Lifetime enhanced energy efficient wireless sensor networks using renewable energy

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 3088~3098
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp3088-3098  3088
Journal homepage: http://ijece.iaescore.com
Lifetime enhanced energy efficient wireless sensor networks
using renewable energy
Trupti Shripad Tagare1
, Rajashree Narendra2
1
Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India
2
Department of Electronics and Communication Engineering, Dayananda Sagar University, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Aug 7, 2022
Revised Sep 9, 2022
Accepted Oct 1, 2022
In this paper, we consider a remote environment with randomly deployed
sensor nodes, with an initial energy of E0 (J) and a solar panel. A
hierarchical clustering technique is implemented. At each round, the normal
nodes send the sensed data to the nearest cluster head (CH) which is chosen
on the probability value. Data after aggregation at CHs is sent to the base
station (BS). CH requires more energy than normal nodes. Here, we energize
only CHs if their energy is less than 5% of its initial value with the use of
solar energy. We evaluate parameters like energy consumption, the lifetime
of the network, and data packets sent to CH and BS. The obtained results are
compared with existing techniques. The proposed protocol provides better
energy efficiency and network lifetime. The results show increased stability
with delayed death of the first node. The network lifetime of the proposed
protocol is compared to the multi-level hybrid energy efficient distributed
(MLHEED) technique and low-energy adaptive clustering hierarchy
(LEACH) variants. Network lifetime is enhanced by 13.35%. Energy
consumption is reduced with respect to MLHEED-4, 5, and 6 by 7.15%,
12.10%, and 14.975% respectively. The no. of packets transferred to the BS
is greater than the MLHEED protocol by 39.03%.
Keywords:
Cluster head
Clustering
Energy efficiency
Heterogeneity
Number of rounds
This is an open access article under the CC BY-SA license.
Corresponding Author:
Trupti Shripad Tagare
Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering
Shavige Malleshwara Hills, Kumarswamy Layout, Bengaluru, 560078, Karnataka, India
Email: truptitagare-ece@dayanandasagar.edu
1. INTRODUCTION
The enormous research and development in the field of semiconductor technology, communication,
and natural science have led to a global distribution of wide-range wireless sensor networks (WSNs). We are
witnessing the next generation of WSNs that vary substantially from the networks set up as recently as 5 to
10 years ago. Now, it is possible to accommodate many sensor nodes with advanced processing and
communication abilities. Further, the networking standards equipped with security, confidentiality, and the
least latency have led to the expansion of WSN into the field of control and monitoring the area [1].
Basically, a WSN comprises tiny nodes which are positioned in inhospitable, inaccessible environments to
sense the physical parameters of the environment [2]. These nodes have limited resources in terms of
bandwidth, memory, and energy The WSNs are classified into homogeneous and heterogeneous networks
[3], [4]. When the nodes have the same functional capabilities in terms of sensing and initial energy, they are
called homogeneous networks or heterogeneous. Although heterogeneous networks require more network
energy, they provide large network lifetime when compared with homogeneous networks [5]. Further, when
we consider renewable sources of energy like solar energy for energizing the nodes, we achieve clean energy
consumption and support to green environment and at the same time achieve a large network lifetime.

Int J Elec & Comp Eng ISSN: 2088-8708 
Lifetime enhanced energy efficient wireless sensor networks using renewable energy (Trupti Shripad Tagare)
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In this research work, we consider a two-level heterogeneity wherein all the nodes will have the
same initial energy at first. At each round of cluster head election based on probability using a hierarchical
clustering approach. All normal nodes will have the same initial energy while the cluster head nodes will
have initial energy plus solar energy only in case their energy level is equal to or less than 0.01 J. We
illustrate the simulation results using MATLAB 2020a for a WSN using renewable sources of energy i.e.,
solar energy to energize only the required cluster heads selected in each round. We evaluate the performance
of the network by comparing the proposed protocol with the multi-level hybrid energy efficient distributed
(MLHEED) technique. The MLHEED protocol proposed in [5] showed a heterogeneity up to 6 levels which
used the remaining energy of the nodes and its density to select the cluster head (CH). The proposed protocol
works using the probability parameter for CH selection. We evaluate the stability period, network lifetime,
energy efficiency, and the number of packets transferred to the CH and base station (BS) using the proposed
protocol and compare it with the results of existing techniques in this work.
