Data transmitted encryption for clustering protocol in heterogeneous wireless sensor networks

Indonesian Journal of Electrical Engineering and Computer Science
Vol. 25, No. 1, January 2022, pp. 347~357
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v25.i1.pp347-357  347
Journal homepage: http://ijeecs.iaescore.com
Data transmitted encryption for clustering protocol in
heterogeneous wireless sensor networks
Basim Abood1
, Abeer Naser Faisal1
, Qasim Abduljabbar Hamed2
1
College of Computer Science and Information Technology, University of Sumer, Al-Rifai, Iraq
2
Center of Computer, University of Sumer, Al-Rifai, Iraq
Article Info ABSTRACT
Article history:
Received Jun 17, 2021
Revised Sep 2, 2021
Accepted Nov 1, 2021
In this paper, elliptic curves Diffie Hellman-Rivest Shamir Adleman
algorithm (ECDH-RSA) is a novel encryption method was proposed, which
based on ECDH and RSA algorithm to secure transmitted data in
heterogeneous wireless sensor networks (HWSNs). The proposed encryption
is built under cheesboard clustering routing method (CCRM). The CCRM
used to regulate energy consumption of the nodes. To achieve good
scalability and performance by using limited powerful max-end sensors
besides a large powerful of min-end sensors. ECDH is used for the sharing
of public and private keys because of its ability to provide small key size
high protection. The proposed authentication key is generated by merging it
with the reference number of the node, and distance to its cluster head (CH).
Decreasing the energy intake of CHs, RSA encryption allows CH to compile
the tha data which encrypted with no need to decrypt it. The results of the
simulation show that the approach could maximize the life of the network by
nearly (47%, and 35.7%) compare by secure low-energy adaptive clustering
hierarchy (Sec-LEACH and SL-LEACH) approches respectively.
Keywords:
Cheesboard clustering
Data encryption
Routing protocol
Secure clustering
Wireless sensor networks
This is an open access article under the CC BY-SA license.
Corresponding Author:
Basim Abood
College of Computer Science and Information Technology, University of Sumer
Al-Rifai, Thi-Qar, Iraq
Email: b.abood@uos.edu.iq
1. INTRODUCTION
Information security (IS) is used to prevent unauthorized access to information and perform various
operations on such information, such as the use, disclosure, disabling, destruction or modification of such
information [1]-[3]. IS has many objectives in relation to the protection of information against any risks to
which such information may be exposed. The type of risk to which the data is exposed varies by application
[4]-[6]. However, The proposed security of low-energy adaptive clustering hierarchy (LEACH) protocol
(SLEACH) to construct a secure wireless sensor networks (WSN) clustering model [7]-[9]. It purposes to
avoid sinkholes, forwarding with care, and SLEACH in general, are limited by system memory, resulting in
network efficiency reduction and a shorter lifespan. To overawed the complexity and difficulty of traditional
encryption organizations in WSNs have a limited amount of storage space, the advanced encryption standard
(AES) and elliptic curve cryptography (ECC) algorithms are used in [10] to reduce the complexity and
exploit the advantages of these algorithms. In WSNs, ECC is used to create with sharing the key. To protect
the aggregation with authentication scalable data management, analysis, and visualization (SDAV) is
proposed [11], [12]. The researchers select the ECC over conventional asymmetric algorithms because of its
low key and performance in terms of simulation and capacity. The aggregator gathers in SDAV for its
members' encrypted data, decrypts it, averages it, and then returns the result to them. Secure enhanced data
aggregation (SEDA) based on ECC was used by another secure in [13]. SEDA-ECC is based on the concepts

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Indonesian J Elec Eng & Comp Sci, Vol. 25, No. 1, January 2022: 347-357
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of privacy encryption algorithm for homomorphic technique. This system has great security outcomes,
particularly when it comes to node exploitation attacks. But, The key challenges are the necessary memory
capacity and energy consumption. For the energy cost of communication in WSNs [14], the authors
suggested a cryptography to secure data transmission in WSNs routing architecture elliptic curves Diffie
Hellman algorithm -elliptic curve digital signature algorithm (ECDH-ECDSA) key exchange and verify that
it must be favored in cases where a trusted third party is accessible. Therefore, When it comes to calculating
the cost of cryptographic protocols on sensor nodes, monitoring should be taken into account. In this paper,
we proposed the ECDH-RSA an enhanced encryption algorithm plan based on ECDH and RSA in order to
ensure data transfer security in WSN to overcome these limitations of various articles with dynamically
clustered sensor nodes, The biggest drawbacks are a finite quantity of memory and the possibility of a single
node failure. For compromise communication lines, the attacker can compromise many more nodes.
