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Congestion Control in Wireless Sensor Networks
based on Support Vector Machine, Grey Wolf
Optimization and Differential Evolution
Hafiza Syeda Zainab Kazmi1, Nadeem Javaid1∗
1COMSATS University Islamabad
Islamabad, Pakistan
{zainab.kazmi13, nadeemjavaidqau}@gmail.com
∗Correspondence: nadeemjavaidqau@gmail.com; www.njavaid.com
Muhammad Imran
Kind Saud University
Saudi Arabia
dr.m.imran@ieee.org
Fatma Outay
College of Technological Innovation, Zayed University
United Arab Emirates
fatma.outay@zu.ac.ae
Abstract—Transmission rate is one of the contributing factors
in the performance of Wireless Sensor Networks (WSNs). Con-
gested network causes reduced network response time, queuing
delay and more packet loss. To address this issue, we have
proposed a transmission rate control method. The current node
in a WSN adjusts its transmission rate based on the traffic
loading information gained from the downstream node. Multi
classification is used to control the congestion using Support
Vector Machine (SVM). In order to get less miss classification
error, Differential Evolution (DE) and Grey Wolf Optimization
(GWO) algorithms are used to tune the SVM parameters. The
comparative analysis has shown that the proposed approaches
DE–SVM and GWO-SVM are more proficient than the other
classification techniques in terms of classification error.
Index Terms—Wireless Sensor Networks, Support Vector Ma-
chine, Transmission Rate, Congestion Control, Differential Evo-
lution, Grey Wolf Optimization
I. INT ROD UC TI ON
Wireless Sensor Network (WSN) consists of large number
of scattered sensor nodes. The data sensed by the sensor nodes
is sent to the sink or a base station. Sensor nodes are being
used for:
•animal tracking,
•flood detection,
•forecasting of the weather data,
•monitoring of patients, and
•vehicle monitoring.
Sensor nodes independently perform some sensing task and
carry out some processing. These sensor nodes communicate
with each other in order to forward the collected data to the
sink node. Some nodes act as relay nodes. Relay nodes are
used to collect the sensed data and route the data to the sink.
WSNs are prone to communication failures. Sensor nodes have
the ability to work in harsh environments. However, sensor
nodes have the following constraints [1]:
•limited battery time,
•less memory, and
•fast energy depletion.
Large number of sensors in a wide geographical area
provides better accuracy. Congestion at a node occurs if the
arrival of data packets at a particular node are greater than
the number of outgoing data packets. Congestion can cause
packet loss and reduced response time. Response time of the
network is described as the amount of time needed for a
packet transmission from a sender to a receiver. Response
time decelerates with the reduced network throughput in a
congested network. Special considerations are required to deal
with congestion in a WSN. Several transmission rate control
mechanisms have been proposed in the past years. Congestion
control has been tackled by the adjustment of transmission
rate at each node [2]. The incoming and outgoing rate of data
packets can be handled in order to avoid retransmission and
packet loss. Congestion should be controlled at each hop to
avoid the problem of packet loss [2]. Mechanisms to detect
and avoid congestion can serve the purpose. Data mining al-
gorithms have been used to recognize such complex problems
and make smart decisions. Learning techniques are categorized
as supervised learning and unsupervised learning that works
on labeled and non-labeled data, respectively. For the problem
mentioned earlier, several classification methods can be used to
classify the data and predict the right amount of transmission
rate of sensor nodes in a WSN. Classification methods like
Support Vector machine (SVM), k-Nearest Neighbor (k-NN),
Naive Bayes, Neural Networks (NN) and Decision Tree can
be used to classify such data. The optimization algorithms like
2019 Wireless Days (WD)
978-1-7281-0117-0/19/$31.00 ©2019 IEEE
Genetic Algorithm (GA), Harmony Search Algorithm (HSA),
Grey Wolf Optimization (GWO), Differential Evolution (DE),
Firefly algorithm (FA) and Particle Swarm Optimization (PSO)
can be used to optimize the classifier parameters in order
to accurately classify the data. To avoid the problem of
congestion, a huge amount of packet re-transmissions, fast
energy depletion and reduced throughput in WSN, a reliable
transmission rate adjustment methodology is required. In [2],
the authors have shown high classification error using GA
and their proposed technique does not sufficiently reduce the
amount of re-transmitted packets. Better accuracy has been
achieved using SVM in the presented scenario in [2]. We have
used the same classification method SVM to avoid congestion.