Efficient energy consumption and increased network lifetime have always been important areas for
research in the field of WSN. Here, we present some works carried out by researchers in achieving the said
requirements. The concept of heterogeneity in the WSNs with multi-levels helps in increasing the network
lifetime. Singh [5] provided a 6-level heterogeneity and achieved an increase in network lifetime wherein the
last node was alive up to the 2,997th
round but the total energy of the network utilized was 36.8 J. Elshrkawey
et al. [6] clearly highlighted, that heterogeneity helped in increasing the network lifetime and proved to be
better than the existing protocols like low-energy adaptive clustering hierarchy (LEACH), modified LEACH,
energy efficient clustering and data aggregation (EECDA), distributed stable cluster head election (DSCHE).
Qing et al. [7] proposed distributed energy-efficient clustering (DEEC) protocol, where the nodes with high
initial and residual energy would be selected as cluster heads. The results showed that DEEC achieved a longer
network lifetime. Further, Javaid et al. [8] worked on an enhanced developed distributed energy-efficient
clustering scheme (EDDEEC) for heterogeneous WSNs. Results achieved longer lifetime and stability period.
Next, heterogeneous WSN routing protocols DEEC and enhanced distributed energy efficient clustering
(EDEEC) were compared in [9] and the results showed that the EDEEC protocol provided better performance
values than DEEC. The concept of a green wireless sensor network is the latest trend in the area of WSN.
López-Ardao et al. [10] provided a survey of the different ways to achieve energy balancing and identified the
main trending topics. An extensive survey of all the available heterogeneous wireless sensor networks was
provided in [11]. Otu [12] proposed an energy coverage ratio clustering protocol (E-CRCP) that utilized a
regional coverage area ratio to minimize the consumption of energy. The protocol was tested for different a
number of nodes, and rounds. The results showed better lifetime, load balancing, and overall energy
consumption. El-Sayed [13] proposed a method for CH selection that was energy efficient and thus increased
the life of the network. Zayed et al. [14] provided an in-depth understanding of the hierarchical clustering-
based protocols namely MODLEACH and mobile sink improved energy-efficient power-efficient gathering in
sensor information system-based routing protocol (MIEEPB) protocols. The results upheld the performance of
MIEEPB. Heinzelman et al. [15], [16] implemented different clustering techniques The results proved that the
change in the parameters like a number of nodes, area, and a number of rounds affect the efficiency and
lifetime of WSN. Saini and Sharma [17] provided a three-tier approach heterogeneity to enhance network
lifetime using an enhanced threshold approach. Junior et al. [18] provided a method to use residual energy and
solar energy to increase the lifetime of the network. In [19], the method and usefulness of using MATLAB to
carry out the simulations for wireless sensor networks were discussed. The results show that MATLAB helps
in providing accurate simulation details for the network. Sagare et al. [20] showed a basic understanding of the
use of clustering and the role of probability in selecting the cluster head.
From the literature review, it is learned that the multilevel heterogeneous network models offer better
network lifetime. Singh [5] suggests that increased network energy is more favorable than adding more nodes to
the network. However, in our proposed clustering technique we consider a same number of nodes as in [5] and
develop a network model which promises not only an increased network lifetime but also reduced network
energy requirement. Further, we energize a few nodes to bring in the concept of heterogeneity in the network by
making use of solar energy. Also, this paper gives a comparison of the proposed protocols: modified LEACH
(mod-LEACH) [21], LEACH [22], extended-MODLEACH (E-MODLEACH) [23], and DEEC [24].
