1. Sustainable Energy Technologies and Assessments 52 (2022) 102184
Available online 28 March 2022
2213-1388/© 2022 Elsevier Ltd. All rights reserved.
Data security tolerance and portable based energy-efficient framework in
sensor networks for smart grid environments
Venu Madhav Kuthadi a,*
, Rajalakshmi Selvaraj a
, S. Baskar b
, P. Mohamed Shakeel c
a
Department of CS & IS, Botswana International University of Science and Technology (BIUST), Botswana
b
Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India
c
Faculty of Information and Communication Technology, UniversitiTeknikal Malaysia Melaka, Malaysia
A R T I C L E I N F O
Keywords:
Wireless sensor networks
Energy
Security
Attacks
Smart grid
A B S T R A C T
Wireless Sensor Networks (WSNs) are effective devices used for remote surveillance, device failure prediction,
and housing energy control in numerous smart grid implementations. Several interaction structures and rem­
edies, such as broadband networks, cable networks, Wireless Sensor networks, have been suggested to assist
Smart Grid implementations. Owing to their cheap, dynamic nature, robustness, and low energy profile, WSNs
are attractive devices, and preserving a low energy pattern is an essential factor in WSN. Implementing quality
services and safety techniques in sensor networks is challenging in smart grid applications. Thus, in this article,
Portable and Data Security Tolerancebased Energy-Efficient Framework(PDST-EEF) has been proposed for
maintaining a high standard of data security by lowering the sensor device energy usage in smart grid sur­
roundings. PDST model is developed to ensure data privacy in sensor networks by utilizing an authentication
method integrated with the cryptographic signature model to detect the various attacks. PDST identifies and
separates attacks like denial of service and replay efficiently. EEF presents a low-power cyber safety mechanism
on sensor networks with smart grid tracking applications. EEF is modeled with different stages like identifying
anti-nodes, group development, and allocating keys less energy. EEF can operate with higher power efficiency
techniques while preserving sustained throughput and reliability ideals. The experimental result shows that the
PDST-EEF’s specific request and authentication period is often enhanced by just a second with less energy usage
of 5.06%.
Introduction to sensor network in a smart grid environment
The smart grid atmosphere is identified by secure and robust data
interaction with the influence of power grid resources and energy gen­
eration devices, transmission devices, distribution networks, and in­
vestors [1]. The smart grid idea has been presented to remodel the
current grid using dependable, actual, and stable two-way interactions,
detecting and monitoring various grid elements [2]. These smart grid
resources are widely scattered and dispersed across a broad geograph­
ical region [3]. The distributed evolution of such resources accordingly
requires stratified communications networks, in which the benefits of
each structure are integrated into the assistance of particular devices
[4]. Denial-of-service attacks on smart grids decrease in frequency in
this group by sensor modules classified by Denial of Service and replay
attacks.
The conventional grid can supply or distribute electricity, but the
smart grid can store, interact, and resolve [5]. The dependability of the
smart grid is focused on the truthfulness of the communications net­
works linking the electricity grid resources [6]. Even then, the widely
dispersed existence of the smart grid interaction structures enhances the
difficulty of security techniques [7].
Furthermore, grid providers, policy creators, and authorities of data
security in smart grid connectivity are essential [8]. The broad sense of
cyber-attack scenarios on smart grid networks influences the systems’
accessibility and capacity to operate the data in the network [9]. When
numerous smart grid implementations require, accessibility guarantees
reasonable access to all information and network assets [10]. In this
group of smart grid threats, denial-of-service threats drop in data
transmission [11].
Wireless Sensor Networks have recently been used to implement
several smart grid devices, including remote automated systems, phasor
measuring devices, home appliances [12]. Due to its several specific
* Corresponding author.
E-mail address: venumadhav.kuthadi@yahoo.com (V. Madhav Kuthadi).
Contents lists available at ScienceDirect
Sustainable Energy Technologies and Assessments
journal homepage: www.elsevier.com/locate/seta
https://doi.org/10.1016/j.seta.2022.102184
Received 12 January 2022; Received in revised form 25 February 2022; Accepted 12 March 2022

2. Sustainable Energy Technologies and Assessments 52 (2022) 102184
2
characteristics, such as low cost, low power usage, high efficiency,
failure sensitivity, and broad visibility of areas, WSNs have become
increasingly popular [13]. Despite these, WSNs may be prone to attacks
even though their restricted assets are challenging to enforce advanced
cyber methodologies in data transmission [14]. In preserving the sensor
nodes safe and secure, they must be located in difficult locations to ac­
cess. The sensor device can increase security in smart grid environments
while consuming less energy.