However, we have tuned the SVM parameters using DE and
GWO algorithms. This is because DE-SVM and GWO-SVM
are capable of yielding better results than other classification
methods. In this paper, we present two novel methods to
avoid the congestion at every hop in a WSN. The congestion
problem is tackled by adjusting the transmission rate using
Support Vector Machine (SVM). The parameters of SVM
are tuned using Differential Evolution (DE) and Grey Wolf
Optimization (GWO) algorithms. We have used these classi-
fiers because they have the ability to produce better results
than other classification methods. The paper is organized as
follows: Section 2 consists of the related work. Section 3
describes the strategy for transmission rate adjustment. Section
4 provides the proposed method which consists of a brief
background of SVM and the transmission rate adjustment
based by SVM tuned by Differential Evolution (DE) and Grey
wolf Optimization (GWO-SVM). The performance evaluation
and comparison of the presented work with other classifiers
is presented in Section 5. Finally, the paper is concluded in
Section 6.
II. RE LATE D WOR K
The problem of congestion in WSNs is tacked in [2] by
adjusting the transmission rate at current node. The node
adjusts its transmission rate by taking buffer occupancy ratio
and congestion degree estimate from the upstream node to
avoid congestion and improve the network throughput. Mul-
ticlassification is done by Support Vector Machine (SVM).
The authors have used Genetic Algorithm (GA) for parameter
tuning. The parameters adjusted for all SVMs are acceptable
error, penalty ratio and deviation of Gaussian kernel function.
Authors of [3] have proposed a clustering routing protocol
in WSNs. The method used to enhance the performance
and network lifetime is a three-level hybrid algorithm. The
Multilevel Hybrid Protocol (MLHP) combined tree-based
techniques. At level one, cluster heads are selected, whereas
Grey Wolf Optimization is used for data transfer. To save
energy, nodes select the best route using GWO. At level tree,
distributed clustering is proposed. MLHP gives comparatively
more residual energy, more stability and improved network
lifetime in WSNs. Finding location of unknown nodes is an
important issue to be tackled. GWO [4] can be used for
localization problems. Node localization problem articulates
using range-based technique to calculate the coordinates of
unknown nodes using the positions of the known nodes. The
known nodes are called the anchor nodes which have a GPS
device. Using the GPS device, the anchor nodes determine
their positions. GWO gives better performance in terms of less
computation time and success rate of localized nodes. It can
be combined with other heuristic algorithms for finding the
location of nodes. Deployment of sensor nodes in unreceptive
environments causes the unreliable data collection. To gain
the accurate information, anomaly detection mechanisms have
been proposed earlier [5]. In order to make decisions from
the gathered data, it is noteworthy to detect anomalies in a
sensor network. Discovering anomaly is an extensive process
to determine its variance in behavior than the expected perfor-
mance. Authors of [5] took the initiative to solve the one-class
classification issue. The issue of anomaly detection is resolved
by One Class SVM (OCSVM). Support vector machine has
been proven to be the efficient classification method. Radial
base function can be used as kernel in OCSVM. Optimization
of hyperparameters is done using OCSVM for the core purpose
of anomaly detection. Authors of [6] have catered the fault
identification by initially classifying the sensor data using
SVM. The sensor faults are detected using the proposed
Online Sparse Least Squares SVM (OS-LSSVM). The features
of faults are extracted using Error Correcting Code SVM
(ECOC-SVM). The initial characteristics are separated, and
the fault states are classified. ECOC-SVM and OS-LSSVM
are considered to be highly efficient for real-time requirements
of fault identification and prediction. Sensor location is a key
element that contributes in the performance of WSN because
most of the applications in wireless sensor network domain
need the known location of sensor nodes. Several optimization
algorithms have been used to reduce the localization error
of sensor nodes. Authors of [7] have used metaheuristics
to solve this optimization problem. Optimization algorithms
like, Particle Swarm Optimization (PSO), Firefly Algorithm
(FA), Grey Wolf Optimization (GWO) algorithm are used to
estimate the position of sensor nodes. The localization problem
is resolved by minimizing the localization error using efficient
optimization algorithms. GWO comparatively worked better
and reduced more error than other algorithms. As anchor
(nodes with known position) nodes are used to estimate the
location of other sensor nodes, transmission range should be
increased to localize more targets. However, it takes more
computation time. As sensors have less energy and their
energy depletes faster, providing a better network lifetime is
challenging in WSNs. According to [8], GWO outperforms
other optimization algorithms. It gives more accuracy and
most importantly, GWO takes less execution time in an energy
constrained environment.
