Available via license: CC BY 4.0
Content may be subject to copyright.
Submitted 2 June 2021
Accepted 16 December 2021
Published 27 January 2022
Corresponding authors
K Abdul Basith,
khateebabdulbasith2020@gmail.com
T.N. Shankar,
tnshankar2004@kluniversity.in
Academic editor
Vicente Alarcon-Aquino
Additional Information and
Declarations can be found on
page 20
DOI 10.7717/peerj-cs.845
Copyright
2022 Basith and Shankar
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Hybrid state analysis with improved
firefly optimized linear congestion
models of WSNs for DDOS & CRA
attacks
K Abdul Basith and T.N. Shankar
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram,
Guntur, Andhra Pradesh, India
ABSTRACT
A decentralized form represents a wireless network that facilitates the computers to
direct communication without any router. The mobility of individual nodes is necessary
within the restricted radio spectrum where contact is often possible on an Adhoc basis.
The routing protocol must face the critical situation in these networks forwarding ex-
ploration between communicating nodes may create the latency problem in the future.
The assault is one of the issues has direct impact network efficiency by disseminating
false messages or altering routing detail. Hence, an enhanced routing approach proposes
to defend against such challenges. The efficiency of the designated model of wireless
devices relies on various output parameters to ensure the requirements. The high energy
efficient algorithms: LEACH with FUZZY LOGIC, GENETIC, and FIREFLY are the
most effective in optimizing scenarios. The firefly algorithm applies in a model of
hybrid state logic with energy parameters: data percentage, transmission rate, and real-
time application where the architecture methodology needs to incorporate the design
requirements for the attacks within the specified network environment, which can affect
energy and packet distribution under various system parametric circumstances. These
representations can determine with the statistical linear congestion model in a wireless
sensor network mixed state environment.
Subjects Algorithms and Analysis of Algorithms, Computer Networks and Communications,
Mobile and Ubiquitous Computing, Security and Privacy
Keywords ACO, Clustering, DDOS, FIREFLY, FUZZY Logic, Genetic Algorithms, LCM, LEACH,
PDOS, WSN
INTRODUCTION
Wireless networks can be susceptible to security threats. Interference on the transmission
channel is less complicated than on wired networks, and the scrambling frequency bands
have launched denial of service attacks (Fang et al., 2016). Based on a wireless model with
an ADHOC case study, the design generates a set of failures (Dong, Abbas & Jain, 2019.
Implementing the novel scenarios on the nodes is vulnerable to DOS attacks (Chen &
Kuo, 2019;Gope, Lee & Quek, 2017). The network security model considers the proposed
scheme’s different methods that recognize various attacks and congestion problems. A
network must deliver the packets cooperatively using the available tools (Ashfaq, Ali Safdar
& Ur-Rehman, 2018). The DDoS vulnerability is one of the most dangerous attacks to
How to cite this article Basith KA, Shankar TN. 2022. Hybrid state analysis with improved firefly optimized linear congestion models of
WSNs for DDOS & CRA attacks. PeerJ Comput. Sci. 8:e845 http://doi.org/10.7717/peerj-cs.845
recognize on an ad hoc network. The information transfer on the network model provides
DDOS information on the current session from source to destination. Affected traffic
consumes the network’s bandwidth or the computing resources of the target host, resulting
in the rejection of legitimate requests. The energy changes in the design architecture for
the network based on the Adhoc model must have provision for the bandwidth features of
the different packets, and the service features can attain the traffic congestion and attack
vulnerability at every time interval. The only viable alternative is to build a defense device
capable of detecting and responding to an attack by reducing excessive traffic (Chen &
Kuo, 2019).
In recent times, information technology has flourished in all dimensions. With WSN,
an efficient medium of data exchange is to provide different features irrespective of place
and time, which is not possible with the interference of human beings. With limited energy
sources, a large number of sensors serve as nodes (Imen & Ahlem, 2021). Thus, the WSN
faces numerous problems concerning energy distribution (Razzaque & Dobson, 2014). The
utilization of power according to the amount of consumption among several components
limits the lifetime of the energy sources Doncel, 2021. Thus, it is essential to preserve the
energy to maintain a balance in terms of power distribution. Data routing and cluster-head
selection are the two significant factors for power conservation and exploitation in proper
dimensions. Therefore, many new routing protocols have been proposed to decrease the
power misutilization in the networks.
After several heuristic experiments, evolutionary algorithms have been introduced
to effect different scenarios considered. The genetic algorithm, ant colony optimization,
fuzzy applications, and firefly algorithms mimic natural phenomena. A genetic algorithm is
developed by referring to Darwin’s theory of the existence of the fittest one. The Fuzzy logic
optimization technique is a rule-based approach where the fundamental methodologies
deal with semantic information. Xin-She Yang introduced the firefly scheme to inspire the
blinking of fireflies based on a flashlight to attract other flies for mating or recognizing
predators.
With the threshold values T (n) feature, our design has improvised on the energy feature
with the DEEC algorithm and DDOS attack detection using the Firefly algorithm, as
mentioned below. Sections II and III present various scenarios based on the CH’s clusters
and protocol, including LEACH-GA, LEACH with cluster head improvement, and other
energy formulations based on threshold values. They are involved in sections IV and V
with DEEC-Firefly algorithms that improve the various formulations of T (n), promising
the different parametric changes using the linear congestion model for a DDOS attack.
This paper is based on the discussed algorithms to conserve energy.
LITERATURE REVIEW
Chen & Kuo (2019) The attack case via TCPIP represents an intriguing problem for the
DDOS model to ensure the attack case via TCPIP for each set of the packets estimated with
the throughput and bandwidth of the network model designed. The authors implement
the robust DDOS scenario natural optimization where each mobile node is estimated and
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 2/23
predicted using ACO Dong, Abbas & Jain, 2019. The mitigation classification technique
utilizes an aspect of reduced/stop packet transfer. An attack in the middle of the way
that interrupts the flow of packets is one of the DOS attacks which slows down the data
flow and can defend by grouping into various clusters (Abdul Basith & Shankar, 2020a;
Abdul Basith & Shankar, 2020b;Spurthy & Shankar, 2020). With network participation of
all the nodes for data, transmission needs a large amount of power that can resolve by node
optimization. Basha & Shankar, 2021,Yee et al., 2020;Sarkar & Murugan, 2019,Yee et al.,
2020.