This paper is organized as follows: section 2 presents the proposed method i.e., a novel clustering
technique using renewable energy for a WSN; which discusses the assumptions made, initial parameters for
the network, the algorithm, and the flowchart. Section 3 presents the simulated results using MATLAB
2020a. In section 4, we present the results and discussion by carrying out the comparison between the
proposed protocol and MLHEED protocol and outcomes. Further, the comparison results with other
protocols like mod-LEACH, LEACH, E-MODLEACH, and DEEC are presented. The evaluation parameters
considered are the first dead node round number, last dead node round number, alive nodes, packets sent to
CH, and packets sent to BS. Finally, section 5 gives the concluding remarks for this paper.

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Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3088-3098
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2. PROPOSED METHOD
Here, the novel clustering technique network model using solar energy for WSN is elucidated in
detail. The novel clustering technique follows the hierarchical clustering technique with heterogeneity
introduced in terms of node energy (in Joules). All the nodes with the same initial energy will participate in
the CH selection and then the selected CH will be responsible for collecting the sensed data from other
nodes, performing data aggregation, and further transmitting the aggregated data to the BS. The CH with its
energy of less than 5% of its initial energy will get charged using the solar panel. The following subsections
describe the technique with the assumptions made, the model, and the algorithm.
2.1. Assumptions
Each node is deployed randomly and is stationary. The location of the base station is fixed. The
nodes are randomly placed in a pre-defined network area and not location aware as we cut down the cost of
the network by not using global positioning system (GPS) enabled nodes. All the nodes initially have the
same energy and resources (central processing unit or CPU and memory) but at each round after cluster head
election, the CH will be energized by the solar panel, if its energy is less than 5% of its initial energy value.
Thus, bringing in heterogeneity in the network. Each node is equipped with a solar panel to energize itself to
0.35 times the initial energy of the node. The data aggregation takes place only in the CH where the
compression of data packets takes place. The node is considered dead when its energy becomes 0 J after
energy dissipation. The base station is located at the center of the area under consideration. The energy
consumed in transmitting or receiving a data packet is considered the same. There are only 2 energy levels in
the network. Level 1-with nodes having the same initial energy. Level 2 of the cluster heads whose remaining
energy is less than 5% of their initial value will be energized by solar energy.
2.2. The model
Even though a lot of work is carried out in the field of energy efficiency, increasing lifetime and
energy harvesting in WSN, no specific work of using solar energy to energize only the CH is available in the
literature reviewed. In the proposed technique, a large number of sensor nodes (100 in our case) equipped
with solar panels are randomly placed in a 100×100 m2
area. The BS is located at the center of this area i.e.,
(50, 50) location. Solar energy which is a renewable source of energy is used to energize the solar panels of
the sensor nodes. The proposed model is depicted in Figure 1.
Figure 1. Depiction of the predicted network model
At each round of the CH election, based on probability the CH is selected. The other nodes in the
cluster are informed about the CH node via a broadcast message. Thus, on sensing the physical parameters,
the normal nodes transmit the sensed data in the form of data packets to their nearest CH. CH in turn
performs data aggregation wherein the redundant data packets are discarded, and the remaining data packets

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are transmitted to the BS for further data analysis and evaluation. All the sensor nodes as well as the BS are
static. The sensor nodes have an initial energy of 0.2 J each. The energy model obeys the same radio model
used in [15] as given in (1) and (2).
𝐸𝑡(𝑏, 𝑑) = {
(𝐸𝑒 × 𝑏) + (𝐸𝑓𝑠 × 𝑏 × 𝑑2
), 𝑑 ≤ 𝑑0
(𝐸𝑒 × 𝑏) + (𝐸𝑎𝑚 × 𝑏 × 𝑑4), 𝑑 > 𝑑0 (1)
𝑑0 = √(𝐸𝑓𝑠/𝐸𝑎𝑚) (2)
where 𝐸𝑡(𝑏, 𝑑) is the energy consumed by the transmitter; in transmitting b bits at a transmission distance of
d, 𝐸𝑒 is the energy consumed by the electronic devices, 𝐸𝑓𝑠 is the energy consumed in amplification (in free
space), Eam is the power amplifier value, and 𝑑0 is the threshold distance. The probability for CH selection
is 0.1. Table 1 gives the simulation parameters for the proposed protocol. All the parameters are set in
accordance with the parameters as set in [5] to make the comparison more apt and relatable.