WSN threats are susceptible to numerous cyber threats, including
Denial of Service (DoS), malware detection, and bugging [15]. DoS
threats are one of the most powerful threats on WSNs [16]. A DoS and
replay attack is a particular type that prevents sensor modules from
waking up to drain their battery packs [17]. An attacker may start DoS
and replay attacks on the sensor network to dismiss specific nodes, cause
issues, and limit data transmission [18]. An offender can incorporate
fake data into the network [19]. An instance is a tapping attack, which
can cause service denial and request reaction system impairment [20].
Researchers engage in some form of communication; it can be normal or
stratified communication. However, normal communication is a
different matter altogether when it comes to distributing messages. This
technical term in stratified communications describes the act of mass
communication. Things like radio transmissions and other forms of
technology fall under this category.
DoS and replay attacks can indeed be categorized as a denial-of-
service threat section, and these threats can have severe consequences
on smart grid networks [7]. PDST-EEF focuses on DoSand replaying
attacks against sensor networks because of its criticism of specific smart
power grids. Security processes in sensor networks usually react to at­
tacks on various levels, such as physical threats, network layers, and
distribution layers [7]. However, it was not regarded that the nodes
would be isolated from the good attacks by a technique to sustain low
energy usage. Consequently, cybersecurity methods for energy-efficient
sensor networkscan handle different attacks such as DoS and replaying.
Sensor networks have restricted energy usage and are frequently situ­
ated in locations that are difficult to achieve such that the sensor nodes
must be securely maintained and monitored. The sensor device can
enhance security levels with less energy usage in the smart grid sur­
roundings. The contribution of PDST-EEF is described below.
• PDST is developed to guarantee data privacy by using a cryptogra­
pher’s signature concept authentication method to detect different
attacks.
• EEF is modelled on various phases, such as identifying anti-nodes,
group development, and allocating the key with less energy-use
separation. EEF can operate with higher technology of efficiency
while maintaining long-term latency.
• Experimental result shows PDST-EEF are often improved with the
authentication period and less energy consumption.
The remaining manuscript is organized as follows: Section 2 com­
prises various background studies related to security maintenance by the
sensor device in smart grid applications. Section 3 Elaborates the pro­
posed PDST-EEF to control the threats in data transmission by lowering
the sensor device energy usage in smart grid environments. Section 4
constitutes the results that validate the authentication stage with less
energy usage. Finally, the conclusion with future perspectives is dis­
cussed in section 5.
Background study on security maintenance in smart grid
application
This section discusses the researchers’ several works; Yuemin Ding
et al. [21] developed a Constrained Broadcast Scheme with Minimized
Latency (CBS-ML). CBS-ML is presented with minimized latency by a
restricted broadcasting program. CBS-ML limits the Transmission to a
limited set of core nodes to prevent traffic problems. Theoretical
approaches address a selection of core nodes related to network
configuration and connect durability. Sensor nodes for PD tracking are
integrated according to the three-dimensional features of the devices for
the target rate assumption measures. The Standing Guard Protocol fo­
cuses on this sensor collaboration system, which aims to verify its impact
on the premise of target identification rate on energy savings and time
reduction.
SungJin Yu et al. [22] introduced Privacy-Preserving Lightweight
Authentication Protocol (PPLAP). The proposed PPLAP resists numerous
attacks and guarantees reciprocal confidentiality and encryption. The
safety characteristics of the proposed system are assessed utilizing an
unofficial safety assessment and demonstrated the key management
workout of the proposed system. Consequently, the proposed scheme
delivers better security and energy efficiency than current connected
guidelines and suits realistic Smart Grid environments.
Uthman Baroudi et al. [23] proposed a genetic algorithm for Ticket-
based QoS routing (GA-TBR). In this work, GA-TBR is used in the
reference sensor node for data collection within the smart grid’s WSN
atmosphere and thus optimizes the choice of paths to guarantee the
necessary Quality of Service. The simulation results showed very few
tickets. The algorithm suggested can choose routes at a small lag po­
tential and demonstrates an enhancement of 28 percent contrasted with
the ad hoc protocol.
Yuchen Jia et al. [24] discussed the Wireless sensor network moni­
toring algorithm (WSNMA). WSNMA constructs a high-voltage (HV)
partial discharge (PD) monitoring system. The integration plan of sensor
nodes for PD tracking is addressed as per the three-dimensional features
of the devices. The proposal is for a system of sensor collaboration
focused on the Standing Guard Protocol to verify its energy-saving and
time-reducing impact on the premise of the target identification rate. It
uses wireless link properties to decompose the natural SNR sequence
into a time series and a stochastic series. This algorithm has SNR period
limits that are forecast, and comparison experiments are used to eval­
uate the preview results’ authenticity and applicability before they can
be used in actual experiments.