Energy consumption can manage better by referring to homogeneous fuzzy-K means
clustering (Abdul Basith & Dr. Shankar, 2020a;Abdul Basith & Shankar, 2020b Abdul Basith
& Shankar, 2021;Seng, Khalid & Yusof, 1999). Classification and clustering improve the
energy imbalance in wireless sensor networks (Siva Shankar et al., 2020;Ashokkumar et
al., 2021). The wireless networks have several issues in terms of energy used to assemble
the heterogeneous mobile Adhoc networks. Fatima, Mahin & Taranum (2019) propose
efficient procedures to overcome such challenges. Any disaster must cause damage to
the tel-infrastructure. Chen & Liu (2013) suggest a low-cost internet communication
system to challenge such a situation (Daanoune & Ballouk, 2020). Presents a novel idea for
improving the routing protocol’s energy efficiency. Doncel & Fourneau (2019) emphasize
the importance of individual packet energy conservation. Elsmany et al. (2019) have
published a comprehensive work on energy-efficient scalable routing algorithms that helps
save energy at various levels. The main intention of any energy conservation measure is
to enhance the duration of wireless sensor networks by minimizing the energy distortion.
Song and FanYanxiang (Yang et al., 2018); A network must refer to the Low Energy
Adaptive Clustering Hierarchy (LEACH) protocol for better performance (Singh, 2014,
Marappan & Rodrigues, 2016;Al-sodairi & Ouni, 2018;Fan & Song, 2007). We will discuss
Energy LEACH and multi-hop LEACH to achieve improved performance. A Genetic
Algorithm (GA) is famous as an essential tool for optimizing complicated challenges based
on the fitness principle of the gene (Lambora, et al. 2019;Wu et al., 2020). In addition
to optimization, it also serves the purpose of machine learning and for research and
development. The Firefly algorithm is like swarm optimization and is simple to learn and
employ (Avudayappan & Deepa, 2016;Khan et al., 2016).
A cyberattack of this kind is likely to result in significant economic losses for companies
and service providers due to the increased operational and financial expenses that will
incur (Fatima, Mahin & Taranum, 2019). Machine learning (ML) methods have been
more popular in recent years to prevent distributed denial of service (DDoS) attacks.
Indeed, with machine learning methods, many defensive systems have been converted
into innovative and intelligent systems, which has enabled them to resist DDoS assaults.
This article examines current research on DDoS detection techniques that has adapted
single and hybrid machine learning methodologies to contemporary networking settings,
as well as their limitations. In addition to this, the article covers several DDoS defensive
systems that depend on machine learning methods and operate in a virtual environment,
such as cloud computing, software-defined networks, and network functions virtualization
environments (NFV) (Marappan & Rodrigues, 2016). Because the growth of the Internet
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 3/23
of Things (IoT) has received considerable academic interest in recent years, the article also
addresses machine learning (ML) methods as security solutions against distributed denial
of service (DDoS) attacks in the Internet of Things settings (Avudayappan & Deepa, 2016).
(Gope, Lee & Quek, 2017) The mix of assault methods with different traffic data analyzed
has been the most challenging problem in DDoS detection. As part of this article, we
introduce Lucid, a practical and lightweight deep learning DDoS detection system that
uses the characteristics of convolutional neural networks (CNNs) to categorize traffic flows
as either malicious or benign. The four significant contributions are now: (1) a creative
framework of a CNN to pinpoint DDoS traffic with limited computational overhead; (2) a
dataset-agnostic data preparation method for producing traffic predictions for web security
attacks; (3) a stimulation evaluation to illustrate Lucid’s DDoS identification; and (4) an
accurate understanding of the alternative model on a resource-constrained computing
system (Yee et al., 2020). Lucid can match the current state-of-the-art detection accuracy
using the most recent datasets while exhibiting a 40x decrease in computing time compared
to the state-of-the-art accuracy rate.
DDOS ATTACK AND ENERGY
ADHOC routing model and DDOS importance
Because of the mobility of nodes in ad-hoc networks, routing has proven to be very difficult.
The routes for a given scenario may or may not exist between source and destination.
The intermediary nodes’ energy levels must consider when making routing choices in a
resource-constrained environment, such as a wireless sensor network (WSN).
In a wireless sensor network, routing methods have been categorized into four
techniques: proactive routing protocols [3], reactive routing protocols [4], hybrid routing
protocols [5], and location-aware routing strategies. In an aggressive routing scheme,
each node keeps its routing table up-to-date by regularly asking its near neighbors for
additional routing information. One such system is the Destination Sequenced Distance
Vector (DSDV) routing protocol [4], an example of this scheme. Such systems, however,
have many significant disadvantages, one of which is the extra cost associated with frequent
route changes. On the other hand, reactive routing includes the creation of routes on
the fly and will drive by demand. It is basis will implicate the request–response paradigm
of communication. Flooding may be used during the initial discovery phase to locate
the intended node, and the response phase is responsible for establishing the temporary
active routing route. Ad-hoc On-Demand Distance Vector (AODV) Routing and Dynamic
Source Routing (DSR) are two examples [5, 6] of such routing techniques.
Many hybrid protocols combine the node-discovery technique of the proactive routing
protocol to establish routing paths on the fly to create a hybrid version of the protocol.
A hybrid system is the Zone Routing Protocol (ZRP), developed by Cisco [7]. When
making routing choices in position-aware routing protocols, the nodes choose the adjacent
node that is the most geographically nearest to them. Among the protocols that fall into
this category is the Geographical and Energy-Aware Routing (GEAR) protocol. Gear, on
the other hand, does not take safety into account. Because asymmetric-key cryptography
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 4/23
Figure 1 Representing the literature survey of the DDOS AND CRA attacks.
Full-size DOI: 10.7717/peerjcs.845/fig-1
(RSA-based algorithms) was computationally expensive, most secure methods in WSN
relied on symmetric-key cryptography as its foundation.