Table 1. Simulation parameters for proposed protocol
Simulation Parameters Values
Network area 100×100 m2
Base Station (50, 50)
Number of nodes: n 100
Initial Energy: E0 0.2 J
Threshold Distance: d0 70 m
Energy consumed in electronics circuit: Ee=Etx=Erx 50 nJ/bit
The nodes probability to become cluster head 0.1
Energy consumed in amplification: Efs
Multi path loss: Emp
10 pJ
0.0013 pJ
Energy spent in CH for data aggregation: EDA 5 nJ
Total number of rounds: rmax 5000
The algorithm for the proposed protocol is based on these steps.
1) All the nodes equipped with solar panels are randomly deployed in an area of 100×100 m2
.
2) The location on the BS and all the parameters for the network are set.
3) The CH is selected based on probability.
4) The CHs’ selected information is broadcasted to all neighboring nodes to send the sensed data to the
nearest CH.
5) If the node is CH, then follow steps 6 and 7 else go to step 9.
6) The CH node performs data aggregation on received data packets.
7) The data packets are sent to base station.
8) The CH gets energy from the solar panel for that round if its node energy is less than 0.01 J. Then it is
energized as S(i). E=S(i). E+0.35*E0.
9) The nodes send the data packets to CH.
10) Et estimates the total energy (due to battery alone) of the network. (Et=Et+E(i), where i is the round
number).
11) Re estimates the renewable source of energy used. (Re=Re+R(i), where i is the round number).
12) Te estimates the total energy used in the network: Te=Et+Re.
13) The first dead node is observed for the round number and accordingly its flag is set. (When S(i). E<=0).
14) Then the tenth dead node is observed when 10 percent of the total number of nodes are dead and
accordingly its flag is set. when S(i). E==0.1xn.
15) Repeat steps 3 to 12 until all the rounds considered rmax for the network are carried out.
16) Total number of alive nodes and dead nodes after rmax number of rounds is estimated.
17) Number of data packets sent to BS and CH are evaluated.
18) All the figures for different parameters are plotted.
19) Total energy consumed and network lifetime is evaluated.
20) Comparative study on the observed results is carried out.
2.3. The flowchart
Figure 2 depicts the flow of events in the proposed protocol. The process in the diagram repeats for
the number of rounds selected for the protocol. Finally, when all the nodes are dead it stops. As long as there
are active nodes the data packets will be sent to the CH and BS.

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Figure 2. Flowchart of the proposed protocol
YES
NO
Sense data and send it to
the nearest Cluster
Head, evaluate the
energy consumed by the
node based on its
distance w.r.t d0
NO
The Cluster Head
selected information is
broadcasted to the
entire network
YES
START
All the simulation parameters are set
Cluster Head for the round is selected based on probability.
Sensor nodes are randomly placed in the area under study.
Is the
node
CH?
Is the Cluster Head
energy less than
0.01 J?
Collect data packets from other nodes, perform data aggregation, send aggregated data
to base station. Evaluate energy consumed by the nodes based on its distance w.r.t d0.
Energize the cluster head with solar energy by E0*0.35 J. Evaluate the
Total energy= Battery energy + Solar Energy
STOP

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3. SIMULATED RESULTS
The results of the proposed protocol are presented in this section. Here, we observe the results for a
number of dead nodes, alive nodes, packets sent to CH, packets sent to BS, number of CHs at each round.
MATLAB 2020a version is used in this research work to carry out all the required simulations [25]. We
obtain the simulated results for the following parameters the number of dead nodes at each round, the number
of alive nodes, the number of packets sent to CH and BS, and the number of CHs at each round. The results
clearly show that the proposed protocol provides a longer network lifetime, and a large number of data
packets are transmitted at relatively low usage of network energy.
3.1. The number of dead nodes at each round
The simulation result in Figure 3 shows that with 100 nodes in a 10,000 sq. m area and for
5,000 rounds, the first dead node is observed at the 473rd
round. Further, at the 525th
round, 10% of the total
number of nodes is dead. This shows a good stability period. Further, all nodes die by 3 the 459th
round.