III. TRANSMISSION RATE ADJUSTMENT
Congestion occurs in wireless sensor networks when the
number of incoming packets at a sensor node are more than
the number of outgoing packets. 100 nodes were randomly
deployed [2] in an area of 100m*100m with a sink and the
congestion information was stored as shown in Fig. 1. The
authors of [2] controlled the congestion on each hop. Trans-
mission rate is increased or decreased based on the channel
information of the downstream node. An awareness packet is
sent from each node to the upstream node regarding the traffic
information. Here, upstream node is the one from which the
data is being received, whereas, downstream node refers to
the node that will receive the data. Normalized queue length
(4B) and congestion degree (4C) are referred as traffic
loading. Based upon these two, traffic loading information
is estimated at each sensor node. Normalized buffer size at
any node v is the ratio of number of packets in queue and
buffer size. Congestion degree is calculated as the ration of
average processing time of packs and the interval of arrival
time of two adjacent packets. If the current node has more
traffic loading than the downstream node, then there is a need
of increased transmission rate. Clearly, if the buffer occupancy
of the current node is greater than the buffer occupancy of the
downstream node, the current node is more congested and
it should increase the data transmission rate. 4Band 4C
determine the change in buffer and congestion whereas, 4R
represents the increased or decreased data transmission rate.
The amount of data transmission is determined on the basis
of traffic loading information. The packet loss or number of
retransmission of packets is determined using the values of
4B,4Cand 4R. The data transmission rate which gives
the less amount of packet loss is selected.
IV. PROPOSED TECHNIQU E
A. Transmission rate adjustment based on DE-SVM and
GWO-SVM
Support Vector Machine: Suppose we have a data space
X and we have to classify the data in two classes. We have
d1, d2 . . . , dk data points or the training points with labels
y1, y2 . . . , yk. we need to classify them in classes C or C1.
The prediction is made whether the data point d belongs to
a particular class or not. SVM can work efficiently on this
problem. SVM [1] is used to separate the hyperplane optimally
to classify the input data into positive or negative class. It
produces the supreme distance between the data and the plane.
A kernel function is used in non-linear classification to map the
low-dimensional feature space classification data into a high-
dimensional feature space. SVM [16] is a supervised learning
machine that classifies the objects by finding a hyperplane. The
hyperplane segments or divides the objects and determines in
which category the object lies. Non-linear classification is done
by changing the kernel function and generating hyperplane
lines using Gaussian Radial Basis (RBF). SVM [17] uses
different parameters like Penalty, Loss (loss function i.e. hinge
and squared hinge), Dual (for optimization problem), Tol
(for stopping criteria) and Random state (to generate random
number). We have used the data set provided by [2] of
100 randomly deployed sensor nodes and used GWO and
DE algorithms for SVM parameter tuning. The steps of the
proposed work are taken as follows:
1) For sensor nodes, retransmission values are determined
using the provided values of 4B,4Cand 4R
2) The data is divided into independent variables and
response variable that are 4B,4C,4Rand the num-
ber of retransmission packets, respectively. The (4B),
(4C) and (∆R) are used to interpret the retransmission
values.