On the other hand, Symmetric-key cryptography has significant disadvantages in
key management, and security depends on pre-shared secret keys. The successful
implementation of pairing-based cryptographic algorithms in WSN has opened the door to
developing an entirely new platform for deploying asymmetric-key cryptographic methods
in WSN. Each node needs the energy to perform any operation such as packet forwarding,
receiving defense attacks; however, any node malfunctioning or lacking strength may
succumb to a denial of service attack, which can nullify by optimizing the nodes as the
firefly algorithms.
The features of the different algorithms and their analysis by other authors have been
analyzed. Figure 1 and Table 1 are the same, but only a clearer perspective has been
mentioned using figures and table formatting.
Leach A-B implementation
Heinzelman, Chandrakasan & Balakrishnan (2002) Introduce the LEACH, Low-Energy
Adaptive Clustering Hierarchy, hierarchy model term, the first and most prominent case
of the WSN energy reduction scheme. This protocol transmits the various assumptions
on power transmission and its associated formulation model to the base station BS with
precise transmit power. The node ensures the control capability for each set of conditions
is adjusted to withhold the Tx-power to address the power computation observed while
providing the MAC protocols to ensure the signal processing functions are modeled.
LEACH protocol improvises with timing scenarios on states of clusters such as setup,
steady-state, framing, and rounding the data to ensure the generation of a practical
solution after passing through the threshold.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 5/23
Table 1 (Razzaque & Dobson, 2014–Singh, 2014) Representing the literature review on DDOS-CRA attacks.
SNO References Year Objective Accuracy
1Siva Shankar et al., (2020),
Hassanat et al. (2016)
2018–2019 The ML algorithms with KNN, DT are improvised on
routing protocol. With the learning models an algorithm
with packet and energy control features are obtained
91.56, 92.67
2Chen & Kuo (2019) 2019 DDOS attack module for SDN controller is modelled,
BILSTM utilized with SDN environment.
96.85
3Toumia & Hassine (2021) 2018 With feature for the DDOS attack with sensor networks,
improvising the different features on heterogeneous
network with mathematical approaches to govern the
feature of attack criteria
90.52
4Doncel (2021) 2017 A sequence of the different solution features and its
parametric are advised with gradient features.
94.2
5Abdul Basith & Shankar (2020a) 2016 SDN with cloud computing environments are estimated
and implemented with DDOS and other network features
for high data rate
–
6Abdul Basith & Shankar (2021),
Abdul Basith & Shankar (2020b)
2016, 2018 ANN is utilized to initiate both routing and Energy with
prediction of the different parametric features. In [15] multi
neural layers (DNN) are estimated to analyses the intrusion
detection for SDN.
95.52, 97.25
7Spurthy & Shankar, 2020,
Basha & Shankar (2021)
2016, 2019 DDOS with cloud conceptual framework are mitigation on
the solutions framed with Energy and other attacks in cloud
systems.
–
8Ashfaq, Ali Safdar & Ur-Rehman (2018) 2017 Different layers are implicated on the improved model for
attack and defense structure model with DDOS attacks.
–
9Fatima, Mahin & Taranum (2019) 2017 Control systems are analyzed with feature on the
information criteria and its perception for different
environments.
–
10 Marappan & Rodrigues (2016) 2020 A key model and its importance feature for improvising a
protocol for the attack features, are estimated with sensor
nodes for each WSN model in ISM.
–
11 Avudayappan & Deepa (2016) 2018 A CNN model with empowering the different features for
input and output intrusions of DDOS attacks.
98.52
12 Gope, Lee & Quek (2017) 2018 A SDN is implicated on the mitigation DDOS attacks.
13 Yee et al. (2020) 2016 A clustering model and method for energy and estimating is
mathematical improvised to provide a clusters of different
characteristic nodes are implemented.
95.14
14 Razzaque & Dobson (2014) 2020 The first stage is the identification of temporary CHs, as
well as the determination of its entropy value, which is
determined by calculating the correlation between residual
and original energy. In addition, in the cluster algorithm,
the rotating epoch and its entropy value must be anticipated
automatically by each of the sensor nodes in the cluster
algorithm. In the next step, if any member of the cluster
has a greater amount of residual energy than the deciding
set, the temporary CHs will be modified in the direction
of the deciding set. When nodes with high energy are
targeted, there is a good chance that they will be CHs, which
is determined by the two steps described above that are
intended for CH selection. T
–
(continued on next page)
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 6/23
Table 1 (continued)
SNO References Year Objective Accuracy
15 Singh (2014) 2021 This article primarily focuses on categorizing threats
and potential security solutions in relation to the IoT
layers architecture. Because of this, each attack is tied to
one or more levels of the architecture and is followed by
a review of the literature on the different IoT security
countermeasures available.
–
T is the sensor nodes considered with different values where the cluster head becomes
the futuristic cluster current round if the threshold number is chosen. It iterates until
Eq. (1) is satisfied.
T(n)=
p
1−prmod 1/p n∈G
0others
.(1)
Here, T(n) represents the overall sensor nodes probability, p defines the probability
for which one node is active. N represents number of nodes and G represents the overall
cluster.
The LEACH represents an advanced protocol where each energy set is remodelled with a
heterogeneous probability of each node on failure and alive scenario. The synchronization
model on clocking applies with a factor of each node observed on every round where the
maximum energy is estimated with selected clusters head which could provide the path
on each set of distances observed from the base station. Similarly, a balanced LEACH
protocol is considered, which utilizes the residual energy equation established for each
sensor node. The decentralized approach provides the position of source and destination,
which improvises three cases on selecting CHs, cluster formation, and data transmission
information accessed with multiple scenarios. Hence, the current node’s energy dissipation
for each CH’s destination estimate is established on recent rounds.
a. LEACH with genetic algorithm. One such feature on WSN’s has been proven a newer
feature for reducing energy on sensor nodes and implicating higher data transfer with a
Nature-based algorithm as a Genetic algorithm. Data groups and their functional values
have become a critical scenario for using genetic algorithms in wireless sensor networks.