Prakash Pawar et al. [25] narrated a Smart Energy Management
System (SEMS). The SEMS is designed to replace, according to the user’s
tendency, the scenario of a whole power failure of a province with a
limited load discharge. Experimental work is demonstrated in supply
reaction considering the highest requirement threshold restriction in
separate situations and changes in priority for the device.
Fei Fan et al. [26] developed Dynamic Barrier Coverage (DBC). DBC
is merged with robot evaluation and a wireless sensor network (WSN). A
reduced, energy-saving and flexible smart grid-centered sensing system
focused on the mobile wireless sensor network would be developed in
the DBC proposed. Moreover, the dynamic barrier coverage method for
the actual smart grid surveillance scene considers the equilibrium
among network quality and financial costs.
Xue Xue et al. [27] introduced a Random-Vector-Functional-Link-
based Link Quality Prediction (RVFL-LQP). The algorithm selects a
qualitative connection measure for a signal-to-noise (SNR) ratio. It
breaks the natural SNR sequence down into a time series and the sto­
chastic series based on wireless link properties. Finally, the probability-
guaranteed SNR period limit is forecasted, and comparison experiments
assess the preview results’ authenticity and workability, respectively.
Melike Yigit et al. [28] proposed A new efficient error control al­
gorithm (EECA). EECA compares two forward error controls (FCE)
coding technologies like Bose-Chaudhuri-Hochquenghem code (BCH)
and Reed Solomon coding (RS) with various modulation methods, which
include Frequency Shift Keying, Offset Quadrature Phase Shift (OQPSK).
In addition, EECA compares itself in various smart grid environments to
RS static or FEC-free techniques centered on performance measures such
as throughput, Bit Error Rate, or delay.
To overcome all the issues in the smart grid environment in the form
of security measures, PDST-EEF has been developed to maintain the
highest security level by reducing the energy usage for sensor devices in
V. Madhav Kuthadi et al.

3. Sustainable Energy Technologies and Assessments 52 (2022) 102184
3
the smart grid atmosphere. PDST is implemented to maintain the secu­
rity level by identifying the different types of attacks in the network. EEF
is designed for high power efficiency to preserve the latency.
Portable and data security tolerance based energy-efficient
framework
PDST-EEF is developed to preserve a high standard of safety by
decreasing the energy usage of sensors in the smart grid environment.
The PDST model provides confidentiality in the sensor network through
a cryptographic signature model by implementing an authentication
method for identifying attacks. PDST can efficiently identify and sepa­
rate the various types of attacks, such as service denial. PDST ensures the
data transmission to the base station and the attacks in a sensor network.
PDST has an authentication method integrated with the cryptographic
signature method. The service providers and the control centers monitor
all the attacks like DoS and replay. The smart grid environment includes
the smart home, thermal power plant, factories, wind generator, as
illustrated in Fig. 1. EEF helps to identify antinode, group development,
and allocation of keys; these three stages use less energy in a smart grid
environment and identify threats during the time of data transmission.
PDST model
Authentication method
The authentication method is a reduced subscription system and
works for devices with limited resources. The whole system always
understands standardized participants, and attackers may even be
acknowledged. Group development and key allocation are three stages
of EEF that use less energy in a smart grid environment even while
Fig. 1. The architecture of PDST-EEF.
Fig. 2. The security maintenance by authentication and cryptographic method.
V. Madhav Kuthadi et al.

4. Sustainable Energy Technologies and Assessments 52 (2022) 102184
4
identifying threats during data transmission. The three stages are iden­
tifying antinode, group development, and allocation of keys. The system
would be known as the false positive likelihood and variable for
measuring the safety level in the smart grid environment. The authen­
tication method involves encryption, decryption, and the authenticated
key generation stage. Select the variables l with two larger primary
digits a, b where |a|=|b|, and calculate δ = (a − 1, b − 1) based on the
safety variables l. Set a feature k(V) = V− 1
m ; m = ab. The alternator is
selected h ∈ A*
m2
, calculate δ(k(hδ
‖m2
)− 1
. The generation of the public
key is p = (m, h), and the secret key is s = (δ, α). Encryption is imple­
mented when the text is sent by n ∈ Am, and e ∈ A+
m can be measured.
The text of a particular count can be calculated as D = C(A) =
hn
.em
‖m2
. The decryption is provided with the encoded data with a
secret key E(d) = k
(
αδ‖m2
)
.δ‖m is used to reconstruct the original signal.
The authentication stage is implemented by the false-positive chance,
and it is shown below.