Thus, exhibiting that the proposed protocol provides a longer network lifetime.
Figure 3. The number of dead nodes at each round
3.2. The number of alive nodes at each round
Similarly, the simulation result in Figure 4 shows that for given parameters; the proposed protocol
has active nodes even at the 3,500th
round depicting a longer network lifetime. Thus, the use of solar energy
keeps the cluster head nodes active for a larger number of rounds. The data aggregation at the CH further
reduces the energy consumed in sending even the redundant data packets to the BS. Hence, this all again
leads to an increase in the network lifetime.
Figure 4. The number of alive nodes at each round

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3.3. The number of data packets sent to cluster head
The simulation result in Figure 5 shows that for the given network, there is a steady increase in the
number of data packets sent at each round, further a constant amount of data packets is transmitted to the
CHs by the normal nodes. Totally, 45,740 data packets are sent to the CH by the end of 5,000 rounds. The
results show that the network can carry a huge amounts of data packets across the nodes.
Figure 5. The number of packets sent to CH at each round
3.4. The number of data packets sent to base station
Next, the simulation result in Figure 6 shows that for the set parameters, out of 45,740 data packets
received by the CHs only 41,060 data packets are sent to the BS. It shows that the CH performs data
aggregation and sends only the non-redundant data packets to the BS. Again, the results show that the
network can carry a huge amount of data packets from the CHs. Also, the energy of the network is efficiently
utilized due to data aggregation at the CHs.
Figure 6. The number of packets sent to BS at each round
3.5. The number of cluster heads at each round
The simulation result in Figure 7 shows that for the given parameter, initially, the number of CHs is
high, however with each round the number of dead nodes increases, and hence the number of CH selections
for the given probability decreases. The results show that the protocol has active CHs till 3,459 rounds to
send data packets to BS. For the first 500 rounds, there are a large number of CHs present in the network and
then they decrease as the number of dead nodes in the network increase.

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Figure 7. The number of CHs at each round
4. RESULTS AND DISCUSSION
Here, we discuss the results of the proposed protocol and compare them with results obtained from
[5]. The simulation parameter set for the comparison between the MLHEED protocol, and the proposed
protocol is maintained as shown in Table 1. The proposed algorithm and MLHEED [1] comparison for the
evaluation parameters first dead and all dead round number is as shown in Table 2. The results show that the
proposed algorithm shows a longer network lifetime. Also, the time for the first dead node is 473rd
round
which is better than MLHEED-1, 2, 3, 4 and 5 protocols. MLHEED-6 has its first dead node at round number
601 [5] compared to the proposed protocol which is the 473rd
round but all the dead nodes for MLHEED-6
happened at 2,997 compared to 3,459 for the proposed protocol.
From Table 2, it is clear that the proposed protocol provides a longer network lifetime compared to
all the MLHEED techniques. MLHEED-1, MLHEED-2, MLHEED-3, MLHEED-4, MLHEED-5, and
MLHEED-6 remained active for 668; 1,156; 1,626; 2,092; 2,458; and 2,997 rounds respectively [5] while the
proposed protocol remained active for 3,459 rounds. Thus, the percentage increase in network lifetime
achieved by the proposed protocol with respect to the MLHEED-1, MLHEED-2, MLHEED-3, MLHEED-4,
MLHEED-5, and MLHEED-6 is 80.68%, 66.57%, 52.99%, 39.52%, 28.93%, and 13.35% respectively.
Table 2. Round number for first and last node when dead
Protocol First Dead Node All Dead
MLHEED-1 [5] 167 668
MLHEED-2 [5] 192 1156
MLHEED-3 [5] 254 1626
MLHEED-4 [5] 365 2092
MLHEED-5 [5] 459 2458
MLHEED-6 [5] 601 2997
Proposed Protocol 473 3459
We now discuss the proposed protocol results when the number of rounds is varied. The results are
tabulated in Table 3. It is observed from the table that for a different number of rounds the proposed
technique provides better results in terms of network lifetime as well as energy consumption. The death of
the first node is delayed in the proposed technique; hence the stability period of the network is increased.