Furthermore, the decryption algorithm is not suited for encrypting large amounts of data. The goal is to have
the least amount of impact on the network's lifecycle, chessboard clustering routing method (CCRM) and
ECDH is used to produce public and private keys for sensor nodes, and is used to find the most suitable
sensor nodes as cluster heads to relay the message to the base station.
The suggested encryption method is based on CCRM, which employs the chessboard clustering
algorithm (CC) to select the best network structure for lowering energy consumption after each round.
CCRM is written at section 3. The following is how the rest of the paper is structured: The approach of this
paper is clarified in section 2. The structure of the chessboard clustering routing protocol is showed in section 3.
Our proposed solution for securing data clustered sensors in WSN is discussed in section 4. Simulation
experimental findings and contribution are discussed in section 5. A summary finishes section 6 of this work.
2. METHODOLOGY
Figure 1 depicts the stages of our planned project. The first phase entails using CCRM to build a
network topology that reduces energy fatigue. Then, to ensure secure data flow from sensor nodes to the BS,
the proposed encryption schema is implemented. The next sections go over each of these phases in depth.
Figure 1. Developing the secure data transfer technique
3. THE CHESSBOARD CLUSTERING ROUTING PROTOCOL
In this part, the chessboard clustering algorithm is used to suggest heterogeneous sensor networks.
We will employ the following two types of sensors:
− The usage of a restricted number of powerful high-end sensors is referred to as an H-sensor (cluster
head).
− The term "L-sensor" refers to the employment of a variety of low-cost (basic) sensors.
3.1. Cluster deployment
We introduce our heterogeneous wireless sensor networks (HWSNs) checkerboard clustering
approach in this part for heterogeneous sensor networks. In the sensor network, chessboard sensors are
employed. The sensor network is divided into several small, equal-sized cells, as shown in Figure 2, with
adjacent cells colored in various hues (white/black). H-sensors and L-sensors are expected to be distributed

Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 
Data transmitted encryption for clustering protocol in heterogeneous wireless sensor … (Mohd Ali Hassan)
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evenly and randomly in this area. H-sensors, on the other hand, should be installed with greater care to ensure
that all L-sensors are covered. That is, at least one cluster head can be heard by each sensor.
Figure 2. The chessboard clustering scheme
3.2. The partition method for clustering
Cluster partition is a technique for homogeneous networks that has been extensively researched
[15]-[17], and for heterogeneous networks [18], [19]. First, Only the H-sensors in white cells are active
during the initiation period, whereas the H-sensors in black cells are turned off. All of the L-sensors are
working. In white cells, clusters form around H-sensors, and these H-sensors become cluster heads. Later,
when H-sensors in white cells run out of energy, the clusters are formed around the H-sensors in black cells
in the same way. The cluster partition concept will be described in terms of the H-sensors in the white cells.
In turn, broadcast hello messages based on the H-sensors' IDs and their locations, starting with the H-sensor
with the smallest ID. Each L-sensor will then build a list of the H-sensors it has heard from, or whose
messages it has successfully received. The broadcast's transmission range is large enough, based on received
signal strength, for most L-sensors to receive hello messages from multiple H-sensors. The cluster leader is
then chosen by each L-sensor as the H-sensor whose hello message has the best signal strength. After this,
each L-sensor will recognize which H-sensor it belongs to and will favor the H-sensor at the top of the list.
The H-sensor then begins to determine which sensors should be included in its cluster. We just discuss it for
cluster 1 because it is the same for all clusters. H-sensor 1, abbreviated H1, will send a message that says "All
sensors within a reasonable distance of me should report to me as the preferred cluster head". Following
that, each eligible L-sensor will deliver a packet to H1, this contains the ID as well as the location of the ID.
After all, L-sensor has reported, H1 will add them to a list L and broadcast an acknowledgment packet to
them. The sensor in L with the least ID is then asked by H1, say S1, to send a message to sensors asking them
to report to S1 if they: i) H1 is the best cluster head to use.; ii) S1 has conveyed this message to H1; and iii) H1
has not acknowledged S1.