They allow the development of the most energy-efficient and stable clusters. GA is used
exclusively for integrated grouping measurements in more spectacular execution hubs such
as the BS. Generally, a high-quality gene in the chromosomes communicates with a sensor
center. The duration is defined when the network model has developed the characteristics
of the architecture parametric parameters, ensuring the various sensor hubs that interact
either individually or in clusters.
LEACH-GA improvises the hierarchy of the different clusters governed by the features
on the nodes identified as the characteristic nodes. These are estimated with the specific
models to be analyzed in the different iterative scenarios that govern the energy or path
features—the value of T(n) implicating the probability analysis on each set of sensor node
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 7/23
creation. With the propositions mentioned per the algorithm, a model estimation of the
different scenarios for each design stage is made.
b. DEEC-FIREFLY. In DEEC-FIREFLY, the most effective method of increasing the
endurance of WSN is to combine WSN with tremendous energy, which is referred to as
the Head of the group, also known as the Cluster Head (CH). CH becomes reliant on
other clusters for intra-cluster and inter-cluster communication. The energy level of CH
increases the longevity of a set in a fully functional WSN. The difficult task is determining
the amount of energy used by heterogeneous networks while simultaneously developing
the clustering method. The proposed work is titled ’’Novel Distributed Entropy Energy-
Efficient Clustering Algorithm,’’ or short for ’’DEEEC for High-Speed Networks (HWSNs),
and it is based on the Firefly Algorithm CH (DEEC FA-CH) Selection. The DEEEC
Algorithm, represented by the letter CH, is divided into two phases. The identification
of temporary CHs and their entropy value, which is determined using the correlative
measure of residual and original energy, is performed in the first stage. In addition, each
sensor node must automatically anticipate the rotating epoch and its entropy value in the
clustering algorithm. Next, if any cluster member has considerable residual energy, the
deciding set where the particular nodes representing the cluster heads are minimized in
the sensor node’s direction is active. When nodes with high energy are targeted, there is
a good chance they will be CHs, which is determined by the two steps described above
intended for CH selection. Simulating the DEEEC algorithm requires the use of MATLAB
software. Compared to existing conventional clustering protocols, which are utilized in
heterogeneous WSNs, the simulated results of the proposed DEEEC Algorithm provide
favorable outcomes in terms of energy consumption and improved lifespan.
A sensor node is appropriately installed in most geographical locations where energy
solutional values become minimum. The extraction of wide-ranging network architectures
for simulation is accomplished by assuming random node positions as realistic as possible. A
fixed sensor network is established after the deployment of the sensors, and data is typically
sent to a stationary base station, which is situated at a distance from the sensing part. In
contrast to DEEEC, if the middle region is located as the source point, the assumption is
made to ensure that the BS is correctly positioned in the organization (0, H).
Each node is acquainted with the overall energy used in the network by retrieving a data
packet from the BS. When considering the whole HWSN, the equation represents the total
energy.
Entotal =
N
X
i=1
(1+ai)En0=En0(N+A)(2)
Here, airepresents random number varying values from (0-1), m being the fraction
of advanced nodes of the total nodes N also A refers the different lower bound energies
available. The multipath-channel form is utilized in various situations depending on
the location between the transmission and reception. The amount of energy required to
broadcast an l-bit packet over a long distance is referred to as
Etx =lEntotal +d2
minlefs +d4
minlefs (3)
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 8/23
From Eq. (3) Etx representing the transmitted energy, l represents the data bits to the
sender, d2
min minimum distance squared value from selected node to Tx and Rx, efs being
the estimated amplified energy
Similarly for receiving side,
Erx =lEntotal (4)
Finally for all the iterations,
Et=Erx +Etx (5)
With Eq. (4) our design improvises the design feature to evaluate it,
dismin =W/√2πexc(6)
With the estimation on Eq. (5), the minimum values for the distance are statistically
modeled with excand W.
W with the probability observed as the weights. The simulation parametric are mentioned
in the simulation results section.
Fuzzy logic for leach protocol
The design factor of the blocks considers three parameters. Parametric factors such
as distance, velocity, and density in the communication model implicate the different
solutions of the energy model. Karimulla Basha and T. N. Shankar, 2021. The fitness values
are acquired with fuzzify-defuzzify on the required data considered. A characteristic map
is created with conditional changes in parameters for each fitness value observed from
defuzzification.
Figure 2 depicts the structure of the fuzzy system in various requirement scenarios where
each set of the cluster justifies the importance of the implementation of WSN features for
each specific value. The energy and network lifetime models use the prediction values with
the different case studies.
Genetic algorithm. Some potential solutions create a dilemma with the population/pool
of possible solutions. The solving techniques process experience recombination and
replication (as in natural genes), lead to the resolution and eventually pass on as new
life-forms. The technique is used and handed down from generation to generation.
Individuals (who have better fitness) are granted a higher priority because the preferred
option (or product) has the advantage of being made and given to healthy individuals (the
best-fit ones are assigned a high priority), consistent with the ’’survival of the fittest’’ theory
of Charles Darwin.
Instead of discarding each age and going back to the previous one, progressing towards
better individuals or alternatives is quite impossible before hitting a hard limit. Assume
at random to do much better than just a simple search by gathering knowledge from the
past (which depends on using the ‘‘try all and pick out the strongest’’ algorithm) for the
combinatorial experience.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 9/23
Figure 2 Representing the flow diagram of fuzzy logic optimization.
Full-size DOI: 10.7717/peerjcs.845/fig-2
Genetic algorithm phases. The criteria on the current algorithms for the design features
improvise in different scenarios with each set of parametric to ensure the correct and
predicted outcome where the section of modeling establishes as the problem-solution that
depends on various states.
Population: Solution set on each subset of problems assigned to specific criteria on
specifications such as node estimation and characterizing the node value states.
Chromosomes: A single solution set on each set of iterated outcomes observed on a single
phase.
Gene: Elemental values on each position for the variation observed when a chromosome
generates.
Genotype: Computation model for each set of problem solutions.
Encoding and Decoding : Spatial change on variables and the positional values.
