Abstract:
Wireless sensor networks (WSN) are very susceptible to location errors due to malicious attacks on
sensor nodes that distort the position of the sensor nodes, which will lead to an error during unknown
node localization. In this paper, we propose a localization algorithm to defend against independent
attacks and collusion attacks. In the algorithm, we first select three random reference nodes, then use the
trilateral detection method and the confidence interval to get rid of the malicious nodes. We then use the
PSO optimization algorithm to locate the unknown node, is called (Secure localization algorithm against
advanced attacks-SL4A). Through the simulation results, we prove that our proposed algorithm outperforms
the existing algorithms, in terms of the variability of malicious nodes and noise, average localization error
and degree computational complexity.

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ISSN 2354-0575
Journal of Science and Technology54 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
SECURE LOCALIZATION AGAINST MALICIOUS ATTACKS
ON WIRELESS SENSOR NETWORK
Vinh V. Le1,*, Dang Van Anh2, Nguyen Thi Thanh Hue2, Tran Do Thu Ha2
1 National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
2 Hung Yen University of Technology and Education
* Corresponding author: vinhlv.utehy@gmail.com
Received: 05/05/2020
Revised: 20/08/2020
Accepted for publication: 15/09/2020
Abstract:
Wireless sensor networks (WSN) are very susceptible to location errors due to malicious attacks on
sensor nodes that distort the position of the sensor nodes, which will lead to an error during unknown
node localization. In this paper, we propose a localization algorithm to defend against independent
attacks and collusion attacks. In the algorithm, we first select three random reference nodes, then use the
trilateral detection method and the confidence interval to get rid of the malicious nodes. We then use the
PSO optimization algorithm to locate the unknown node, is called (Secure localization algorithm against
advanced attacks-SL4A). Through the simulation results, we prove that our proposed algorithm outperforms
the existing algorithms, in terms of the variability of malicious nodes and noise, average localization error
and degree computational complexity.
Keywords: Wireless sensor network (WSNs), Secure Location, Independent Attack, Collusion Attack.
1. Introduction
In recent years, a number of algorithms have
been proposed for the secure localization of sensor
nodes, determining safe locations that are widely
used in health care, environmental monitoring,
and smart homes, IoT, and other commercial
applications [1], [2]. All of these WSN localization
algorithms have the same feature that they estimate
the actual location of nodes (anchors or beacons)
with location node (unknown or target).
The proposed PSO localization algorithms
do not take into account malicious attacks [3], [4].
When the beacon nodes are affected by the attacks
have a bad purpose, malicious nodes deliberately
enhance or weaken signal strength and affect the
accuracy of localization. Current localization
algorithms take into account anti-attack [5], [6]. In
[6], the localization algorithm based on minimum
mean square estimation and voting based is
proposed. In WSNs, the common attack methods
in localization are independent attack and collusion
attack [7], [8]. In [8], two methods are used based
on the exploration of beacon node locations that are
interfered with by malicious attack nodes: The ﬁrst
method ﬁlters out malicious beacons signals on the
basis of the consistency among beacons signals, the
second method accepts malicious beacon signals by
using the voting method to remove malicious nodes.
Propose combining clustering technique in attack-
resistant localization method [7]. The localization
method is to measure the received signal strength
(RSS) of any three points to make location
estimates and combine with the K-means clustering
method to eliminate malicious location. Based on
the clustering effect of localization results from not
attacked access points and based on the localization
results of attacked access points, it is divided into
two separate groups and achieve effective ﬁltering
of toxic point locations.
In this paper, we design a localization algorithm
against malicious attacks in two independent
attack and collusion attack environments: First, we
randomly select a set of three reference nodes and
use it with PSO position estimation algorithm. Next,
we use the conﬁdence interval method to get rid of the
malicious nodes. Finally, we use the PSO optimization
algorithm to estimate the location of the unknown
node with the nodes within conﬁdence intervals.
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Khoa học & Công nghệ - Số 27/Tháng 9 - 2020 Journal of Science and Technology 55
2. Network Model and Problem
In this section, we ﬁrst introduce the WSNs
node model. Next, we then introduced two
independent and collusion attack. Finally, we
investigate the effects of attacks on beacon nodes
in localization.
2.1. WSN Network Node Model
Figure 1. WSN network node model
Figure 1 shows our WSN nodes model
consisting of three types of nodes: 1) M is an
unknown node, 2) A, B and C are beacon or anchor
nodes, and 3) A’ is a malicious node. Where B and C
are benign nodes that have not been attacked, node
A is attacked by malicious node A’. Therefore, the
check node will assume that the position of node
A is at the position of node A’. The distance from
nodes A, B and C to the unknown node is called the
measurement distance.
