In recent years, there are a lot of routing protocols in WSN are used that a few of them have considered the requirements of network applications in time critical data transmission is important [8][9][10][11][12][13][14][15]. In wireless sensor network gathers data from the relevant data would reduce the large amounts of data traffic on the network, avoid information overload, creating a more accurate signal and requires less energy than sending all unprocessed data inside the network. ...
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Along with handling and poor storage capacity, each sensor in wireless sensor network (WSN) is equipped with a limited power source and very difficult to be replaced in most application environments. Improving the energy savings in applications for wireless sensor networks is necessary. In this paper, we mainly focus on energy consumption savings in applications for wireless sensor networks time critical requirements. Our Paper accompanying analysis of advanced technologies for energy saving techniques for the optimization of energy efficiency together with the data transmission is optimal. Moreover, we propose improvements to increase energy savings in applications for wireless sensor networks require time critical (LEACH improvements). Simulation results show that our proposed protocol significantly better than LEACH about the formation of clusters in each round, the average power, the number of nodes alive and average total received data in base stations.
... where α, β, γ, and δ are the weights and DV, DA, Dφ and DT are the distances returning a value between 0 and 1.We have used Sorensen [20] as a distance metric. The distance d ij between feature vectors F i and F j can be described as: ...
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In past years there has been increasing interest in field of Wireless Sensor Networks (WSNs). One of the major issue of WSNs is development of energy efficient routing protocols. Clustering is an effective way to increase energy efficiency. Mostly, heterogenous protocols consider two or three energy level of nodes. In reality, heterogonous WSNs contain large range of energy levels. By analyzing communication energy consumption of the clusters and large range of energy levels in heterogenous WSN, we propose BEENISH (Balanced Energy Efficient Network Integrated Super Heterogenous) Protocol. It assumes WSN containing four energy levels of nodes. Here, Cluster Heads (CHs) are elected on the bases of residual energy level of nodes. Simulation results show that it performs better than existing clustering protocols in heterogeneous WSNs. Our protocol achieve longer stability, lifetime and more effective messages than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC).
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Wireless Sensor Networks (WSNs) with their dynamic applications gained a tremendous attention of researchers. Constant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network life-time as much as one could, for efficient and optimal utilization of resources. Different approaches based upon clustering are proposed for optimum functionality. Network life-time is always related with energy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose Quadrature-LEACH (Q-LEACH) for homogenous networks which enhances stability period, network life-time and throughput quiet significantly.
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Cluster based routing technique is most popular routing technique in Wireless Sensor Networks (WSNs). Due to varying need of WSN applications efficient energy utilization in routing protocols is still a potential area of research. In this research work we introduced a new energy efficient cluster based routing technique. In this technique we tried to overcome the problem of coverage hole and energy hole. In our technique we controlled these problems by introducing density controlled uniform distribution of nodes and fixing optimum number of Cluster Heads (CHs) in each round. Finally we verified our technique by experimental results of MATLAB simulations.
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Wireless Sensor Networks (WSNs) consist of large number of randomly deployed energy constrained sensor nodes. Sensor nodes have ability to sense and send sensed data to Base Station (BS). Sensing as well as transmitting data towards BS require high energy. In WSNs, saving energy and extending network lifetime are great challenges. Clustering is a key technique used to optimize energy consumption in WSNs. In this paper, we propose a novel clustering based routing technique: Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) for heterogeneous WSNs. Our technique is based on changing dynamically and with more efficiency the Cluster Head (CH) election probability. Simulation results show that our proposed protocol achieves longer lifetime, stability period and more effective messages to BS than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC) in heterogeneous environments.