⎧
⎪
⎨
⎪
⎩
D = C(A) = hn
.em
‖m2
E(d) = k
(
αδ‖m2
)
.δ‖m
positivelikelihood = (J − a− lM/n
)l
(1)
The implementation of the authentication stage is obtained from
Equation (1), J represent the number of changes in the safety level, a
denote the false positive likelihood function, M is the size described for
the amount of authenticated users during startup phases n. l denote the
number of devices. Devices with limited resources can use this authen­
tication method because it uses a reduced subscription system. The
system understands standardized participants based on equation (1),
and even attackers are acknowledged. False-positive likelihood and
safety level in the smart grid environment ensures system’s metric by
encryption, decryption, and the generation of an authenticated key are
all part of the authentication process.D denote the decryption, E(d)
represent the encryption stage, C(A) denote the text content.hn
denote
the number of key functions, m2
is the number of variables, em
represent
the encoded data, k
(
αδ‖m2
)
denote the secret key in encryption. The
participants represent the number of smart grid application users in
which data security must be ensured. The smart gid environment should
ensure the security level to overcome the DoS and replay attacks by the
authentication and cryptographic method, as shown in Fig. 2.
The sender and the intended receiver of a message can see the
message’s contents encrypted with cryptographic techniques. As the
Greek word Kryptos implies, it refers to something hidden. Individuals
can decrypt and decode the text if the message is intercepted. The
authentication method assists a highly complicated signature method
filter arriving signals from theDoS attack in the smart grid environment.
The cryptography is the benefit of one such system, and the recipient end
delay can be regulated with the binomial threshold value with each
riddle stiffness as described below.
mG,k =
a{1 − [
(1− 10− 10
)
k2
high
(
k −
(
khigh + 1
)
)2
]}
a{1 − (1
2
)k
}
(2)
The recipient end delay mG,k can be regulated from Equation (2), the
threshold value restricts the series of cryptographic iterations focused on
a predefined authentication stage represented in the significance of the
challenge k2
high. a denote the false positive likelihood function, k repre­
sents the threshold value. The largest amount of cryptographic iterations
in the polynomial limit feature for each riddle creation is shown below.
mG =
a{1 −
[
(1− 10− 10
)
k2
high
(
l − khigh + 1
)
)2
]}
a{1 − (1
2
)l
}
(3)
The polynomial limit feature mG for each iteration are obtained from
Equation (3), khigh is the beginning force with the threshold value. If the
amount of cryptographic iterations is above the target value and would
be reduced by a lower limit. If the outcome of riddle hardness k is larger
than one, the riddle answer is discovered. The final stage is the pro­
duction of tags. This tag would then alter the mechanical properties of
the indicator and riddles. The label is k -bits in a particular distance. The
entire process to ensure security in the authentication method is
described below in the developmental security algorithm.
The developmental security algorithm.
Signature ← value_Polynomial (G).
If iter < limit_value(k)then.
k ← k + 1.
Repeat (Iter).
End.
Crypt a ← authentication_.mG,k(G‖khigh)
Iter = iter-1.
End.
If k ∕
= 0, then.
1_value.
Else.
A.←a + 1
End.
Algorithm 1 described above is used to determine the appropriate level
of security. The polynomial security algorithm shows the steps of the
authentication method. Authentication is the first step in the develop­
ment of a security algorithm. The next step is to create a riddle that
meets the iterations (iter), and the actual quality of the riddle is khigh. The
label is k -bits in a particular distance.
The PDST model for WSN is useful in identifying the DoS and replay
attacks and depends without a sorting procedure on a principal signa­
ture method.PDST uses a source delay-controlled riddle system to ensure
the privacy of the signal conveyed with cryptography. The framework
for subscription relation illustrates the interaction among the sensor
network, user, investigator. The investigator must log in to their
decryption key and access code with developers that wish to connect the
WSN. The first step in authentication is to identify the user, and the
latter is to authenticate the user at the central level. A real user’s in­
dividuality is supplied in the id number and verification during the first
step of the authentication system. However, this will not mean that the
visitor has been authenticated even though the first step has been suc­
cessful. Provided l variables to select two basic values ar = br = l and
develops the key for the symmetric key cryptographic protocol oper­
ating op(l) with the digital signature. The digital signature with the
public key p : (M = arbr, h) and the secret key s : (δ,α). The investigator
describes the functionality in the form of four stages like G : (1, 0)+
→A+
A ,
G1 : (1, 0)+
→H, G2 : (1, 0)+
→A+
A , G3 : (1, 0)+
. The client V’
j r with the
identification stage IdentityVj
. IdentityVj
∈ (1, 0)+
, client calculation V’
j r
private identification Ridentityj
= G1(Identityj). The authenticated data is
stored by a structure-like box over which the cryptographic key has been
chosen, unavoidable.The network’s participants are observed and
investigated to guarantee that security measurements are taken. The key
sequence provides the security measurement using equation (4).The
cryptographic finding is a peculiar situation, which symbolizes the exact
location in the authentication method, as explained below.