3) 80% and 20% data is used as training and testing data.
SVM is designed for each retransmission value. Zero
retransmission data values and other data values are
labeled with 1 and -1, respectively. Five SVMs are
designed for five retransmission values.
4) Grey Wolf Optimizer is used to tune SVM parameters.
The adjusted parameters are penalty ratio (C), acceptable
error and the deviation of the gaussian kernel function.
Maximum iterations and number of search agents taken
are 50 and 5, respectively. GWO depicts the same mech-
anism as grey wolves hunting. Grey wolves always hunt
in a pack. Each pack consists of four types of wolves
that are alpha, beta, delta, and omega. Alpha wolves are
known to be the leaders, the dominant members or more
accurately the decision makers. Beta wolves support the
alpha wolves and help them in decision making. Delta
wolves follow the commands of alpha and beta. Omega
are not considered an important entity. With a good
hierarchy, each pack successfully hunts the prey. They
track the prey, encircle and then harass it and attacks
the prey when it attempts for self-defense. The pseudo
code of GWO is given below in algorithm 1.
•Social Hierarchy: Social hierarchy of grey wolves
is distinguished into alpha, beta and delta which are
considered as the best or optimum solution, second
best and third best solution, respectively. Here the
goal is to get a required solution or prey.
•Encircling Prey: It includes the encircling of a prey
for an optimal solution. The values of A and C
coefficient vectors can be adjusted in order to reach
near the best agent.
•Hunting: The core of GWO algorithm is hunting.
It means to move towards the solution and up-
dating the alpha solution. With the alpha score,
beta and delta can calculate their positions. The
omega wolves are the remaining solutions and up-
date themselves in reference with alpha, beta and
delta solutions.
•Attacking Prey (exploitation): When the prey stops
moving, the wolves attack the prey to finish the
hunt. The fluctuation of the coefficient vector A is
decreased by a. The random value A [-2a, 2a] where
a is decreased from 2 to 0. With the operators, GWO
search agents can update their positions using alpha,
and delta positions.
•Search for prey (exploration): Random population is
generated, and the position of prey is estimated by
alpha, beta and delta wolves. The distance of solu-
tion from prey is updated. To highlight exploration
and exploitation, parameter a tends to decrease from
2 to 0.
5) Differential Evolution is also used to tune SVM pa-
rameters. The adjusted parameters are penalty ratio
(C), acceptable error and the deviation of the gaussian
kernel function. DE works the same way as GA. It
performs crossover, mutation and selection. It takes two
independent elements and accumulates the difference of
these two.The they are multiplied by the mutation factor
to generate a mutant element. The second step involves
making the trial elements same as the population rate to
perform crossover. The last step is known as selection
as it selects the elements estimated in the previous step
[18]. We have used DE for getting the suitable parameter
values of SVM. Algorithm 2 explains the DE algorithm.
Algorithm 1 Pseudo code of Grey Wolf Optimization
Require: Input:[parameters, minimum value, maximum
value]
1: Initialize the population X
2: Initialize a, A, C
3: Calculate the fitness of each search agent
4: Update alpha=the best search agent
5: Update beta= the second search agent
6: Update delta=the third search agent
7: while itertaion < M axIter ation do
8: for edoach search agent
9: State Update the position of current search agent
10: end for
11: Update a, A and C
12: Calculate the fitness of all search agents
13: Update alpha
14: Update beta
15: Update delta
16: itertaion + +
17: end while
18: Return the best search agent
Fig. 1 shows the system model of tuning of support vector
machine parameters using grey wolf optimization and differ-
ential evolution algorithms. The datasets [2] are divided into
train and test sets. In the train phase, GWO and DE are used to
obtain the SVM parameters. The fitness value for each solution
is estimated. The optimized parameters from GWO and DE are
used to re-train the SVM. Then, the errors of classification are
calculated which shows the amount of misclassification made
by the proposed methods.
