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An Efficient Digital Cluster Scheme to Improve the
Lifetime Ratio of Wireless Sensor Networks
Soni M
Assistant Professor, Dept. of EEE
Dayananda Sagar College of Engineering
Bengaluru, India
sonimuju0211@gmail.com
Akshatha Bhat
Assistant Professor, Dept. of ECE
CMR University
Bengaluru, India
bhatakshatha2@gmail.com
Sachin Aralikatti
Assistant Professor, Dept. of ECE
CMR Institute of Technology
Bengaluru, India
sachin.a@cmrit.ac.in
Afroz Pasha
Assistant Professor, Dept. of EEE
HKBK College of Engineering
Bengaluru, India
afrozplus@gmail.com
Niranjan L
Assistant Professor, Dept. of ECE
CMR Institute of Technology
Bengaluru, India
niranjanl1983@gmail.com
Yousuf Madar R
Assistant Professor, Dept. of EEE
HKBK College of Engineering
Bengaluru, India
yousufmadar@hotmail.com
Abstract—The amount of data that can be transmitted is
determined by how effectively the restricted energy is managed
for an increased system life once interconnections are built into
the wireless sensor network because batteries cannot be changed.
In comparison to routing schemes, digital cluster routing (DCR)
shapes the most effective digital cluster while taking node firm-
ness into account, chooses the nearest network, and controls the
directing information to greatly minimize overhead by referring
to directing table data only inside the digital cluster and not via
the head. Additionally, DCR has a longer survival time and a
shorter transmission latency.
Index Terms—MANET, Sink Node, LEACH, Sensor Network,
Sensor Node.
I. INTRODUCTION
Small device nodes, such as wireless transmitters, sensing
modules, and microcontrollers, make up the Wireless Sensor
Network (WSN). Additionally, node information is mostly
transferred via multi-hop communication to the sink node,
which collects data. With the help of many sensor nodes, the
WSN was initially developed for military usage to monitor
and reconnoiter inaccessible places. To gather other types of
information, however, its application has broadened to include
building risk assessments, environmental monitoring, medical
services, patient monitoring, etc. [1]. In the sensor network, a
huge quantity of sensor networks is put in a vast sensor field,
emulating the Mobile Ad-Hoc Network (MANET) scenario,
lacking dependable foundations like Access Points (AP), to
build diverse dynamic structures and have nodes self-reliant
and independent of each other [2, 3]. Here, it’s important to
think about how to make the most of the sensor node’s finite
energy supply. The battery that powers the sensor node cannot
be changed or recharged because of the conditions under
which it operates. Energy efficiency, the precision of sensed
information, and amenity quality are taken into account while
evaluating the WSN’s performance. The most important one
is energy efficiency. Because energy is consumed intensely,
The amount of time a device can operate before running out
of power depends on its energy efficiency [4]. Depending on
the node design, the current routing architecture in the sensor
network can be classified into a hierarchy-based and a plane-
based one. In contrast to the hierarchy-based routing system,
where several groups are formed to build the node grading
for information communication, nodes communicate with one
another at an equal level in the plane-based directing system
[5, 6]. The management of control packets and the number of
routing messages increase when the plane-based networking
approach is employed with a large-scale network system. Low
Energy Adaptive Clustering Hierarchy (LEACH), a hierarchy-
based directing technique, has been proposed as a solution to
this issue [7, 8]. Routing overhead rises in the case of LEACH
because of ineffective clusters and a greater reliance on cluster
heads. The Digital cluster Routing (DCR) method is proposed
in this paper as a solution to these issues by fusing plane-
based and hierarchy-based routing schemes. DCR constructs
effective DCR taking into account node compactness. In
addition to using its heads to communicate with other clusters,
the digital cluster can also choose the closest node by using
its internal routing table. Additionally, the costs of managing
routing tables can be significantly condensed since just routing
information for the cluster is kept current.
II. RE CE NT ST UDY
A. Orientation to the Wireless Sensor Routing Algorithm
The sensor network depicted in Figure 1 demonstrates
how the data from each node’s sensor is read [9]. There
may be two sets of protocols depending on the type of
network: cluster-based hierarchical routing and plane routing.
Underneath the plane routing protocol, It is believed that
the network is one region, and each node equally takes part
in routing. According to their roles, nodes are arranged in
a hierarchy inside the network’s clusters using the cluster-
based protocol [10-12]. Information gathered by minor nodes
is sent to superior nodes, who consolidate them before sending
2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES) | 979-8-3503-4729-6/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICSSES58299.2023.10199240
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them to the BS. TEEN, LEACH, and LEACH-Centralized
(LEACH-C) are well-known procedures [13–16]. Additionally,
the network can be divided into proactive and reactive modes
depending on the intended applications and operating modes.