Algorithm 1
Begin
Chi←Encode the chromosomes.
do While
Fi←O(Chi) // Use the fitness function to learn that is fittest or not.
Ci←ChiCrossover Chi+1
Mi←M(C i) // M is the mutation
Ch i+1←F(M i)
End do
End
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 10/23
WSN with Genetic Algorithm
GA is used in several domains, including wireless sensor networks (WSNs)—including
planning, data transmission, directing the different signal data according to an existing
model, and bunching various collections of energy-efficient clusters. Data groups and their
functional values can be used in genetic algorithms in wireless sensor networks as they allow
the development of the most energy-efficient and stable sets. GA can exclusively utilize it
for integrated grouping measurements in spectacular execution hubs like the BS. Generally,
a high-quality gene in the chromosomes communicates with a sensor center. The duration
defined when the network model is under development reflects the characteristics of the
architecture parametric to ensure the various sensor hubs that interact either individually
or in clusters.
Algorithm 2
Cluster(Cl) ←Population
Begin
Chi←Encode is one of the features of a cluster node as a chromosome.
do While
Fi←O(Ci) //Get the fitness genes by the optimization function.
If (Fi——fittest)
Ci←ChiCrossover Ch i+1
Mi←M(Ci) // M is the mutation
Cl i+1←F(M i) // Form a new cluster with fittest nodes
End do
End
PROPOSED MODEL
The proposed model refers to the link state modeling with the threshold algorithms that
ensure the different sets of sensor nodes that are active and passive values with each group
of MPRs chosen. This formation happens by utilizing Dijkstra’s algorithm by considering
various sensors and their relative positions. This design improvises the model with a firefly
algorithm, ensuring the best swarm for the energy optimization and packet (alive and dead
clusters also). The proposed prototype imparts the design features with firefly optimization
to produce a better outcome for each set of parameters such as energy, network lifetime,
and packet drops in alive and dead scenarios. The criteria of each parameter can be observed
and estimated for each functional model. Our design uses a specific optimization algorithm
as a firefly for the Linear Congestion Model (LCM) with different parametric features.
Considering elements in the linear congestion model can utilize the entropy and gain
values from each node and its selected MPR’s.
Problem statement
a. Analyze the current design optimizers with energy equations using the firefly scheme
with the best performance features.
b. The proposed design implicates the factors of the link state with the firefly algorithm
to provide the root-sigmoid calculations over the selected nodes.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 11/23
c. Reduction on functional parameters: Energy, transmission range, packet rate, load on
a network must emphasize the (5).
Algorithm 3
Number of fireflies =m
Begin
B←Brightness
j←1 to m-1
k←j+1 to m
For Loop
if (Bk>Bj)
Print ‘‘Fly firefly j to k.’’
end for
End
Algorithm 4
Number of nodes in WSN =m
Begin
S←Energy level
j←1 to m-1
k←j+1 to m
For Loop
if (Sk>Sj)
Print ‘‘Select the path jto k.’’
end for
End
Formulation for energy-efficient
From the perspective of design scenario on LEACH-FA, With Fuzzy logics on Energy values
which are governed with sigmoid function mentioned below:
S(i)=
N
X
i=1
1/(p(1+e−i)) (7)
The functionality of the S represents the design solution of the nodes that appear at the
given timing aspect, where each set of the design is parametrically considered with active
and dead cells from the equation.
F(i>k)=
N
X
i=1
(ni∗MPr (i)+σ∗S(i)) (8)
F represents the solution model where all the active and alive nodes in a cell region are
established with Eq. (8),and n istands for the involved nodes.6is the best prediction of
firefly optimization for all the iterations.
PEMPRs =F(i>k) (9)
PEHead_cluster =γ∗Wi∗Dmin +µMPr (i)+E(i) (10)
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 12/23
Here E(i)represents the entropy of each selected MPR selected.
Hence the total Network energy estimated is:
PET=PEHead_cluster +PEcluster_MPRs (11)
Here γ , µ,σ , are estimated probabilities for the optimized values of the best solution in
the modified firefly algorithm based on the energy equations on active and dead scenarios.
Power observed based on the algorithm (LS_MFT) on the different MPR active nodes and
control with distributed energy model implementation with an ensemble approach.
Rate control formulations
The actual rate ACRgis given by
ACRij =W∗ARij (12)
Where,
W=Lc
PARij (13)
If the source receives the congestion bit (CB), the allocated rate
ARij =ACRij −δ(14)
The rate monitoring function measures the traffic rate of a given in-out stream over a
time interval T.
MRij =Cij /T(15)
If the estimated intensity MRij is greater than the real rate ACRij for the following time
frame, the flow is classified as an assault value. The state of the request is set to REJECT,
and the FMM records the corresponding source of IP address.
Firefly optimization:
Algorithm improved:
1. Improve the position vector by the formulation based on the energy equation F(I > k)
as:
a.
xk+1=xk+e−r2ω xk
i+1−xk
i
4
√1+e−x
2!+δ∗xmink
i(16)
b. Here x is the expected outcome for each energy−optimized value observed for Net-
work model
c. ωand δare the step factors, xmin are feature values varies from (0,1).
d. e−r2ωis a characteristic feature for Expected value for the Energy optimization with
Minimal step variance of 0.785 value.
2. Secondly, update Eqs. (7) and (16) for each set of values observed for energy
implementation using Link state and DEEC.
3. Thirdly implement this optimization using energy and active and passive nodes.
4. Finally, update the iterations, ensuring the correct and accurate results have been
observed.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 13/23
Figure 3 Represents hoping, node-ids, MPR’s branched with the decision tree algorithm.
Full-size DOI: 10.7717/peerjcs.845/fig-3
Link state firefly optimization
1. Initialize the design with a flag and graph where each node utilizes the maximum value.
2. Estimation on each tree graph can generate from the selection of different MPRs
based on the Dijkstra technique to calculate the optimum with the minimum distance
between the nodes estimated for MPR’s.
3. Optimization of the threshold values for each selected MPR’s generated from Eq. (1).
4. Hoping on iterations changes would suffice the selection of MPR’s onto minimum
energy model applies for given criteria.