All of these L-sensors will pay attention to S1, and S1 will inform H1 about these L-sensors. H1 will
then ask another sensor in L to add these newly identified sensors to L, say S2, to follow in the footsteps of S1,
and so forth, until there are no more sensors to discover. It is undeniable that, after this, H1 will discover
every sensor that has chosen H1 as their preferred cluster head and has a path to H1.
After H1 has finished, in the same way, H2 can discover its sensors, then H3, H4 until the last H-
sensor. When the last H-senor has completed his work, we may claim that the first round of discovery is
finished. It's worth noting that after the first round, the majority of L-sensors have most likely previously
been detected by the favored H-sensors. However, some L-sensors may have yet to be discovered because
they lack a path to their preferred H-sensor. Such L-sensors are called the orphan sensors. To assist orphan
sensors in locating the H-sensor, a second phase of discovery is required, in which each orphan sensor
broadcasts a message stating that it saying that "Any non-orphan sensor who receives this message is
welcome to add me to their cluster". The first non-orphan sensor to reply will inform its H-sensor of the new
discovery. After this, we may claim that all L-sensors in the white cell have discovered the H-sensors.
As an example, Figure 3 depicts a very basic network, H1 and H2 are the cluster heads, and there are
10 sensors in all. The transmission distance of the cluster heads is DH that is only H1 can be heard by sensors
S1 to S5, while H2 can only be heard by sensors S7 to S10. Both H1 and H2 can be heard by S6, although it is
considered that H1's signal is stronger. A sensor can send a packet to another node if it is capable of doing so,
there is an edge between them. At first, Figure 4 shows how H1 and H2 will broadcast their signals in turn.
Following that, H1 will be the chosen cluster head for S1 to S6, and H2 will be the preferred cluster head for S7
to S10. Next, H1 will look for sensors that can communicate with it directly. Because they are within D of H1,
it will send a message, and S1 and S2 will respond, as shown in Figure 5(a). After this, as demonstrated in
Figure 5(b), S1 will discover S3, S4, and S5. Next, H2 will discover sensors S7 to S10 in a similar way, as shown
in Figure 6(a). S6 is an orphan since it chose H1 as its cluster head of choice. However, it is unable to
communicate with any sensor that has a connection to H1. Thus, S6 will send a message to S7, who will add S6
to the H2 cluster, as shown in Figure 6(b).

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Figure 3. A simple network of cluster partition
Figure 4. The messages of H1 and H2 are aired in turn
Figure 5. Because they are within D of H1, it will send a message: (a) S1 and S2 respond H1's message and
(b) S1 discovers S3, S4, and S5
Figure 6. Described the cenarios to join clusters as: (a) H2 discovers S7 to S1o and (b) S6 joins the cluster of H2

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4. ENCRYPTION ALGORITHMS (ECDH AND RSA)
4.1. Elliptic curves Diffie-Hellman (ECDH)
In a variety of cryptographic contexts, elliptic curves were already in use worked independently on
this project [20]-[23]. At that time, integer factorization and primality proof are two examples. ‘Domain
parameters’ ECC is a good example of a constant like this. Unlike private key cryptography, public key
cryptography does not require the communication parties to disclose a secret, but it is substantially slower.
An elliptic curve can be conceived of as being given by an affine equation of them for the purposes of
encryption:
𝑦2
= 𝑥3
+ 𝑎𝑥 + 𝑏 (1)
Where a and b are elements of a finite field containing p elements, and p is a prime greater than 3.
(The equations for binary and ternary fields differ slightly). For every L-sensor in the network, the initial step
before data transfer between the L-Sensor, ECDH, and a base point p that sits on the curve must be known.
The collection of ordered pairs (𝑥, 𝑦) having coordinates in the field and such that 𝑥 and 𝑦 satisfy the relation
given by the equation describing the curve is the set of points on the curve. A group is also formed by a set of
points on an elliptic curve that have coordinates in a finite field, and the procedure is as follows: to increase
the curve by two points 𝑄1 and 𝑄2 together. Then a straight line is drawn through the curve to find the third
point of intersection 𝑅1. Then point 𝑅1 is reflected along the X-axis to obtain (−𝑅1). That is to say, the total
of 𝑄1 and 𝑄2 results (−𝑅1). This group operation's concept is that the three points 𝑄1, 𝑄2, and 𝑅1 Lie down in
a straight line, and the points that sum up to zero as a result of a function intersecting a curve as shown in
Figure 7 [22].