Normally, when the beacon nodes sends
location information to an unknown node, the
unknown node receives a signal from the beacon
nodes and based on the signal strength, it converts
the measurement distance between the beacon
nodes and unknown node. Therefore, when the
beacon nodes are attacked by malicious nodes, will
lead to unknown node localization error.
2.2. Model Attack Location and Noise
In the presence of malicious attacks, location
deviation measurements from compromised beacon
nodes can fool sensors with unknown coordinates.
Malicious measurements that are injected into the
system by the attackers are the exception. The
solution of benign position estimates is generally
largely affected by malicious attacks. We allow
many independent and collusive attacks to transmit
misleading location information without restriction.
A) Collusion attack method: In Figure 2, the
beacon nodes B, C, and D signals send location
information to node A, because the node B, C, and
D are affected by the attack leading to the RSSI signal
wrong, so the beacon node A will be positioned to the A’
beacon node position for incorrect positioning results.
Figure 2. CollusionAttack model
B) Independent attack method: In Figure
3, the node B, C, and D signals send location
information to node A, since node B, C, and D can
be affected in part by independent attacks, credits
RSSI signal is stronger or weaker than the original
signal, leading to position A can be placed in
different positions A’, A’’ and A’’’ form big position
errors, independent attack model [10], using α and
β parameters to control and enhance RSSI signals.
Figure 3. Independent Attack model
2.3. Position Estimation Unknown Node with
Presence of Attacks
In the WSN network, the correct localization
of the network node will help routing, save energy,
and then maintain the life of the node and the
entire network. But it poses challenges because
the appearance of an exception attacker changes
the position of benign nodes, or increases and
decreases the signal strength. Therefore, methods
of location estimation and location measurements
from beacon nodes to unknown nodes position will
lead to localization errors. The issue of estimating
the location in the presence of malicious attackers
is as follows:
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Journal of Science and Technology56 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
Consider a WSN with N anchor nodes and
an unknown node. The location coordinates of the
anchor nodes i can be expressed as Zi = [xi yi]
T, 1,
2, ..., N, and the position of the unknown node is Z
0
= [x
0
y
0
]T .
2 2
0 0 0( ) ( ) ,
1, 2, , (1)
i i i id Z Z x x y y
i N
= − = − + −
=
Where di is the estimated distance between Zi and
Z
0
. id is the measured distance between Zi and Z0
according to RSSI.
Since the actual distance id is not known, the
objective of localization is to minimize the difference
between di and id . Therefore, the 0 0( , )x y of unknown
node is estimated by the following equation:
0
2
0
10 0
arg min
( , ) ( ) (2)
, y
N
i i
i
x y d d
x =
= −∑
where N is the number of anchor nodes used to
estimate the position of an unknown node. Equation
(2) uses the (Least Square-LS) method, which is a
mathematical optimization method [9], [10]. The
applied LS is to minimize the total square of the
location error.
Due to location estimation error, the position
0 0( , )x y in Equation (2) is often a multimodal
function. Therefore, we use the PSO optimization
algorithm to estimate the error location, then use
the CI conﬁdence interval method or the PSO
group method with the smaller value group. Then
calculate the average position from the benign
position estimates (not attacked by malicious nodes)
obtained from the conﬁdence interval method and
the PSO group. Average position is the estimated
position of unknown node.
3. Particle swarm optimization algorithm (PSO)
Our idea throughout the paper is to develop
the localization estimation algorithm based on the
PSO optimization paradigm. In this section, we
ﬁrst present details of how to implement the PSO
paradigm to estimate the unknown node’s location.
In PSO, all individuals in a population are seen as
particles in a N-dimensional solution space, the
ﬁtness value is determined by its location in the
search space.
Assume, in an N-dimensional objective search
space. Three N-dimensional vectors are used to
describe a particle i: (1) current location Xi = [xi1,
xi2, xiN]; (2) the previous location Pbesti = [pi1, pi2,
, piN] of the best ﬁtness; and (3) current velocity
Vi = [Vi1, Vi2, , ViN] . Besides, Gbest = [G1, G2, ,
GN] denotes the position of the best particle so far
(ie, Gbestd is the smallest of all Pbestid). At each
iteration k, the velocity vid and position Xid of each
particle are updated according to the following
equations.