Table 3. Results of the proposed protocol
Rounds First Dead Node
Round Number
10% Nodes Dead
Round Number
All Dead at
Round Number
Total Energy
Consumed (in Joules)
5,000 473 525 3,459 31.2892 J
6,000 495 530 4,107 32.9428 J
7,000 481 556 2,993 33.6197 J
The proposed algorithm and MLHEED comparison for the evaluation of the amount of energy
consumed by the network is shown in Table 4, which shows that the consumption of the network energy by
the nodes in the process of sensing the physical parameters, sending the data to the CHs and further from the

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CHs to the BS. The amount of energy required to transmit as well as receive the data packets is considered to
be the same in the proposed model. The proposed protocol uses 20J of the node battery energy which is the
same as MLHEED-1. In addition to this node battery energy, solar energy is consumed viz. 11.2892 J.
However, the total energy consumed is 31.2892 J which is less than MLHEED-4, 5, and 6 protocols by
7.15%, 12.10%, and 14.975% respectively. Thus, paper [5] provided a great benefit in the enhancement of
network lifetime but at an increased energy consumption of the node battery. However, the proposed
technique provides an improved network lifetime at efficient energy consumption.
Table 4. Comparison of energy consumed by the protocols
Protocol Total Network Energy
MLHEED-1 [5] 20.0 J
MLHEED-2 [5] 28.0 J
MLHEED-3 [5] 31.4 J
MLHEED-4 [5] 33.7 J
MLHEED-5 [5] 35.6 J
MLHEED-6 [5] 36.8 J
Proposed Protocol 20 J+11.2892 J=31.2892 J
Next, we change the number of nodes for the proposed protocol and present the observed results in
Table 5, which suggests that the network lifetime is improved as the number of nodes is increased at the cost
of increased network energy. However, it should be noted that around 20% of this increased energy
consumption is a renewable source of energy, and the battery energy consumption is around 30 J (average).
Further, the number of packets sent to the base station by the MLHEED-6 protocol is 27,900 bits per second
while in the proposed protocol is 45,740 bits per second which increase the percentage of the number of data
packets sent to the base station by 39.003%.
Table 5. Results of the proposed protocol for different number of nodes and at 5000 rounds
Nodes First Dead Node
Round Number
10% Nodes Dead
Round Number
All Dead at
Round Number
Alive
Nodes
Total Energy
Consumed (in Joules)
100 473 525 3,459 0 31.2892 J
200 429 525 NIL 1 74.0231 J
300 438 511 NIL 4 120.3301 J
Next, we compare our proposed technique with other protocols like LEACH and its variants [26].
We implemented mod-LEACH, LEACH, E-MODLEACH, and DEEC along with our proposed protocol for
the following simulation parameters area 100×100 m2
, 100 nodes with an initial energy of 0.5 J, and the
simulation are run for 5,000 rounds. The BS is located at (50, 50) and the threshold distance d0 is 70 m.
Table 6 presents the proposed protocol results for the same simulation parameters with other protocols like
mod-LEACH, LEACH, E-MODLEACH, and DEEC. The highlights of the proposed protocol are that it
provides a longer network lifetime and also the number of packets sent to BS, CH is more compared to the
other protocols. The stability period of the proposed protocol is more. Also, at the end of 5,000 rounds, there
are no dead nodes.
Table 6. Results of the proposed protocol comparison with other protocols
Protocols First Node Dead All Dead Packets to BS Packets to CH
mod-LEACH 938 1,612 14,272 108,569
LEACH 990 1,397 12,211 105,433
E-MODLEACH 1,052 1,514 21,020 103,873
DEEC 1,087 1,498 45,677 86,270
Proposed 1,179 0 119,268 143,562
5. CONCLUSION
In this work, the proposed protocol uses the heterogeneity concept by having two different types of
nodes in the network. The normal nodes have 0.2 J initial energy, while the cluster heads with less than 0.01 J
energy would be again energized with renewable energy which adds 0.07 J of energy to the remaining cluster
head energy. The protocol is evaluated based on two important parameters namely the lifetime of the network
and the energy consumption in the network. The proposed protocol provides a longer network lifetime

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compared to all MLHEED techniques. MLHEED-1, MLHEED-2, MLHEED-3, MLHEED-4, MLHEED-5,
and MLHEED-6 remained active for 668; 1,156; 1,626; 2,092, 2,458; and 2,997 rounds respectively, while
the proposed protocol remained active for 3,459 rounds. Thus, the percentage increase in network lifetime
achieved by the proposed protocol with respect to the MLHEED-1, MLHEED-2, MLHEED-3, MLHEED-4,
MLHEED-5, and MLHEED-6 is 80.68%, 66.57%, 52.99%, 39.52%, 28.93%, and 13.35% respectively.