In a proactive network, field nodes only operate throughout
their sequence in order to handle and gather information,
which they subsequently relay to their upper nodes. LEACH
and LEACH-C are suited for monitoring data on a regular
basis [17-20]. However, in a dynamic network, each node in
the field transmits the signal in a sequential manner, reacts
immediately to a difference in the value, and then sends the
updated data to their higher node. Time-sensitive applications
can use this. TEEN is the most well-known illustration.
Fig. 1. Wireless detector routing architecture
B. LEACH Protocol
There are numerous clusters in the LEACH-based WSN.
Each protocol has a cluster head (CH), which is responsible for
managing entire detector nodes within the group, combining
sensor node data, also sending it to the base station (BS), as
well as a non-CH, it receives information and sends it to its
CH. Much energy is used, particularly when the data from non-
CHs must be combined by the CH before being sent to the
distant BS. Therefore, every time the sequence starts, all nodes
compete for the CH’s attention according to a predetermined
possibility in order to ensure that each node plays a part as
CH equally [21].
Fig. 2. A timeline displaying LEACH’s activity
Rounds are used in the LEACH protocol’s configuration and
execution, as seen in Figure 2. Every round begins with setup,
during which a head is chosen to create a cluster, followed
by steady state, during which information is sent beginning
non-CH towards CH, also finally from CH on the way to BS
[22–26]. For the entirety of the unscheduled slot period, to
conserve energy, the node enters sleep mode. A new series
starts when one does, an innovative CH is chosen, then after
that, the previous procedure is performed again. Data is sent to
the CH even if the information already gathered and the current
data detected are identical. In other words, energy is used
to send pointless data between member nodes. In addition,
because the cluster is constructed depending on the position of
the chosen CH and the CH is chosen based on probability, It’s
possible that the cluster was built with undesirable geological
formations.
C. Sensor Network Features
The sensor network consists of sink nodes that collect and
communicate information to the outside, small sensor nodes
that transmit the information, and processors that process
detected and gathered information [27]. The sensor network,
which is utilized extensively in scientific, medical, military,
and commercial applications, is fundamentally designed to
autonomously collect remote information, in contrast to con-
ventional networks. There are considerable differences in the
sensor network’s application, control, and setup. The conven-
tional network should ensure Quality of Service (QoS), high
bandwidth consumption requires proper mobile node setup,
routing, and mobility management. In a sensor network, it is
vital to manage the energy used by the sensor nodes because
numerous tiny detectors are operating in a space that is difficult
for people to access and where power cannot be replenished.
A sensor network is also built up of a few hundred to tens of
thousands of nodes as opposed to standard wireless settings
like Ad Hoc. Consequently, routing overhead may result from
routing numerous nodes. The massive sensor network needs
to address this problem.
III. DIGITAL CLUSTER ROUTING (DCR)
To create a network, the digital cluster simply communicates
routing information with its nearest node. Since the digital
cluster head is no longer necessary, the sensor node can now
communicate data directly to other cluster nodes or sink nodes.
Digital cluster heads are chosen based on node compactness
to construct a digital cluster. Then, a number of digital clusters
are constructed depending on the heads that were chosen.
The digital cluster is constructed for partition nodes that are
not part of it. Additionally, the level of digital clusters is
chosen to facilitate data exchange. To choose the digital cluster
head, entire detector nodes broadcast the ADV signal to each
other as well as other neighboring networks. The ADV signal
has not yet received an ACK signal. To determine nearby
node information, the overall number of ADV signals from
neighboring networks is used. As the cluster head, one node
is selected if it receives messages whose sum exceeds the norm
determined by the node compactness. If two nearby networks
are identified as cluster members, the greater ADV message
quantity node will remain in the digital cluster head. The
digital cluster head is carefully chosen just on one occasion to
reduce the administrative burden of creating the digital cluster.
The standard head selection value is now reduced by 50%. The
selection of the virtual node head may require other nodes. The
selection of cluster heads is showed in Equation 1.
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TABLE I
ASS ESS ME NT CONDITIONS
Topology System
Dimensions
Node
Count
Communi-
cation Range
Heads
Count
a100m x 100m 100 100m 2
b150m x 150m 100 100m 4
c200m x 200m 200 100m 6
d250m x 250m 200 150m 8
e300m x 300m 200 150m 10
Head SelectionCondition = [(N∗rrR2)/2A](1)
Once the cluster head remains within a LEACH system,
each node evaluates its chances of becoming the head cluster
using Equation 2. An indicator function is Ci(t). If the network
has been the cluster head at the time of the r mod (Nk), the
value of the indicator function would be 0, besides if it weren’t,
it would be 1. In other words, a node cannot be chosen as the
head again if it already served as the head during r mod(N,k).