1 1 2 2( 1) ( ) ( ) ( )id id id id d idV k V k c r Pbest X c r Gbest Xw
(3)
( 1) ( ) ( 1)id id idX k X k V k (4)
where, d =1,2, , N; i=1, 2, , K; and K is the
size of the swarm population, it is denotes iteration
number, r
1
and r
2
are random numbers between
[0,1], and c
1
=c
2
=2 are respectively the cognitive and
social learning parameters, ω is the inertia weight.
The inertial weight is added to element w of
the original PSO algorithm as in Eq. (5). The bigger
value of ω is beneﬁcial for particles to jump out
of local minimum points, the smaller value of ω
is favor the algorithm convergence. As originally
developed, ω often decreases linearly with the
number of iterations. Therefore, ω can be set
according to the following equation.
max
max
( )
(5)ini end end
T t
T
w ww w
where iniw is the initial weigh. endw is the ﬁnal
weight value, maxT is the total number of iterations,
and t is the current number of iterations. In previous
experimental studies, ω was often set from 0.9
reducing linearly to 0.4.
4. Secure Localization Algorithm Against
Advanced Attacks (SL4A)
Existing algorithms for estimating attack
resistance positions often use as many beacon nodes
as possible to estimate unknown node coordinates
[12], [8]. The idea behind the algorithm LS4A is that
we select three random points for an initial subset,
using the trilateral detection paradigm and the PSO
optimization algorithm to identify measurements in
conﬁdence intervals.
To ﬁnd the coordinates of a node requires
at least 3 distance measurements. Therefore, to
estimate the position of an unknown node from a
randomly chosen subset of 3 nodes, Ωi (Step 4). In
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Khoa học & Công nghệ - Số 27/Tháng 9 - 2020 Journal of Science and Technology 57
an attack network model, any node can be attacked,
so that to detect an attack beacon node, (Step 5) uses
the trilateral detection method to detect whether a
set Ωi has been maliciously attacked. If the set Ωi
is not attacked (Step 6) use the PSO algorithm to
ﬁnd the location estimate. Next (Step 7), calculate
the K value by the Equations (7), of all cases falling
into the conﬁdence interval e. When K = N, the
estimated location ofc 0Z is within the conﬁdence
interval of all the beacons, and its coordinates are
completely removed by malicious attacks.
1
( 1.96 1.96),1
where (7)
,0
N
i
i i
i
if e
K u u
otherwise=
− ≤ ≤ = = =
∑
If the value of K is greater than the threshold value
of t given (Step 8). Once a consistency set has
been identiﬁed, the algorithm uses all points in the
conﬁdence interval set to form the ﬁnal estimate ofc
0Z and it terminates (Steps 9). In our experiments,
we use the PSO optimization algorithm to calculate
the initial estimate from the Zi subset and for the
ﬁnal estimate obtained from the set within the
conﬁdence interval.
If the algorithm performs all iterations
and does not ﬁnd the conﬁdence interval have a
minimum size t. Then it will either declare a failure
or give an 0Z position estimate acceptable from the
largest conﬁdence interval (Steps 13).
Algorithm Secure Localization Algorithm Against
Advanced Attacks (SL4A)
1: Input: unknown node Z
0
, maximum number of
interations imax, Set L, conﬁdence interval CI,
threshold t;
2: Ouput: estimated positionc 0Z 0 0( , )x y ;
3: for iter=0 to imax do
4: Randomly select a subset Ωi of size 3 from L;
5: if TriEdgeCheck(Ωi) then
6: Call PSO-Based Localization Algorithm;
7: Calculate K, the number of points in the
conﬁdence interval with to the estimatedc
0Z in e;
8: if K > t then
9: return 0 0( , )x y ;
10: end if
11: end if
12: end for
13: return 0 0( , )x y ;
4.1. The Trilateral Detection Method: [13] is
derived from a beacon node detection method
developed from the theorem of a triangle. The
total 2 sides of the triangle are larger than the third
side, and the subtraction of 2 sides of the triangle is
smaller than the third side. Given a triangle ∆ABC
and the corresponding edges are a, b, c, the three
sides must satisfy a + b > c, a + c > b, b + c > a,
a − b < c, a − c < b and b − c< a.
Figure 4. The trilateral detection method
For example, in Figure 4 M is called an
unknown node or a measured node, A and B are
anchor nodes that provide positioning information.
M node is measured uses RSSI signal strength to
convert the distance between A and B anchor nodes
into dAM distance and dBM, and then use the known
distance dAB to perform trilateral detection, then
assess whether or not to meet the principles of a
triangle.