V. SIMULATION RESULTS
The results section consists of two subsections: At first, the
proficiency of Support Vector Machine (SVM) is evaluated. In
the second section, the presented technique is compared with
other classification techniques based on Mean Square Error
(MSE), Mean Absolute Error (MAE) and Root Mean Square
Error (RMSE). In order to evaluate the performance of the
Algorithm 2 Pseudo code of Differential Evolution
Require: Input:[parameters, No. of iterations, crossover, mu-
tation]
Initialize the population X
2: for each individual j in the population X do
Choose three members n1, n2 and n3 such that,1>
n1, n2, n3 6N
4: Create a random integer i (1,N)
while itertaion < M axIter ation do
6: for each parameter i do
Calculate the fitness values of
8: all individuals
Create mutant vectors using mutation
10: strategy
Create trial vectors by recombining
12: noisy
vectors with parent vectors
14: Evaluate trial vectors with their fitness
values
16: end for
Select winning vectors as individuals in the new
18: generation
itertaion + +
20: end while
end for
22: Return the best values
proposed technique,we have performed simulations in python
3.7. Specifications of the system used are: 1.61 GHz processor,
8.00 GB RAM, 2.66 GHz processor base frequency and 8 MB
cache. We have used the same data set of readings as provided
in [2].
A. Proficiency of GWO-SVM and DE-SVM
We have taken total data of 400 inputs for simulations. The
data used for training phase and test phase are 80% and 20%
respectively. The SVM parameters are tuned using the GWO
and DE techniques. Maximum iterations and number of search
agents taken are 50 and 5 respectively.
Fig. 2 and 4 show that the obtained results from GWO-SVM
and DE-SVM match the actual data. The training data and real
data are represented by blue and green lines, respectively. Fig.
3 and 5 display the compliance of test and real data by red and
blue lines respectively. In both of the above figures, x-axis or
horizontal axis and y-axis or vertical axis display the available
data and amount of packet loss, respectively. We can conclude
from these overlapping lines that, the presented classifiers
produce better results. A contingency table or confusion matrix
is also calculated in order to get a glance of predictions.
The error matrix gives the visualization of errors being made
during classification by the classifier. All correct and incorrect
predictions are specified in a matrix. The matrix consists of
rows and columns and presents the instances in predicted and
actual class. The proposed techniques are made using five
SVMs, so the proposed confusion matrix is a matrix of five
F
u
n
c
t
i
o
n
INPUT
SVM parameters
Apply GWO and DE for
parameter tuning
Train SVM
Evaluate fitness of parameters Is stopping criteria
met?
Optimized Parameters
Retrain SVM
OUTPUT
Classi ca on
Accuracy
MeanSquare
Error
YES
NO
Training
Set
All dataset
Packet loss of each
data
Training
Set
Fig. 1. System Model
Fig. 2. Compliance of data in GWO-SVM
Fig. 3. Compliance of data in GWO-SVM
rows and five columns. The advantage of using the confusion
matrix is to have a clear idea of what types of errors the
Fig. 4. Compliance of data in DE-SVM
Fig. 5. Compliance of data in DE-SVM
classification model has made and how much data is predicted
accurately. The confusion matrices C1 and C2 of the applied
GWO-SVM and DE-SVM techniques are presented as follows:
C1 =
20 2 0 0 0
2 6 1 1 0
0 3 8 0 0
1 0 6 8 1
0 0 0 5 16
C2 =
20 2 0 0 0
3 6 1 0 0
0 3 8 0 0
1 0 6 8 1
0 0 0 6 15
The correctly predicted values are shown on the diagonals
of the matrices. The values that are predicted more than the
real data, and less than the real data are located as the upper
and lower triangular elements of the matrices respectively.
As shown in the error matrices, more than 70% data are
located on the diagonal which means more than 70% data
are accurately predicted. Upper triangular data shows higher
transmission rate in the node. To conclude, the techniques
correctly determined the amount of retransmission based on
inputs as the predicted values are mostly correct.
