Secure localization against malicious attacks on wireless sensor network

e. 
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 
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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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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
[1]. Y.S.I.F.Akyildiz,W.SuandE.Cayirci,“Asurveyonsensornetworks,” IEEE Communications Magazine, 
2002, vol. 40, no. 8, pp. 102–114. 
[2]. L.Doherty and K. Pister, “Convex position estimation in wireless sensor networks,” in Proceeding 
of IEEE International Conference on Computer and Communications Societies (INFOCOM), 2001, 
vol. 3, pp. 1655–1663. 
[3]. T. He and C. Huang, “Range-free localization schemes for large scale sensor networks,” ACM 
MobiCom, San Diego, 2003, pp. 81–95.
[4]. D. Niculescu and B. Nath, “Dv based positioning in ad hoc networks,” Telecommunication 
Systems, 2003, vol. 22, no. 1, pp. 267–280. 
[5]. C. H. Chang and W. Liao, “A probabilistic model for relative location estimation in wireless 
sensor networks,” IEEE Communications Letters, 2009, vol. 13, no. 12, pp. 893–895. 
[6]. O.M.R.Weber,E.MademannandS.Zeisberg,“Localizationtechniques for traffic applications based 
on robust wecols positioning in wireless sensor networks,” Proceedings of Workshop on Positioning 
Navigation and Communication (WPNC), 2012, pp. 215–219. 
[7]. J. Cao, “A localization algorithm based on particle swarm optimization and quasi-newton 
algorithm for wireless sensor networks,” Journal of Communication and Computer 12, 2015, pp. 
85–90. 
[8]. J. M.-S. . E. R.-I. . A. E.-R. . L. Rizo-Dominguez, “A comparative of range free localization 
algorithms and dv-hop using the particle swarm optimization algorithm,” in 2017 IEEE 8th Annual 
Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 2017, no. 
17489361, pp. 150–157. 
[9]. W. T. Y. Chen and R. Martin, “Attack detection in wireless localization,” in Proceeding of IEEE 
International Conference on Computer Communications (INFOCOM), 2007, pp. 1964–1972. 
[10]. S. U. M. Jadliwala, S. Zhong and C. Qiao, “Secure distance-based localization in the presence of 
cheating beacon nodes,” IEEE Transactions on Mobile Computing, 2010, vol. 9, no. 6, pp. 810–823. 
[11]. C. C. C. S. H. Fang and C. Wang, “Attack-resistant wireless localization using an inclusive 
ISSN 2354-0575
Journal of Science and Technology60 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
disjunction model,” IEEE Transactions on Communications, 2012, vol. 60, no. 5, pp. 1209–1214. 
[12]. L. S. J. Jiang, G. Han and H. C. Chao, “A novel secure localization scheme against collaborative 
collusion in wireless sensor networks,” Proceedings of Wireless Communications and Mobile 
Computing Conference (IWCMC), 2011, pp. 308–303. 
[13]. J. H. N. Bulusu and D. Estrin, “Gps-less low cost outdoor localization for very small devices,” 
GPS-less low cost outdoor localization for very small devices, 2000, vol. 7, no. 5, pp. 28–34. 
[14]. V. B. L. J. Yang, Y. Chen and V. Swaminathan, “Robust wireless localization to attacks on access 
points,” Proceeding of IEEE Sarnoff Symposium, 2009, pp. 208–212.
[15]. N. Kiyavash and F. Koushanfar, “Anti-collusion position estimation in wireless sensor networks,” 
in Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems, 2007, pp. 
1–9. 
[16]. Y. Z. Z. Li, W. Trappe, “Robust statistical methods for securing wireless localization in sensor 
networks,” in IPSN 2005. Fourth International Symposium on Information Processing in Sensor.
[17]. Wikipedia, Confidence interval, 2008, https://en.wikipedia.org/wiki/ Confidence interval.
BẢN ĐỊA HÓA AN TOÀN CHỐNG LẠI CÁC CUỘC TẤN CÔNG ĐỘC HẠI
VÀO MẠNG CẢM BIẾN KHÔNG DÂY
Tóm tắt:
Mạng cảm biến không dây (WSN) rất dễ bị lỗi vị trí do các cuộc tấn công độc hại vào các nút cảm biến 
làm sai lệch vị trí của các nút cảm biến, điều này sẽ dẫn đến lỗi trong quá trình nội địa hóa nút không xác 
định. Trong bài báo này, chúng tôi đề xuất thuật toán bản địa hóa để chống lại các cuộc tấn công độc lập 
và các cuộc tấn công thông đồng. Trong thuật toán, đầu tiên chúng tôi chọn ba nút tham chiếu ngẫu nhiên, 
sau đó sử dụng phương pháp phát hiện ba bên và khoảng tin cậy để loại bỏ các nút độc hại. Sau đó chúng 
tôi sử dụng thuật toán tối ưu hóa PSO để xác định vị trí nút không xác định, được gọi là (Secure localization 
algorithm against advanced attacks-SL4A). Thông qua kết quả mô phỏng, chúng tôi chứng minh rằng thuật 
toán đề xuất của chúng tôi có hiệu suất tốt hơn các thuật toán hiện có, về sự thay đổi của các nút độc hại 
và nhiễu, lỗi cục bộ hóa trung bình và độ phức tạp tính toán. 
Từ khóa: Wireless sensor network (WSNs), Secure Location, Independent Attack, Collusion Attack.