⎧
⎪
⎪
⎨
⎪
⎪
⎩
Ridentityj
= G1
(
IdentityVj
)
V’
j r = (1, 0)+
G : A→J × K + (D*mG) + 1
(4)
The cryptographic finding is obtained from Equation (4), the box
count is denoted as A, the structure indicator K, and the signature J. G is
the combination of username and digital signature at WSN. The inves­
tigator must assess the actual valuation D with the reasonable false-
positive possibility mG and fingerprint thickness of the amount of box
V. Madhav Kuthadi et al.

5. Sustainable Energy Technologies and Assessments 52 (2022) 102184
5
count.IdentityVj indicate the identification stage. V’
j r represent the cli­
ent’s identification stage, Ridentityj
denote the complete stage of identifi­
cation. The interaction among the participants in the network is
monitored and investigated to ensure security measurement. The secu­
rity measurement is ensured by the sequence of keys, as shown in Fig. 3.
Depending on the number of authorized participants, the data
capability has to be initiated. The cryptographic finding inhibits higher
inventory processing, particularly in sensor nodes.The authentication
method and cryptographic signature method distinguish the key
sequenceaccording to the box’s capability used for encryption. To the
receiver, a cryptographic signature serves as proof that the document is
being approved by the person claiming to use it. An authentication
method guarantees that the sender cannot later claim that they never
sent the message.The number of boxes is reduced if the predicted
number of verified participants in each box complies with the total
number of sequences. The number of boxes is finalized when the first
comment is not met. The encryption stage for ensuring the security level
is displayed below.
A =
(
M
δ.D
)
+ [1 −
M
(
M
δ.D
)
.D
+ 1 −
M
(
M
δ.D
)
.D
] (5)
The encryption stage for ensuring security level is obtained from
Equation (5). The outcome is the number of exact keys A used for
encryption, where it can be utilized to measure the ordinary quantity of
pieces in the sequence δ, the median amount of material M. The same
could be used to evaluate the ability of each box by utilizing such var­
iables,and the storing capacity (SC) is shown below.
{
Aability =
[
D × δordinary
]
SC = [A × (D2A) + K +
(
Bcap × (box + 1)
)] (6)
The ability of the box Aability by utilizing variables and the storing
capacity Bcap can be utilized from Equation (6) for measuring the ordi­
nary quantity of pieces δordinary. The cryptographic discovery involves
two keys to encrypt and decrypt data in these applications, which are
called public-key encryption (PKI). Encryption authentication uses
digital identifiers and exceptional codes for each party to stop these
attacks. An official authenticates signature and secret.The element
length is denoted as D and the distance of the signature (box) S. The
combined stocking needed to develop the encrypted key is determined
by the box capability, biometric data amount, chain primary keys, and
the number of keys used.
The interaction between users and investigators with WSN can begin
when the sensor nodes accept the stages for interaction in actual envi­
ronments. The smart grid application describes the sender’s and re­
cipient’s procedures. The cryptographic signature method has been
selected for the duration of encrypted code to ensure security re­
quirements. The cryptographic signature method utilizes the possibility
Fig. 3. Security measurement stages.
Fig. 4. The cryptographic and signature method.
V. Madhav Kuthadi et al.

6. Sustainable Energy Technologies and Assessments 52 (2022) 102184
6
option with the median amount of material denoted as {M1, M2…..Ml}
and the element length {D1, D2…..Dl} as shown in Fig. 4. The storage
capacity box has an encrypted code, single-way key, event key with the
data series.
The procedure continues with the key management of the challenge.
A single-way key string uses the random number as the primary algo­
rithm and generates the event key. The key channels are described
below.
∏
Ma− 1
j=1
Gj
(a)
∏
a; a ∈ unspecified (7)
The channels for each key in the cryptographic method is obtained
from Equation (7), Where Ma− 1 is predicted and Gj
(a) implies the secret
key that would be repeated for different values. Ma− 1 is the significance
of the most recent version of the main chain creation with the unspec­
ified values. PDST model attains privacy in sensor networks, and the
various types of attacks are detected by the authentication method in­
tegrated with the cryptographic signature model.
Energy-Efficient framework
With smart grid tracking applications, EEF gains low power cyber
safety on WSN. The three phases are used in EEF with less energy con­
sumption and preserve sustained latency.