B. Comparison of GWO-SVM with other classifiers
The overall error is calculated to evaluate the quality of
the presented technique with other classification methods like
GASVM, Naive Bayes (NB), Random forest (RF), and k-NN.
The data taken in training and testing phases are similar in all
methods.
1) Support Vector Machine using Genetic Algorithm (GA-
SVM): Genetic Algorithm (GA) [5] is a search heuristic
inspired by the biological evolution. Individuals of GA are
termed as chromosomes. Multi classification is done using
SVM and the parameters are tuned using GA. The adjusted
parameters are penalty ratio, acceptable error in SVM and
the deviation of the Gaussian kernel function. Implementation
of GA is done using uniform crossover and mutation. The
values used for population size, crossover and mutation are
50, 0.7 and 0.3, respectively [2]. The error shows that genetic
algorithm efficiently adjusted the parameters and very well
classified the data. The confusion matrix C3 of GA-SVM is
given below:
C3 =
22 0 0 0 0
3 4 3 0 0
0 1 9 1 0
1 0 6 7 2
0 0 1 5 15
Fig. 6 shows that the obtained results from GA-SVM match
the actual data. The training data and real data are represented
by blue and green lines, respectively. Fig. 7 displays the com-
pliance of test and real data by red and blue lines respectively.
In both of the above figures, x-axis or horizontal axis and y-
axis or vertical axis display the available data and amount
of packet loss, respectively. The comparison of the presented
Fig. 6. Compliance of data in GA-SVM
Fig. 7. Compliance of data in GA-SVM
techniques with GA-SVM is shown in 10, 11, and 12 and from
the performance evaluation, we came to the conclusion that the
proposed DE-SVM and GWO-SVM produce less classification
error as compared to GA-SVM.
2) Random Forest: A Random Forest (RF) [14] includes
numerous different decision trees. Each decision tree analyses
and votes on how the feature must be is classified. New items
are classified based on voting done by the trees in the forests.
Number of estimators and random state are taken as 9 and
42, respectively. MSE, MAE, and RMSE of random forest are
displayed in fig. 10, 11, and 12. The bar plots display that
random forest works quite well on this data set. However,
random forest did not produce as much accurate results as
the proposed techniques. Fig. 8 displays the tree generated
using Weka tool. M5P is a well known binary regression model
in which the the last nodes produce continous attributes. A
standard deviation reduction or divergence metric is used to
construct a tree.
3) Naive Bayes: Naive Bayes [14] is an efficient supervised
learning algorithm that uses conditional probabilities to predict
an outcome. It works accurate in real world scenarios. Nave
Bayes is based on statistics and assesses each feature indepen-
dently in the data set. It deals with two features independently.
In this way, a firm correlation between the factors is made.
However, we have used Gaussian Nave Bayes (GaussianNB)
and checked the performance of this classifier on the given
problem. This class (GaussianNB) assumes the features to be
normally distributed. At first, we have scaled the features and
then classified them using gaussian nave bayes. MSE, MAE
and RMSE are displayed in fig. 10, 11, and 12, respectively.
Fig. 8. The Graph of M5P tree results
This classifier handles features independently and assumes that
the presence of a feature is unassociated to the presence of
other features. The results proved that naive bayes does not
work well on the given dataset because provided features are
related in our scenario i.e., transmission rate is dependent upon
the the features of traffic loading information.
4) k-Nearest Neighbor (k-NN): This classifier is used to
measure the difference based upon a distance function. It finds
the closest neighbors of an instance and assigns a class to the
instance based on voting [6]. The results depend upon the
number of neighbors selected. We have randomly selected the
number of neighbors and number of jobs 5 and 2, respectively.
The K value impacts the accuracy of the predictions. Fig. 9
shows the error for the predicted values of test set for all the
K values between 1 and 25. The reason behind low testing
performance of k-NN is that it overfits the data and produce
unreliable training predictions of observations if the data is
finite.