Figure 7. Group law on an elliptic curve
Because the majority of wireless sensor environments are unsecured and difficult to connect, it's
difficult to reliably exchange keys in them. One of the elliptic curve types that offers service or solves the
difficulty outlined is the Diffie-Hellman key. When two parties exchange keys, but those keys are subjected
to particular processes by the same party after the switch until it becomes a key encryption by that party.
The difficulty of guessing the type of operation and the digits in which the layer of inquiry led to this exit is
the principle of power in the Diffie-Hellman key [22].
Therefore, it’s crucial to get the group operation up and running as efficiently as possible. Many
options have been considered, however how to optimize the L-main sensor's group operation is typically
influenced by the underlying system [20], [22]. That some points on an elliptic curve with affine coordinates,
as defined above, must be represented. Then to add two 𝑄1 = (𝑥1, 𝑦1) and 𝑄2 = (𝑥2, 𝑦2), where 𝑥1 ≠ 𝑥2, it
is necessary to get the slope of the line that passes through them:
𝜆 = (𝑦2 − 𝑦1) (𝑥2 − 𝑥1)
⁄ (2)
This necessitates division in the limited field beneath. Then figure out where the line intersects the
curve for the third time, it is found that (−𝑅1) = (𝑥3, 𝑦3), where:
𝑥3 = 𝜆2
− 𝑥1 − 𝑥2 (3)
for the finite field (𝑃 ≠ 2 𝑜𝑟 3), forming the sum necessitates one division, one squaring, and one
multiplication, when two affine points with different 𝑥 −coordinates are combined, are occasionally utilized.

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Triples of coordinates are used in weighted projective coordinates (𝑥, 𝑦, 𝑧), corresponding to the affine
coordinates (𝑥 𝑧2
⁄ , 𝑦 𝑧3
⁄ ) whenever 𝑧 ≠ 0. Weighted projective coordinates have the advantage of allowing
point addition on an elliptic curve to be done in 16 field multiplications instead of all field divisions [20], [22].
The steps of the ECDH algorithm are as follows:
− Select a number (𝑃) which must be primary and larger than 3.
− Select two numbers (𝑎, 𝑏). Where ((4𝑎3
+ 27𝑏2)𝑚𝑜𝑑 𝑃 ≠ 0).
− Find the set of points (𝐺) on the elliptic curve through this equation 𝑦2
= 𝑥3
+ 𝑎𝑥 + 𝑏 over Z. The
addition rule:
i. 𝑃 + 𝑄 = 𝑄 + 𝑃 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑃 𝜖 𝐸(𝑍𝑃)
ii. 𝑖𝑓 𝑃 = (𝑥, 𝑦)𝜖 𝐸(𝑍𝑃), 𝑡ℎ𝑒𝑛 (𝑥, 𝑦) + (𝑥1, −𝑦) = 𝑄
(𝑥1, −𝑦) is denoted by –P, and is called the negative of P; that –P is indeed a point on the curve.
iii. Let 𝑃 = (𝑥1, 𝑦1) ∈ 𝐸(𝑍𝑃) 𝑎𝑛𝑑 𝑄2 = (𝑥2, 𝑦2) ∈ 𝐸(𝑍𝑃), 𝑤ℎ𝑒𝑟𝑒 𝑃 ≠ −𝑄.
Then 𝑃 + 𝑄 = (𝑥3, 𝑦3), where:
𝑥3 = 𝜆2 − 𝑥1 − 𝑥2 (4)
𝑦3 = 𝜆(𝑥1 − 𝑥3) − 𝑦1 (5)
and 𝜆 = (𝑦2 − 𝑦1) (𝑥2 − 𝑥1)
⁄ 𝑖𝑓 𝑃 ≠ 𝑄 (6)
𝜆 = (3𝑥1
2
+ 𝑎1) 2𝑦1
⁄ 𝑖𝑓 𝑃 = 𝑄 (7)
Then a random point is chooses from set of points (G) from set of points:
− Choice of a large number 𝑛.