Identifying antinode
In an attempt to improve cybersecurity toward DoS and replay at­
tacks, the network must distinguish among legitimate users and hidden
nodes just before creation. All anit-node can be removed from the WSN
network and implemented in smart grid applications. Therefore, the
anti-nodes could be removed from the layout of the network in smart
grid implementation. Every client Vj gathers information (b1, b2, ⋯⋯bm)
by antinode separation (ANi) at a particular time s ∈ S and execute steps
to produce V’
j summary. V’
j determines the sequence number G(s). The
randomly generated ej ∈ A*
m is selected, and the encrypted data calcu­
lated after the authentication to identify the antinodeis shown below.
Dj = (h’
)bj
.G(s)H(aj).Ki(0)
.SM
j ‖M2
= hk.bj
.G(s)h(aj).kj(0)
.SM
j ‖M2
(8)
The authentication stage to identify the antinode is obtained from
Equation (8), G(s)H(aj).Ki(0)
denote the encryption stage, G(s)h(aj).kj(0)
represent the decryption stage, (h’
)bj
represent the number of antinodes,
SM
j separation of antinode, M denotes the number of users.
Group development
Group Development is often used to choose pattern heads and an
entry point node for intra-group and inter-group interaction and divide
the sensor network into different groups. A node with the highest
number of neighbors is a potential candidate for node formation and
cluster heading. Sensor nodes are coordinated into groups by allocating
a group identification with the necessary data. All sensor nodes notify
the data of their neighbors to choose the new cluster head.The control
center sets the grouping limit as per current energy usage and the pos­
sibility of damage in the sensor node. The proportion r ∈ Am is selected,
r +r0= 0‖M is calculated, and the number r ∈ Am with l(l ≥ m) is for­
warded to the destination gateway. Then, the secret variable would be
partitioned into m parts with more respondents H(a). Control center
delivers {H
(
aj
)
, ki(0)} to Vj with the safety platform. The respondent’s
data delivery rate is shown below.
H(a) = r0 + i1a + i2a + ⋯.. + il− 1al− 1
Ki(a) = Ul
j=1,i∕
=1
a − aj
ai − aj
⎫
⎪
⎬
⎪
⎭
(9)
The respondent’s data delivery rate is obtained from Equation (9),
here i1a +i2a +⋯.. +il− 1al− 1
represent the number of the sensor node,
Ki(a) denote the control center delivery rate, ai, aj represent the number
of secret variables chosen, r0 represent the proportion.
Allocation of keys
Multiple secret keys would be circulated and encoded by a pre-
distributed key in allocating keys. In allocating keys, the initial step is
the group key provided among the group head, and the next step is a
portal that is exchanged across two groups to ensure safe communica­
tion. After the above-described antinode detection stage, allocating keys
eliminates the unused anti-nodes already introduced in the network.
Allocation of the key is necessary to improve the safety of interaction
between groups.
Cyber safety on sensor network
The real-time data users gather energy usage for every step, deliver
queries to consumers, and obtain an exact energy usage pattern.The
Control Centre generates an arbitrary amount k∊Mn to the first time and
calculates h’
= hk
. The information-gathering application is then sent to
the client Vj ∈ V and sent to the gateway. The client Vj ∈ V would then
pick an irregular quantity bj ∈ Mn and then deliver the data packets to
predict the secret identification values Rb
identity. The pre-generated key
encrypts the query Vk
j for Transmission of data among the client and
gateway. The data transmission between the client, gateway and the
gateway and client is shown below.
clientj→gatewayi : {Rb
identityj
‖Ridentityj
}Vj
gatewayi→clientj: {Rb
identityj
‖Ridentity‖Ra
identityi
⎫
⎬
⎭
(10)
The smart grid improves conversation, technology, and intercon­
nection of power network components for users can interact. Data
Fig. 5. The data transmission between the client and the gateway.
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7. Sustainable Energy Technologies and Assessments 52 (2022) 102184
7
analysis helps power plants better anticipate and respond to high de­
mand in the smart grid application in an actual environment. The data
transmission is obtained from Equation (10), clientj denote the client
node, gatewayi represent the gateway node, Rb
identityj
denote the secret
identification values, Ridj
represent the number of values, Vj denote the
number of clients. Gateway gatewayi would then decode the values from
an arbitrary amount aj ∈ Mn after getting the data packet from the client
and calculates Ra
identityj
to the client Ridentityj
. The data transfer by the
sender and receiver during the active and rest mode is illustrated in
Fig. 5.