Fig. 9. Miss classification in k-NN
The amount of retransmission packets is obtained by using
the values of buffer occupancy ratio, congestion degree and
transmission rate. Before providing SVM with the available
data for predictive analytics, we should split the dataset into
training and testing sets. In classification, first the model is
Fig. 10. Classification errors comparison
Fig. 11. Classification errors comparison
constructed and then this model is tested. SVM works well
on small datasets, therefore, we have used it to predict the
amount of packet loss based on the available data i.e. 4B,
4Cand ∆R. Simulation results show that the proposed
technique GWO-SVM and DE-SVM outperform the other
classifiers. Comparative analysis of all classifiers is performed
taking into consideration on the same dataset, however, DE-
SVM has done classification more accurately with less errors
than all other methods. Proposed GWO-SVM presented the
Fig. 12. Classification errors comparison
second best results and proved to be a good classification
method in our scenario. As compared to other classification
methods like GA-SVM, random forest, naive bayes and k-NN,
DE is more robust, and it performs computations efficiently.
DE better classifies the continuous data and provides results
faster whereas GE provides good enough results on discrete
problems. The comparison results given in table I concluded
that the proposed techniques GWO-SVM and DE-SVM solve
the congestion problem and adjust the rate of transmission in
a better way.
TABLE I
ERRO RS OF A LL C LAS SI FIER S
Errors GA-
SVM
Random
Forest
Nave
Bayes
k-NN GWO-
SVM
DE-
SVM
MSE 0.425 0.463 0.664 0.458 0.412 0.387
MAE 0.325 0.400 0.58 0.333 0.312 0.312
RMSE 0.425 0.46 0.96 0.458 0.412 0.387
VI. CONCLUSION AND FUTURE WO RK
Many researches have alluded the efficiency of SVM clas-
sification method. This study aims to control congestion in
WSNs by adjusting the transmission rate. Congestion degree
and buffer occupancy ratio for different values of transmission
rate are used to obtain the amount of retransmission pack-
ets. The congestion problem is solved using DE and GWO
algorithms. As, it is difficult to classify the complex data,
SVM parameters are tuned using GWO and DE to reduce
the classification error. The simulation results show that the
proposed approaches efficiently deal with the complex data
and outperforms the GA-SVM, K-NN, Naive Bayes, and
Random Forest in terms of classification error. We aim to
apply other optimization algorithms for parameter tuning in
the future in order to get more accurate classification. We will
also introduce and classify different type of faults to decrease
sensor failures and handle network traffic appropriately and
we aim to consider more dynamic and practical test scenarios
in WSNs.
REFERENCES
[1] Shah, S.A., Nazir, B. and Khan, I.A., 2017. Congestion control algorithms
in wireless sensor networks: Trends and opportunities. Journal of King
Saud University-Computer and Information Sciences, 29(3), pp.236-245.
[2] Gholipour, M., Haghighat, A.T. and Meybodi, M.R., 2017. HopbyHop
Congestion Avoidance in wireless sensor networks based on genetic
support vector machine. Neurocomputing, 223, pp.63-76.
[3] Al-Aboody, N.A. and Al-Raweshidy, H.S., 2016, September. Grey wolf
optimization-based energy-efficient routing protocol for heterogeneous
wireless sensor networks. In Computational and Business Intelligence
(ISCBI), 2016 4th International Symposium on (pp. 101-107). IEEE.
[4] Rajakumar, R., Amudhavel, J., Dhavachelvan, P. and Vengattaraman,
T., 2017. GWO-LPWSN: Grey wolf optimization algorithm for node
localization problem in wireless sensor networks. Journal of Computer
Networks and Communications, 2017.
[5] Tran, K.P. and Huong, T.T., 2017, October. Data driven hyperparameter
optimization of one-class support vector machines for anomaly detection
in wireless sensor networks. In Advanced Technologies for Communica-
tions (ATC), 2017 International Conference on (pp. 6-10). IEEE.