− User a key generation:
i. Select privet 𝑛𝐴 with condition 𝑛𝐴 < 𝑛
ii. Calculate public 𝑝𝐴
𝑝𝐴 = 𝑛𝐴 × 𝐺 (8)
− User B key generation:
i. Select privet 𝑛𝐵 with condition 𝑛𝐵 < 𝑛
ii. Calculate public 𝑝𝐵
𝑝𝐵 = 𝑛𝐵 × 𝐺 (9)
− The two sides exchange keys (𝑝𝐴, 𝑝𝐵).
− Calculate of secret key by user A:
𝐾 = 𝑛𝐴 × 𝑝𝐵 (10)
− Calculate of secret key by user B:
𝐾 = 𝑛𝐵 × 𝑝𝐴 (11)
− Convert the packet data to a set of points (𝑃𝑚). And then use the following encryption eq. for 𝑃𝑚:
𝐶𝑚 = {𝑘𝐺, 𝑃𝑚 + 𝑘𝑃𝐵} (12)
− Decryption for 𝐶𝑚, use the following:
𝑃𝑚 + 𝑘𝑃𝐵 − 𝑛𝐵(𝑘𝐺) = 𝑃𝑚 + 𝑘(𝑛𝐵𝐺) − 𝑛𝐵(𝑘𝐺) = 𝑃𝑚 (13)
4.2. RSA algorithm
The original RSA algorithm was publicly illustrated in 1977. This algorithm consists of three stages
namely key generation, the encryption and finally the decoding stage. RSA is one of the cryptographic
algorithms, which are a non-symmetric type and thus need a pair of keys, one of which is used for encryption
and may be non-confidential. The other is the key to decryption, which is private and confidential and
authorized only to decrypt the data sent. This algorithm employs two large prime numbers, p and q.
The strength of this scheme is based on the difficulty of finding these large initial numbers that are
indispensable for finding the secret key while the public key can be freely distributed. The RSA phases and
steps of each phase are as follow [24]:

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Key generation algorithm:
Step 1: Select or generate two large random prime numbers, 𝑝 and 𝑞.
Step 2: Compute 𝑛 = 𝑝 × 𝑞.
Step 3: Compute ∅ = (𝑝 − 1)(𝑞 − 1).
Step 4: Select random integer , 1 < 𝑒 < ∅, such 𝐺𝐶𝐷(𝑒 , ∅) = 1.
Step 5: Compute, where 𝑑 = 𝑒−1
𝑚𝑜𝑑 ∅.
Step 6: Public Key: (𝑒, 𝑛).
Step 7: Private Key: (𝑑).
Encryption process:
Step 1: Suppose entity 𝑅 needs to send message 𝑚 to entity 𝑆. When m: plaintext.
Step 2: Entity 𝑆 should send his public key to entity 𝑅.
Step 3: Entity 𝑅 will encrypt 𝑚 as = 𝑚𝑒
𝑚𝑜𝑑 𝑛 , and will send 𝐶 to entity 𝑆.
Where 𝐶: cipher text.
Decryption process:
Step 1: Entity 𝑆 will decrypt the received message as 𝑚 = 𝑐𝑑
𝑚𝑜𝑑 𝑛.
4.3. Data aggregation in a secure environment
In CCRM-based HWSN, because it receives, processes, and retransmits data. When compared to an
L-Sensor, an H-Sensor requires more energy. This level attempts to reduce the utilization of the H-energy
Sensor by allowing it to collect encrypted data from cluster members without having to decrypt it. As a
result, the attacker will be unable to listen in on data sent between intermediate nodes. As a result, standard
aggregation approaches provide far less privacy. To do that, we use the RSA encryption's addition
characteristic. Which allows us to execute arithmetic operations on ciphertext, as it described at previous part
from this section A.
In this proposed scheme, each L- sensor senses data 𝑚𝑖, and encrypts it with its key 𝑒𝑖
𝑟
as shown in
(14) and sends it to its H-Sensor. Where 𝑟 is the round index in which the node produced the key 𝑒𝑖:
𝑐𝑖 = 𝑚𝑖
𝑒𝑖
𝑟
𝑚𝑜𝑑 𝑛 (14)
the H-Sensor collects 𝑛 messages after receiving sensed data and aggregates them by simply adding them up.
as shown in (15):
𝑐 = ∑ 𝑐𝑖
|𝑁|
𝑖=1 = ∑ 𝑚𝑖
𝑒𝑖
𝑟
|𝑁|
𝑖=1 𝑚𝑜𝑑 𝑛 (15)
where |𝑁|is the count of L-sensors in the cluster. After aggregating the data, the final step is to send it to the
BS. In order to organize the data that has been aggregated, at the end of the message. H-Sensor will attach all
node indexes. Thus, the final version of the sent ciphertext CT to BS in terms of total size (𝑁 ∗ 176 + 𝑁 ∗
13) 𝑏𝑖𝑡𝑠.