Link state model for multi-point relays in wsn
The current link state design incorporates a switching node to forward packets labeled
with routers. Figure 3 shows two scenarios for learning models: design accuracy and
energy reduction capabilities. With the formulation provided on Eqs. (2)–(3), our design
implicates the different distance measurement from each set of cluster nodes.
Link state prediction with energy clustering
According to the prediction study of the new hybrid protocol, various relay points emerge
in response to the threshold and total node considerations. To start the multi-point
segmentation with defined hops, created flags and exit flag conditions must be updated
and current authentication dedicated to the cluster nodes associated with the MPR’s. Initial
Activation and its values for each active node can represent either alive or dead scenarios
for each cluster accumulation on the current network using the cluster head selection.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 14/23
The formulation for defensive mechanism for energy model and
resource consumption
The behavioral model has two distinct phases: (i) query and transfer and (ii) bandwidth
request and reply controls. The source IP address, destination IP address, port, and flow
ID is essential for making a query on a node in a request packet. The proposed system
computes the FMT data along a vector. If the intermediate node receives a return answer
on the back-to-back route, that modifies the FMT and pushes forward into the return path
by transmitting the BnBW value to the next node. For the initial determination of ABW,
the available bandwidth is tested. If the bandwidth is accessible, this excess can provide the
flow, ABW > BnBW. Otherwise, BnBnB is reduced to AB. In this case, the reply packet’s
assigned rate is now RRij. An FMM entry is created if the stream before j was previously
inactive. Following the REPLY packet, the message is sent in the same direction. The source
discards the real-time flow based on the BnBW value at this stage.
Proposed defense algorithm
With the parametric feature of the design modules based on the DEEC-FIREFLY, our
design has initiated different hybrid states for the IFFLY (Improved Firefly). The node
features on the particular time and simulations for the sender, and receiver id depends on
the minimum energy values observed with the LSF algorithm. The proposed algorithm
(IFFLY and LSF) would operate with different features based on standard and attack
modules.
A. Algorithm:
1. Initiate a timing scenario with a start and stop values for the design set.
2. With Link state firefly optimization, the design with the estimation of the minimum
values for different nodes generated a threshold based on the Th_min (ensuring the
minimum distance from each node observed).
3. Finally, we check whether the Links are optimized with Th_min and its corresponding
nodes with E_min.
B. ATTACK MODULE:
DDOS attacks are consistent with not having users access the correct information with
available resources. The initiating of the attack feature would represent the packet that is
not enabled for the attacker node from the sending to receiving to the neighboring nodes.
With the characteristic of the Link states, we have established that selected MPR’s with the
least distances are entitled to features of Firefly optimization based on the DEEC protocol
for parametric network features. With the timing feature, we provide a condition that
governs the design as attack and defense mechanism as Th_min and E_min are less than
that T_r*E_min, T_r*Th_min. T_r is the estimated probability for observing alive and
dead nodes on the Routing protocol as the Links state mechanism.
C. Algorithm:
1. Create a Timing event for any Node as attack node when Th_min <Th_min*T_r
2. The attack rate of 0.49 for Th_min, Emin is 0.9*E_min
3. Scanning and analysis for each set of the design model and its attack and defense
mechanism features are established for every iteration.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 15/23
Figure 4 Representing WSN structure on LS-DT algorithm model for MPR selection.
Full-size DOI: 10.7717/peerjcs.845/fig-4
4. 1,000, 5,000, and 10,000 iterations are utilized to estimate the thresholds and Energy
minimization.
RESULTS AND DISCUSSION
Figure 4 covers the orthogonal least squares (OLS) algorithm for providing the different
selections of MPR as mentioned. Also, it describes the condition of the network model
for which each set of the design criteria on the active and passive nodes are available in
a short range refers to the orthogonal least squares OLS algorithm. The connection of 47
nodes with the multi-point relay MPR selection indicates the green color and MPRs by the
yellow.
Ls-Dt Defines Link State Decision Tree, MPR Defines Multi-Point Relays, Ols Defines
Orthogonal Least Squares, DEEC Defines Deterministic Energy Efficient Clustering Protocol,
Leach-Fa Defines Leach-Fa Defines Leach-Fa Defines Leach-Fa Defines Leach-Fa Defines
Leach-Fa
The graph provides the least energy simulation values, as mentioned in Fig. 5, for 5,000
iterations of the design matrix 100X100. The experiment was conducted with various values
of the OLS algorithms and provided a different scenario of the energy reduction from the
protocol utilization. The multi-point relay (MPR) of the iterations 93, 54, and 1 ensures
the sets of changes in each iteration’s minimum and maximum values.
Figure 6 demonstrates the output of simulations for the proposed scheme with the
LEACH fuzzy application (FA), Genetic Algorithm (GA), and DEEC Fire-fly scheme.
The above bar graph describes the first node die (FND) at the 830th iteration for fuzzy
application (FA), the 800th for GA, and 520 of the proposed one. Half-node dies (HND)
measurement is 1200 for FA, 1500 for GA, and 1820 for the project. Finally, the last node
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 16/23
Figure 5 Representing the minimum energy values for the MPR-node at each iteration length of 5000.
Full-size DOI: 10.7717/peerjcs.845/fig-5
Figure 6 Representing the minimum energy clustering for all the three different algorithms imple-
mented.
Full-size DOI: 10.7717/peerjcs.845/fig-6
die (LND) calculation is 1,600 iterations of LEACH FA, 1820 for GA, and 2490 for the
suggested algorithm.
The number of data packets received by the base station is also a parameter for evaluating
the high energy efficiency of the utilization rate. The more balanced the energy distribution
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 17/23
Figure 7 Representing the active nodes observed for all the three different algorithms implemented.