The Cybersafety approach depends on the verification method and
consists of the recipient’s verification using a workout key according to
level 1. In Level 1, the transmitter groups respond and generate a
declaration containing a safe token for the sender and recipient identi­
fication. For one hash functions, the safe sign,sign = g(Ld|Mr) with a
group key Ld and an irregular quantity Mr Chosen as an input by the
recipient.The randomly generated choice g is streamlined because the
hacked identity and controller unit indicates the combination of frames.
The final relationship gives the energy usage on the sensor network is
described below.
eu = [(Ecu × Dcu) + (Erm × Dcu)] (11)
The energy usage on the sensor network eu is obtained from Equation
(11), Ecu denote the energy of controller unit inactive phase, Dcu repre­
sent the active controller unit and operating period in stages, Erm denote
energy in the silent phase.
The recipient goes away to sleep and conserves energy when the safe
item is inaccurate. The recipient then calculates the request key when
the token is checked.The sender’s station would be transmitted during
the transaction period and then attend regularly to the recipient for the
identification stage. The token transfer between sender and receiver has
antinode identification, allocation key, group development. The
Fig. 6. Energy usage during the active and rest mode.
Fig. 7. Energy usage during signal transmission.
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8. Sustainable Energy Technologies and Assessments 52 (2022) 102184
8
controller unit initiates the Transmission between the administrator.
The energy usage during the rest and active mode during signal trans­
mission is shown in Fig. 6.
The broadcast of the recipient is received, and the controller unit is
effective after the last declaration has been received. The mentioned
relationship gives the energy usage as shown below.
eu= [Ecu × (DI + DIat) + (Esx + DI) + (Erx × DIat) (12)
The relationship gives the energy usage eu in smart grid applications
is obtained from Equation (12), DI represent the period of introduction,
DIat denote the identification stage, Ecu denote the energy of controller
unit inactive phase, Esx represent senders sign in the form of the original
text, Erx represent the receiver token for declaration. The receiver must
have the same period as the transmitter calculation period. As soon as
the safe item is inaccurate, the recipient sleeps and saves energy. When
the token is checked, the recipient calculates the request key. During the
transaction period, the sender’s station would be transmitted, and then
the recipient would be regularly visited for identification.Thetransmitter
and receiver have the following relationships to their energy usage:
{
et = (Ecu × Dcu) + (Eidle × Dcu)
er = (Ecu × Dcu) + (Erest × Dcu)
(13)
The energy usage among the transmitter et and the receiver er is
obtained from Equation (13), Ecu denote the energy of controller unit
inactive phase, Dcu represent the active controller unit, Eidle denote the
energy usage in idle state, Erest represent the energy usage in the resting
state. In the energy usage phase, the transmitter and receiver signals are
transmitted and received in the same configuration. The two controller
units are still active. The energy usage during the signal transmission is
described below.
(
et = (Ecu × Dtransmitter) + (Etransmitter × Dtransmitter)
er == (Ecu × Dreceiver) + (Ereceiver × Dreceiver)
)
(14)
The energy usage during Transmission is obtained from Equation
(13), (14), et represent the transmitters state, er denote the receiver state,
Etransmitter represent the path followed by the transmitter, Dtransmitter denote
the communication period by the transmitter, Ereceiver denote the path
followed by the receiver, Dreceiver denote the communication period of the
receiver. The Transmission of a signal between the transmitter and the
receiver, the path followed is illustrated in Fig. 7. The event key gen­
eration during signal transmission and the energy usage follows an
active path.
PDST-EEF ensures the data security standards with less energy usage
by sensor devices in smart grid environments. The data privacy in sensor
networks is obtained by the authentication method combined with the
cryptographic signature method to detect attacks. EEF achieves cyber­
safety on sensor networks with less energy consumption.
Results and discussion
The experimental section PSDT-EEF was validated by utilizing 200
nodes within the 200 × 200 m network region. This integration method
has been used with a simulation sustained frame rate generator to
specify the communication structures with numerous security measures.
Every node has been connected to one single node with the desired
location. During the iterations, the activation moment of the outlets has
been evenly split, and the modeling setting is presented energy usage
and security level. The simulation parameters of PSDT-EEF are shown in
Table.1.
The standard procedure is a non-preventive key focus provider
whenever a higher importance data signal reaches the scheme and does
not disrupt the service of lesser priority data messages. The entry rates
for smart grid applications based onPSDT-EEF and the data delivery
rates are obtained from M = Rs
E . δ = M × A × E. Here M is the test
quantity per phase; the data rate is denoted as Rs, the structure peri­
odicity is represented as E, the delivery ratio is denoted as δ, and the data
delivery rate of PSDT-EEF is shown in Fig. 8. (a). The destination method
is immense in the waiting delay framework, even if happenings in the
Smart Grid environment regularly occur at sustained average rates.