5. SIMULATION PERFORMANCE RESULTS
The system throughput was used to assess the system's performance, energy consumption and the
total data rate for sensor nodes rounds [25]. In this section will be describerd the simulation paremeters by
matlab and implantation these parameters in second part from this section. Simulation Result to compute the
System Performane to get result better than other methods which compared with proposed method.
5.1. Simulation analysis setup
MATLAB R2018a is used to run the simulations. For our suggested technique, 200 L-sensors and
10 H-sensors are randomly deployed in a topographical dimensional for region (100 m x 100 m). Under the
chessboard clustering concept H-sensors used the cluster technique, whereas L-sensors were spread around
them. On the other hand, for heterogeneous sensor networks the costs of an H-sensor and an L-sensor vary
depending on the type of sensor. The manufacturer, other factors, and this is outside the scope of this paper.
The simulation runs for 1000 transmission packets (rounds). A single base station gathers data from nodes all
throughout town (90 m and 90 m). The 20 and 80 meters of detected transmission, respectively, the starting
energy of all L-sensors and H-sensors is 0.5 and 2.5 J, respectively. All sensors are stationary and their
locations are known, if adequate energy is available each sensor can communicate directly with the base
station. The first radio model is used to implement the methods, it is commonly used in WSNs for evaluating
routing protocols [10]. The network simulation parameters are detailed in Table 1. In addition, while
constructing the network structure with CC, the nodes are randomly positioned in the field, and the field

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center is positioned at a random distance from the base station. To assess the network's security and
efficiency, comparison studies are carried out using several state-of-the-art technologies
Table 1. Network simulation parameters
Paremeters Value
Area of Sensor field (meters) (100 × 100 𝑚)
Sink location (meters) (90 × 90 m)
Idle State energy 50
𝑛𝐽
𝑏𝑖𝑡
⁄
Data aggregation energy 5
𝑛𝐽
𝑏𝑖𝑡
⁄
Amplification energy 𝑑 ≥ 𝑑0 10 𝑝𝐽 𝑏𝑖𝑡 𝑚2
⁄
⁄
H- sensor to base station 𝑑 < 𝑑0 0.0013 𝑝𝐽 𝑏𝑖𝑡 𝑚2
⁄
⁄
Amplification energy 𝑑 ≥ 𝑑1 𝐸𝑓𝑠 10 = 𝐸𝑓𝑠1
⁄
L-Sensor to H-Sensor 𝐸𝑚𝑝 10 = 𝐸𝑚𝑝1
⁄
5.2. Simulation results
In this section, the ECDH-RSA method under CCRM, the mentioned algorithms ECDH and RSA
which described at (section 4.1 and 4.2) are used to encrypt the transmitted data through that network. In this
section, the simulation scenarios are really specific to show the effect of encryption operation on the energy
consumed of the network sensors under the performance of cheeseboard clustering, balancing energy
consumption by comparing with three methods (Sec-LEACH [26] and SL-LEACH [7], and our proposed).
Figure 8 depicts the proposed method's flowchart.
Figure 8. Flowchart for proposed method

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Figure 9 depicts the proposed approach as can be observed, outperforms ECDH-RSA in this area.
The proposed strategy extended the network lifetime by almost (47% and 35.7%) compared to the (Sec-
LEACH, and SL-LEACH) security approaches, respectively. Furthermore, as shown in Figure 9, the
suggested method's number of living nodes is always greater than both Sec-LEACH and SL-LEACH. Table 2
displays the various time intervals related to the first dead node as determined by the three different
approaches. Clearly, the time it takes for the first node to die in the suggested technique is much longer than
in Sec-LEACH and SL-LEACH.
Table 2. Number of rounds to extend the network lifetime by compute first dead node for different
approaches
Approaches Sec-LEACH Sl-LEACH Proposed
Lifetime of the first dead node (Rounds) 682 917 1439
For the three techniques, Figure 10 shows the total energy consumed by a WSN as a function of
transmission rounds. Because it uses less power and has the longest network lifetime, the suggested method
outperforms two other ways (Sec-LEACH and SL-LEACH) when the round number in the region grows.