Full-size DOI: 10.7717/peerjcs.845/fig-7
in the network, the more packets the base station can receive Fig. 7 observes that the
number of packets received at the BS for LEACH, LEACH-C, SEP, and
LEACH-VA protocols, where the length of one packet is 4000 bits. As can be seen
from this picture, LEACH-VA Increases packet counts received at the BS by 71.4% when
compared to LEACH, 33.3% when compared to LEACH-C, and 14.3% when compared
to LEACH-C. Against SEP. The significant increase in packet counts received by the base
station reduces the probability of cluster head clusters. It effectively reduces negotiated
communication consumption within the group of sets. Based on the stable number of
cluster heads and the geometric principle of the Voronoi diagram, the clusters are more
uniform, the energy consumption between the sets is better, and the energy utilization of
the unit nodes is also improved. Moreover, in the paper, a multi-hop transmission routing
protocol according to an ant colony optimization algorithm is used to forward the data
packets of a long-distance cluster head by a neighboring cluster head of the BS to reduce
the energy consumption of direct communication.
Figure 8 compares the proposed solution to LEACH according to the remaining energy
metric.
The remaining energy is the difference between the initial and consumed energy. From
these curves, we can see that the remaining energy of total sensor nodes in the proposed
protocol gradually decreases. Compared to the classical LEACH approach. Then, at around
1200, it is almost null in the LEACH, while in the proposed protocol, it is still more than
15% of the initial energy of all the network nodes. From these results, we can see that the
proposed protocol in this paper can optimize energy consumption and extend the stability
of the network better than the original LEACH protocol.
Tabulations:
From this perspective on Table 1, our design feature implicates the different sets
of the survey publications as references and its accuracy parametric as mentioned in
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 18/23
Figure 8 Total remaining energy of nodes in LEACH and the proposed protocol.
Full-size DOI: 10.7717/peerjcs.845/fig-8
Table 2 Representing the different Algorithms and its Energy minimazation values comparisons.
Feature Total
iterations
Existing algorithm
models
Proposed
algorithm
Energy Total
iterations
Leech DEEC
firefly
HSLP
algorithm
Minimum energy observation
for 1k iterations
1,000 −45.42 dB 75.19 dB 85.89 dB
Minimum energy observation
for 5k iterations
5,000 −89.34 dB −90.75 dB −119.34 dB
Minimum energy observation
for 10k iterations
10,000 −101.23 DB −105.23 dB −147.23 dB
Table 1. References Abdul Basith & Shankar (2021),Abdul Basith & Shankar (2020b) and
Avudayappan & Deepa (2016) provide the highest precision as per the design criteria are
noted with designs that did not specifically mention the design accuracy.
From Figs. 5–7in Table 2, we have represented a practical solution that utilized the
design importance based on the metric equations proposed for firefly optimization and its
essential feature for energy and distance reduction based on the formulation model of the
design scenario. This model provides extensive usage in WSN as the observed minimum
energy would be around 85-150 dB as a minimum as possible for iterations 1,000, 5,000,
and 10,000. These scenarios are observed even on alive and dead nodes, modeled based on
the firefly equation, ensuring the correct optimized values.
CONCLUSION
Saving energy is challenging within limited resources for packet forwarding in a wireless
network. So many methods have been proposed for node optimization to eliminate the
number of forwarding points and save resources. The Firefly algorithm is one of the best
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 19/23
schemes for improving efficiency in LEACH as an alternative to randomly choosing the
number of cluster heads, which consumes more power for packet dissemination in such
an environment. As the output of simulations demonstrates through various graphical
representations, the proposed scheme can be stabilized based on the power level and make
the network more efficient based on the power level. In the future, the design features are
implicated with the enhanced-LEACH protocols to learn the story of energy conservation
and the duration of the system lifetime. There is an advantage over traditional network
equipment in a resource-constrained setting, though this is only true about specific types
of technology that can operate with WSN. As mentioned in the literature survey, the
security of nodes is represented with different algorithms but may put the network at risk.
The defend-but-do-not-abuse (Do Not Distrust) protocol can refer to trust relationships
with WSNs. Since a wireless sensor network must respond quickly, its speed must be low.
Additional research is needed on current wireless channels and applications and enhancing
the trust framework itself. Thus, we will use risk evaluation, recycling, and novel approaches
that combine trust and energy efficiency to reduce risk.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this work.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•K Abdul Basith conceived and designed the experiments, performed the experiments,
performed the computation work, prepared figures and/or tables, and approved the final
draft.
•T.N. Shankar analyzed the data, authored or reviewed drafts of the paper, and approved
the final draft.
Data Availability
The following information was supplied regarding data availability:
The codes of the design are available in the Supplemental Files.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj-cs.845#supplemental-information.
REFERENCES
Abdul Basith K, Dr. Shankara TN. 2020. A detection of distributed denial of service
attack using advanced quadratic route factor estimation. International Journal of
Advanced Science and Technology 29(6):8036–8044.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 20/23
Abdul Basith K, Shankar TN. 2020b. Security using ECC based on fuzzy clustering in
wireless sensor networks. IJCSNS International Journal of Computer Science and
Network Security 20(11):20201104.
Abdul Basith K, Shankar TN. 2021. Energy and efficient privacy cryptography-based
Fuzzy K-Means clustering a WSN using genetic algorithm. In: Advances in intelligent
systems and computing. vaddeswaram , vijayawada: springer artical - koneru laxmia
education foundation, 291–304 DOI 10.1007/978-981-33-6176-8.
Al-sodairi S, Ouni R. 2018. Sustainable computing: informatics and reliable and energy-
efficient multi-hop LEACH-based clustering protocol for wireless sensor networks.
Sustainable Computing: Informatics and Systems 20:1–13.
Ashfaq K, Ali Safdar G, Ur-Rehman M. 2018. Comparative analysis of scheduling
algorithms for radio resource allocation in future communication networks. PeerJ
Computer Science 7:e546 DOI 10.7717/peerj-cs.546.
Ashokkumar P, Siva Shankar G, Srivastava G, Maddikunta PKR, Gadekallu TR.
2021. A two-stage text feature selection algorithm for improving text classification.
ACM Transactions on Asian and Low-Resource Language Information Processing
20(3):Article 49 DOI 10.1145/3425781.
Avudayappan N, Deepa SN. 2016. Optimal location of TCSC and SVC using hybrid
fruit fly firefly optimization algorithm in transmission system. Asian Journal of
Information Technology 15(16):2863–2872.