The delay rate of PSDT-EEF is illustrated in Fig. 8.(b). Although the
data messages are served with specified periods, all the data packets
with equivalent durations have the average length ina probabilistic
manner.The energy usage is the energy specification dedicated to con­
sideringenergy flow from the basic sender node to the destination. En­
ergy confidence attempts to measure whether such a node contains
sufficient leftover energy to finish new interaction and data handling to
measure battery life. xandy are two different groups, and the computa­
tional form is given as Esa,b = (Esd
a,b + Esi
a,b)/2. The energy usage among
Table 1
The simulation parameters of PSDT-EEF.
Number of Parameters Obtained Values
Number of nodes 200
Data delivery rate (%) 98.56
Energy usage (%) 5.06
Throughput (%) 94.66
Delay (ms) 2.1
Fig. 8. (a) Packet delivery rate, (b) Delay.
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9. Sustainable Energy Technologies and Assessments 52 (2022) 102184
9
all nodes in WSN is calculated from the above Equation. Here a, b rep­
resents the number of nodes. Esd
a,b, Esi
a,b representthe primary and sec­
ondary points in which the energy flows from one cluster head to
another. The energy usage of PDST-EEF is shown in Fig. 9. (a).
The performance relates to a communication connection to the
typical data ratefor effective data or message distribution. Network
performance in bits per second is evaluated based on the throughput
values. The throughput of PDST-EEF is shown in Fig. 9.(b). The inter­
ference can describe the lifespan of the entire network among the set up
of the network’s packet adsorption and the initial node disruption,
which results in a battery decrease. The lifespan of the entire network is
shown in Table.2.
The impact of the attacks on the reliability of various attacks dura­
tions is described as the possibility of effective packet transmission. Data
or message distribution effectiveness depends on a specific communi­
cation connection operating at a standard data rate. A network’s
throughput values are used to calculate its bit rate. The reliability is 100
percent under normal conditions without attacks. The trustworthiness
for a minute is 50 percent. The reliability of PDST-EEF depends on the
security level in various threats. The reliability of PDST-EEF is shown in
Fig. 10. (a) The control center of the transmitter continues to remain
inactive state for security handling throughout the data encryption
authentication, and the broadcast can go into sleep mode. The re­
cipient’s watching time must be the same as the sender’s calculation
time. The security level of PDST-EEF is shown in Fig. 10.(b).
When attacks occur at node and antinode, the sender’s mean energy
Fig. 9. (a) Energy Usage, (b) Throughput.
Table 2
The lifespan of the entire network in PDST-EEF.
Nodes in the network (hrs) Lifespan
20 120
40 90
60 85
80 113
100 120
120 125
140 109
160 105
180 88
200 97
Fig. 10. (a) Reliability of network, (b) Security level.
Table 3
The interaction and the computational delay.
Condition Sender (ms) Receiver (ms)
Interaction 10.34 9.76
Computational 9.33 8.44
Attack in node PDST-EEF
Interaction 5.77 6.88
Computational 8.66 8.90
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10. Sustainable Energy Technologies and Assessments 52 (2022) 102184
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consumption is measured from the security level. The energy consumed
by the transmitter is usually less for fewer nodes.Whenever an antinode
targets every 60 s, the combination of multiple packets sent to the
receiver decreases with the reducing power usage. The interaction and
computational delay for the sender and receiver in normal conditionsare
shown in Table 3.
The proposed PDST-EEF achieves the highest reliability and less
energy consumption when compared with other existing Random-
Vector-Functional-Link-based Link Quality Prediction (RVFL-LQP),
Constrained Broadcast Scheme with Minimized Latency (CBS-ML), ge­
netic algorithm for Ticket-based QoS routing (GA-TBR).
Conclusion
This paper presents PDST-EEF to maintain a high level of data se­
curity by reducing energy consumption in a smart grid environment. The
PDST model provides data privacy in sensor networks with a crypto­
graphic signature model embedded withan authentication method to
detect various attacks. PDST identifies and separates various attack
types such as DoS and replay attacks. EEF presents a low-power cyber­
security mechanism with smart grid tracking applications in the sensor
network. EEF is modeled on various stages such as anti-nodes identifi­
cation, group development, and allocating keys with less energy usage.
EEF can operate with higher technological efficiency while maintaining
sustained performance and reliability. The experimental findings show
that the standard requirements of the PDST-EEF lead to the lesser energy
consumption of 5.06%.This work’s PDST-EEF shortcoming involves
identifying and separating for two types of attacks alone (DoS and replay
attacks). These can be used for more types of attacks to provide security
in the future.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
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