Pi(t) = k
N−k(rmod n
k):Ci(t) = 1;Pi(t) = 0 : Ci(t) = 1 (2)
In Equation 2, is the indicator for the node, t is the passage
of time, The entire node count is N, the number of clusters is
k, also the round number is r. When the head is picked from
networks that were never a head earlier, the quantity of rounds
multiplies in that round, leading to a straightforward increase
in Pi (t). This method occurs at the frequency of Nk to provide
each node an equal opportunity to become the head node.
IV. EVALUATION OF PERFORMANCE AND TESTS
A. Assessment Environment
Table 1 displays the network size, node count, transmission
range, and headcount for the different sensor network topolo-
gies that were tested.
Through this interface, the NI 9792 SSC gateway accepts
packets of information mostly from SSC as well as transmits
those towards the fiber to a gigabit Internet adapter. Until the
events under investigation smart grid technology occur contin-
uously, independently, and with a constant average level, the
arrival process in our queuing model developed is exponential.
As all data packets have the same length and are served at
regular intervals, the service time distribution is deterministic.
There will be only one server, equivalent to that of the NI
9792 gateway’s single microprocessor. The non-preemptive
priority service discipline of the queue ensures that once a
much larger packet of data enters the system, the operation of a
reduced data transmission is not stopped. For the reasons listed
overhead, we practice the M/D/1/PNPN scheduling algorithm
for the whole of our paper. We primarily concentrate on entry
speeds of dual smart grid technologies that use this queuing
model: synchrophasors and teleprotection.
TABLE II
NET WOR K EXISTENCE TIMES ASSESSMENT
Networking
Protocol
Topology
A
Topology
B
DSDV 1526 241
AODV 1412 236
LEACH 1279 213
DCR 1561 276
V. SU RVI VAL TIME TEST
The survival time and the current routing strategy were
compared in order to assess the sensor network’s DCR energy
efficiency. In this test, topologies A and E received a starting
energy of 100 J, after which CBR information was transferred
0.5 seconds every time. In Table 2, Network existence times
were seen. As seen below, topology A takes a lengthy persis-
tence period within the system of LEACH, DSDV, AODV, as
well as DCR shown in figures 3, 4, 5, 6, 7, and 8.
Fig. 3. Topology-A Energy Dissipation of nodes vs number of nodes
Fig. 4. Topology-B Energy Dissipation of nodes vs number of nodes
DCR outlived LEACH, DSDV, and AODV by 6%, 9%,
also 22%, correspondingly. When a new route was specified,
route-searching messages within the AODV system rapidly
rose, which resulted in a large rise in energy consumption.
As 100 routing tables had to be kept up to send data in the
DSDV system, this caused overhead and significant energy
use. The energy was used less than in the AODV and DSDV
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Fig. 5. Topology-C Energy Dissipation of nodes vs number of nodes
Fig. 6. Topology-D Energy Dissipation of nodes vs number of nodes
Fig. 7. Topology-A Routing Overhead vs number of nodes
schemes thanks to the LEACH scheme’s reduction of the
overhead brought on by directing information as well as
routing messages. However, power consumption exceeded that
of the suggested DCR strategy since clusters were generated
after each loop. Additionally, DCR’s survival time outlasted
LEACH, DSDV, and AODV by 11%, 19%, and 27% cor-
respondingly within the test with Topology E, in which the
overall node count, transmission range, and network size all
significantly increased. As seen in ADV there isn’t an ACK
indication yet for the ADV transmission. The total amount
of ADV signals from nearby networks is utilised to identify
Fig. 8. Topology-A Throughput vs number of nodes
nearby node information. One node is chosen to serve as the
cluster head if it receives data whose total is greater than the
threshold set by the node density. The network with the higher
ADV packet quantity will stay in the digital cluster head if
two adjacent networks are recognised as cluster members. To
lessen the administrative load of setting up the digital cluster,
the digital cluster head is meticulously selected just once.
Other nodes might be needed depending on the virtual node
head that is chosen.
VI. CONCLUSION
Due to the maintenance of routing tables and an increase
in routing messages, the plane-based routing technique in the
detector system adds overhead. The hierarchy-based directing
strategy, which manages the routing table with many clusters,
is offered as a solution to this issue and has less overhead.
Cluster overhead is caused by insufficient cluster development
as well as dependence taking place on the cluster head
designated in each round. The virtual root node is chosen in the
DCR scheme taking into account the network’s existing sensor
nodes’ compactness, which results in increased efficiency.
Additionally, It is possible to reduce cluster overhead because
a digital cluster only forms in the composite structure area.
Additionally, only the routing table within the node’s digital
cluster is used to send data because the digital clustering
level has been configured. By doing this, the overhead brought
on by packet forwarding and routing tables can be reduced.
The test demonstrated that the proposed DCR technique, with
higher network size, communication range, and node count,
had decreased communication delays in the large-scale sensor
network.
ACKNOWLEDGMENT
We would like to thank COE-Embedded Systems under
ECE department of CMR Institute of Technology, Bengaluru
for supporting and encouraging us in the implementation of
this research.
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