This suggests that the proposed strategy achieves a better energy balance in a WSN. The Figures 10-12
shows the energy usage in relation to data rate, simulation rounds, and the number of sensors, respectively.
When compared to traditional cheeseboard clustering, the energy consumption during encryption is lower.
Table 3 shows that the suggested method beats existing alternatives in terms of energy usage, data rate, and
sensor node highest path. When compared to existing ways, we see that the proposed method uses less
energy. As a result of the increased power consumption, other nodes were subjected to increased load,
reducing the network life node over time. This resulted in lower power usage and a longer network life. In an
ideal world, all nodes should have the same amount of leftover energy.
Figure 9. Lifetime simulation of alive node for
different different three approaches (Sec-LEACH,
SL-LEACH, and proposed method)
Figure 10. Total energy consumed with respect to
data rate for different three approaches (Sec-LEACH,
SL-LEACH, and proposed method)
Figure 11. Network energy consumption for
different three approaches (S-LEACH, sec-LEACH,
and proposed method)
Figure 12. Total energy consumed with respect to
number of sensors for different three approaches
(Sec- LEACH, SL-LEACH, and proposed method)

 ISSN: 2502-4752
356
Table 3. Energy consumption for three approaches (Sec-LEACH, SL-LEACH, and proposed method)
Method Data Rate Simulation Rounds Sensor Node
Sec-Leach 13.9 % 25.025 % 14.115 %
SL-Leach 17 % 23.884 % 16.926 %
Proposed Method 23 % 18.706 % 20.742 %
6. CONCLUSION
Cheeseboard clustering wireless sensor network has an advantage of choosing the proper path for
transmitting the data from the sensors to the base station. The power consumption of encryption during the
encryption operation is increased as a tax to make the data transmitted over the network secure. Despite
significant advances in secure WSN clustering. In this paper, to secure data transmission in HWSNs with
dynamic clustering, we present a unique encryption schema based on ECDH and RSA encryption. The
cheeseboard clustering algorithm is used to find the most suitable sensor nodes as H-sensors to relay
messages to the base station, with the purpose of maximizing the network's lifetime. Then as a result, even if
the H-sensor is compromised, the attacker will not be able to see anything because the H-sensor is not
responsible for encrypting signals. In comparison to other ways, the provided results show that this strategy
enhances network performance in terms of energy usage significantly.
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BIOGRAPHIES OF AUTHORS
Basim Abood was born in Thi-Qar City, Iraq, in 1984. He received the B.Sc.
degree from University of Basra (UoB), Basra, Iraq, in Electrical Engineering, in 2007. He
received his M.Sc. and Ph.D degrees from Huazhong University of Science and Technology
(HUST), China, in 2013 and 2016 respectively, in Telecommunication and Information
Engineering. Currently he is Assistance Proof, working Head of Computer Science
Department, College of Computer Science and Information Technology, university of Sumer,
Iraq. His research interests include digital communication, wireless sensor networks, mobile
and Ad-hoc network (MANET), network security, artificial intelligence, and LTE- A cellular
network. He can be Contacted at email: bas.eng1984@gmail.com, b.abood@uos.edu.iq.
Abeer Naser Faisal received the B.Sc. degree in computer science from the
University of Thi-Qar, Iraq, the M.Sc. degree in computer science from the University of
Basrah, Iraq. She is currently a director in the Department of Computer Information Systems,
University of Sumer. She has supervised more than 10 graduate projects. She has authored or
coauthored more than 15 publications, with 2 H-index and more than 10 citations. Her
research interests include image processing, biometrics, and pattern recognition and machine
learning. She can be contacted at email: a.nasir@uos.edu.iq, abeernaser13@gmail.com.
Qasim Abduljabbar Hamed was born in Thi-Qar City, Iraq, He received the
B.Sc. degree from University of Baghdad, Baghdad, Iraq, in Information Technology, in 2010.
He received his M.Sc in information technologyin 2015 Russia Workplace. He is Currently
Working as manager for Computer Center in University of Sumer-Iraq. His research interests
include digital communication, wireless sensor networks, mobile and Ad-hoc network
(MANET), network security, artificial intelligence, and LTE-A cellular network. He can be
contacted at email: qalrikabi@gmail.com, q.alrikabi@uos.edu.iq.

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