[6] Deng, F., Guo, S., Zhou, R. and Chen, J., 2017. Sensor multifault
diagnosis with improved support vector machines. IEEE Transactions on
Automation Science and Engineering, 14(2), pp.1053-1063.
[7] Kaur, R. and Arora, S., 2017. Nature inspired range based wireless
sensor node localization algorithms. International Journal of Interactive
Multimedia and Artificial Intelligence, 4(6), pp.7-17.
[8] Shieh, C.S., Sai, V.O., Lee, T.F., Le, Q.D. and Lin, Y.C., 2017. Node
Localization in WSN using Heuristic Optimization Approaches.
[9] Zhang, Y., Liu, Y., Chao, H.C., Zhang, Z. and Zhang, Z., 2018. Classi-
fication of Incomplete Data Based on Evidence Theory and an Extreme
Learning Machine in Wireless Sensor Networks. Sensors, 18(4), p.1046.
[10] Goeschel, K., 2016, March. Reducing false positives in intrusion de-
tection systems using data-mining techniques utilizing support vector
machines, decision trees, and naive Bayes for off-line analysis. In
SoutheastCon, 2016 (pp. 1-6). IEEE
[11] Gupta, S., Mittal, M. and Padha, A., 2017, December. Predictive Analyt-
ics of Sensor Data Based on Supervised Machine Learning Algorithms.
In 2017 International Conference on Next Generation Computing and
Information Systems (ICNGCIS) (pp. 171-176). IEEE.
[12] Yun, S., Lee, J., Chung, W., Kim, E. and Kim, S., 2009. A soft
computing approach to localization in wireless sensor networks. Expert
Systems with Applications, 36(4), pp.7552-7561.
[13] Alam, M.N., Das, B. and Pant, V., 2015. A comparative study of
metaheuristic optimization approaches for directional overcurrent relays
coordination. Electric Power Systems Research, 128, pp.39-52.
[14] Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.C. and Hanzo, L., 2017.
Machine learning paradigms for next-generation wireless networks. IEEE
Wireless Communications, 24(2), pp.98-105.
[15] Swain, R.R. and Khilar, P.M., 2017. Composite fault diagnosis in
wireless sensor networks using neural networks. Wireless Personal Com-
munications, 95(3), pp.2507-2548.
[16] Abuassba, A.O., Zhang, D., Luo, X., Shaheryar, A. and Ali, H., 2017.
Improving Classification Performance through an Advanced Ensemble
Based Heterogeneous Extreme Learning Machines. Computational intel-
ligence and neuroscience, 2017.
[17] Shakeel, U., Jan, N., Qasim, U., Khan, Z.A. and Javaid, N., 2016, July.
DRADS: Depth and reliability aware delay sensitive routing protocol
for underwater WSNs. In Innovative Mobile and Internet Services in
Ubiquitous Computing (IMIS), 2016 10th International Conference on
(pp. 78-83). IEEE.
[18] Javaid, N., Majid, A., Sher, A., Khan, W. and Aalsalem, M., 2018.
Avoiding Void Holes and Collisions with Reliable and Interference-Aware
Routing in Underwater WSNs. Sensors, 18(9), p.3038.
[19] Ali, B., Sher, A., Javaid, N., Aurangzeb, K. and Haider, S.I., 2018.
Retransmission avoidance for reliable data delivery in underwater WSNs.
Sensors, 18(1), p.149.
[20] Ahmed, F., Wadud, Z., Javaid, N., Alrajeh, N., Alabed, M.S. and Qasim,
U., 2018. Mobile Sinks Assisted Geographic and Opportunistic Routing
Based Interference Avoidance for Underwater Wireless Sensor Network.
Sensors, 18(4), p.1062.
[21] Javaid, N., Hussain, S., Ahmad, A., Imran, M., Khan, A. and Guizani,
M., 2017. Region based cooperative routing in underwater wireless sensor
networks. Journal of Network and Computer Applications, 92, pp.31-41.