4.2. Conﬁdence Interval (CI): We assume that not
all beacon nodes are attacked by malicious nodes,
meaning that non-toxic distance measurement
errors are Gaussian random variables distributed
according to N(0,σ2), error variance is the RSSI
distance measurement technique in this paper. The
distance measurement error e computes the distance
between the actual position of with the reference
position (xi , yi) according to the Equations (8).
2 2
0 0( ) ( ) (8)i i ie d x x y y= − − + −
In statistics, the conﬁdence interval of the
probability sample means estimating the interval of
some of the overall parameters of the sample [17].
The conﬁdence interval is a distance estimation
method that uses a range to estimate a parameter.
This shows that the true value of this parameter
is likely to fall into the measurement result, the
ISSN 2354-0575
Journal of Science and Technology58 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
probability is called the level of Conﬁdence 1 - α,
α is the probability of not falling into the range.
For example, as shown in Figure 5 we apply the
conﬁdence interval method to measure errors in the
estimated position coordinates, 95% CI are a given
level taken from the Normal distribution N(0,σ2),
be within the standard deviation [-1.96e, 1.96e], of
which 1 - α = 95%; α = 5%. In other words, e is
in the CI conﬁdence interval with a probability of
more than 95%.
Figure 5. Example of 95% confidence interval
5. Simulation Evaluations
Two types of the simulation scenario with
network environment are 50x50. Total of 15 beacon
nodes including 3 to 7 malicious nodes; and 1
unknown node. Let α = 1.1 and β = - 10. Scenario
1, evaluates the influence of the collusion attacks
by changing malicious nodes, and Scenario 2,
evaluates the influence of the independent attacks
by changing Gaussian noise intensity. The program
is simulated for 10,000 times.
We compare the performance of the new SL4A
algorithm with current algorithms: LMS [16],
RANLD [15], IDM [11] and Cluster-NLS [14].
First, compare the secure location in the collusion
attack environment, in which the number of attack
nodes in the environment ranges from 3 to 7 nodes.
Next, compare the safe position in the collusion
attack environment, where the noise variation is
1, the attack intensity Alpha varies from 0.6 to 1.4
and β = 0. With α have strength from 0.7 to 1 is a
weakening attack, and α from 1 to 1.4 is a signal
booster attack.
5.1. Collusion Attack: Figure 6(a), under the
scenario of collusion attack by changing malicious
nodes. The average localization error of the
algorithm increases as the number of malicious
nodes increases. Because the number of malicious
nodes directly affects the power of collusion attacks.
When the number of malicious nodes is 3 and 4, the
Cluster-NLS algorithms have the smallest average
localization error. When the number of malicious
nodes is 5, 6 and 7, the SL4A and RANLD
algorithms are best against collusion attacks. Based
on the empirical results, it can be concluded that
when the number of malicious nodes is small in
collusion attack, the secure location algorithm using
group combination shows better localization ability.
Fig 6. Comparing secure localization algorithms against attacks:
Fig. 6(a) is collusion attack and Fig. 6(b-f) are independent attacks
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Khoa học & Công nghệ - Số 27/Tháng 9 - 2020 Journal of Science and Technology 59
5.2. Independent attack: Figure 6(b) to Figure
6(f) with the scenario of independent attacks by
changing Gaussian noise intensity. In the cases of
number of attack nodes changer from 3 to 7 nodes.
We can see that when the attack intensity α = 0.9
and α = 1.1, all algorithms are affected by the worst
attacks power. With different attack intensities, the
SL4A and RANLD algorithms have the smallest
average localization error. IDM algorithm randomly
chooses 5 nodes to estimate position. Therefore,
the average localization error in the middle. As
the signal strength increased, the Cluster-NLS and
LMS algorithms had average localization errors
increased accordingly.
6. Conclusions
In WSNs there is practically a lot of noise
caused by malicious attacks on anchor nodes. Most
localization algorithms are suffer from malicious
attacks, resulting in a large difference between
the estimated location and the actual location
of an unknown node. However, there are a few
localization algorithms that considers independent
attack mechanisms, due to inappropriate algorithmic
architectural design, the ability to resist malicious
attacks is limited and the complexity of the algorithm
is too high compared to hardware devices. In this
paper, we have proposed a anti-attack security
algorithms SL4A with stability and robustness,
algorithm focused on combating independent
attacks and collusion attacks. Compared to existing
secure localization algorithms, our two algorithms
resist malicious attacks and calculate the location
coordinates of the unknown node more accurately.
References
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