Basha K, Shankar TN. 2021. Fuzzy logic-based forwarder selection for efficient data
dissemination in VANETs. Wireless Networks 27(3):2193–2216.
Chen H-C, Kuo S-S. 2019. Active detecting DDoS attack approach based on entropy
measurement for the next generation instant messaging app on smartphones.
Intelligent Automation and Soft Computing 25(1):217–228.
Chen D, Liu Z. 2013. Natural disaster monitoring with wireless sensor networks: a case
study of data-intensive applications upon low-cost scalable systems. Mobile Networks
& Applications 18(5):651–663.
Daanoune AB, Ballouk A. 2020. An enhanced energy-efficient routing protocol for
wireless sensor network. International Journal of Electrical and Computer Engineering
10(5):5462–5469 DOI 10.11591/ijece.v10i5.
Doncel J. 2021. Age of information of a server with energy requirements. PeerJ Computer
Science 7:e354 DOI 10.7717/peerj-cs.354.
Doncel J, Fourneau J-M. 2019. Energy packet networks with multiple energy packet
requirements. Probability in the Engineering and Informational Sciences 35(1):92–110.
Dong S, Abbas K, Jain R. 2019. A survey on distributed denial of service (DDoS) attacks
in SDN and cloud computing environments. IEEE Access 7:80813–80828.
Elsmany EFA, Omar MA, T-C W, Altair AA. 2019. EESRA: energy efficient scalable
routing algorithm for wireless sensor networks. IEEE Access 7(2019):96974–96983.
Fan X, Song Y. 2007. Improvement on LEACH protocol of wireless sensor network.
In: Proc. of international conference on sensor technologies and applications (Sensor-
Comm’2007). Piscataway: IEEE, 260–264.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 21/23
Fang WD, Shan LH, Jia GQ, Ji XH, Chen SJ. 2016. A low complexity secure network
coding in wireless sensor network. Journal of Internet Technology 17(5):905–913.
Fatima LN, Mahin SH, Taranum F. 2019. Efficient strategies to reduce power consump-
tion in MANETs. PeerJ Computer Science 5:e228 DOI 10.7717/peerj-cs.228.
Gope P, Lee J, Quek TQS. 2017. Resilience of DoS attacks in designing anonymous
user authentication protocol for wireless sensor networks. IEEE Sensors Journal
17(2):498–503.
Hassanat AB, Alkafaween E, Alnawaiseh NA, Abbadi MA, Alkasassbeh M, Alhasanat
MB. 2016. They are enhancing genetic MB, algorithms using multi mutations. PeerJ
Preprints 4:e2187v1 DOI 10.7287/peerj.preprints.2187v1.
Heinzelman WB, Chandrakasan AP, Balakrishnan H. 2002. An application-specific
protocol architecture for wireless microsensor networks. In IEEE Transactions on
Wireless Communications 1(4):660–670 DOI 10.1109/TWC.2002.804190.
Imen T, Ahlem BH. 2021. A-RESS new dynamic and intelligent system for renewable
energy sharing problem. PeerJ Computer Science 7:e610 DOI 10.7717/peerj-cs.610.
Khan WA, Hamadneh NN, Tilahun SL, Ngnotchouye JMT. 2016. A review and com-
parative study of firefly algorithm and its modified versions. Chapter 13. London:
InTechOpen DOI 10.5772/62472.
Lambora A, Gupta K, Chopra K. 2019. Genetic algorithm—a literature review. In:
International conference on machine learning, big data, cloud and parallel computing
(COMITCon). 380–384 DOI 10.1109/COMITCon.2019.8862255.
Marappan P, Rodrigues P. 2016. An energy efficient routing protocol for correlated data
using CL-LEACH inWSN. Wireless Network 22:1415–1423.
Ning Z, Liu L, Xia F, Jedari B, Lee I, Zhang W. 2017. CAIS:a copy adjustable incentive
scheme in community-basedsocially-aware networking. IEEE Transactions on
Vehicular Technology 66(4):3406–3419.
Razzaque MA, Dobson . 2014. S. Energy-efficient sensing in wireless sensor networks
using compressed sensing. Sensors 14:2822–2859.
Sarkar A, Murugan TS. 2019. Cluster head selection for energy-efficient and delay-less
routing in wireless sensor network. Wireless Network 25:303–320.
Seng TL, Khalid M, Yusof R. 1999. Tunning of a neuro-fuzzy controller by genetic
algorithm. IEEE Transactions on Systems, Man and Cybernetics 29(2):226–236.
Singh JA. 2014. New LEACH-based routing protocol for energy optimization in wireless
sensor network. In: Proc. of international conference on computer and communication
technology (ICCCT’2014). 181–186.
Siva Shankar G, Ashokkumar P, Vinayakumar R, Ghosh U, Mansoor W, Alnu-
may WS. 2020. An embedded-based weighted feature selection algorithm for
classifying web document. Wireless Communications and Mobile Computing
DOI 10.1155/2020/8879054.
Spurthy K, Shankar TN. 2020. An efficient cluster-based approach to thwart wormhole
attack in adhoc networks. International Journal of Advanced Computer Science and
Applications 11(9):312–316.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 22/23
Toumia I, Hassine AB. 2021. A-RESS new dynamic and intelligent system for renewable
energy sharing problem. PeerJ Computer Science 7:e610 DOI 10.7717/peerj-cs.610.
Wu S, Cui T, Zhang X, Tian T. 2020. A non-linear reverse-engineering method for
inferring genetic regulatory networks. PeerJ 8:e9065 DOI 10.7717/peerj.9065.
Yang Y, Schumann M, Le S, Cheng S. 2018. Reliability and validity of a new accelerometer-
based device for detecting physical activities and energy expenditure. PeerJ Computer
Science 11(6):e5775 DOI 10.7717/peerj.5775.
Yee PL, Mehmood S, Almogren A, Ali I, Anisi MH. 2020. Improving the performance
of opportunistic routing using min-max range and optimum energy level for
relay node selection in wireless sensor networks. PeerJ Computer Science 6:e326
DOI 10.7717/peerj-cs.326.
Basith and Shankar (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.845 23/23