The total energy consumed is 31.2892 J by the proposed protocol which is less than MLHEED-4, 5,
and 6 protocols by 7.15%, 12.10%, and 14.975% respectively. The results show that for a different number of
rounds, the proposed technique provides better results in terms of network lifetime as well as energy
consumption. However, it should be noted that around 20% of this increased energy consumption is a
renewable source of energy, and the battery energy consumption is around 30 J (average). Also, the no. of
data packets transferred to the base station using the proposed protocol is greater than the MLHEED-6
protocol by 39.03%. The proposed protocol provides better energy efficiency and network lifetime when
compared with protocols like mod-LEACH, LEACH, E-MODLEACH, and DEEC. The future scope would
be to implement the protocol in different scenarios like in a dynamic environment. Thus, the proposed
technique improves the network lifetime and provides efficient energy consumption in a wireless sensor
network.
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BIOGRAPHIES OF AUTHORS
Trupti Shripad Tagare received her B.E. degree in electronics and
communication engineering from K.L.E. College of Engineering, Belgaum, in 2009 and her
M.Tech. in digital electronics from Gogte Institute of Technology, VTU, Belgaum, in 2013.
Currently, she is working as an assistant professor at the Department of Electronics and
Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, and
pursuing her doctoral degree in the field of wireless sensor networks from Dayananda Sagar
University. She has worked at K.L.E. College of Engineering, Belgaum as an assistant
professor in Electronics and Communication Engineering Department. Her areas of interest
are wireless sensor networks, computer communication networks, power electronics, signal
processing, analog and digital electronics, analog and digital communications, and other allied
subjects and corresponding labs. She has published and presented papers at many national and
international conferences with book chapters published in Springer Lecture Series. She can be
contacted at truptitagare-ece@dayanandasagar.edu.
Rajashree Narendra completed her bachelor’s degree in electronics and
communication engineering from University of Mysore, India in 1988 and her master’s degree
in electrical engineering in the area of optical fiber communication at University of Alberta,
Edmonton, Canada in 1992. She completed her doctoral program in electronics and
communication engineering in the area of susceptibility of electronics systems and devices by
electrostatic discharge at UVCE, Bangalore University. Her major field of study is integrated
optical devices, 5G wireless communication, wireless sensor networks, electrostatic discharge,
and EMI/EMC. She has 33 years of research, industrial and teaching experience. She worked
as a research engineer at TR Laboratories, Edmonton, Canada, a scientist at CEERI, CSIR
Labs, Pilani, India, and a consulting engineer at FRIS Robotics Sdn. Bhd., Penang, Malaysia.
She is currently working as a professor at the Department of Electronics and Communication,
Dayananda Sagar University, Bangalore. She worked as a professor and Head of Department
of Telecommunication, BNM Institute of Technology, Bangalore. She has worked at BITS,
Pilani, PESIT, Amrita ViswaVidyapeetham and East Point College of Engineering and
Technology, Bangalore. She has published and presented papers in IEEE Transactions,
Photonics Technology Letters, Springer Lecture Series, SPIE, IEEE–LEOS International
Conference, Saratov Fall Meet at Russia, Asia Pacific Symposium on EMC, Beijing, China,
PIERS–KL, Malaysia, IEEE AEMC-Kolkata, and INCEMIC, Bangalore. She can be contacted
at rajashree-ece@dsu.edu.in.

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