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AE-Shelter: An Novel Anti-Eavesdropping Scheme
in Wireless Networks
Xuran Li∗, Hong-Ning Dai∗,QiuWang
∗, and Athanasios V. Vasilakos†
∗Faculty of Information Technology, Macau University of Science and Technology, Macau SAR
lxrget@163.com; hndai@ieee.org; qiu wang@foxmail.com
†Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Sweden
vasilako@ath.forthnet.gr
Abstract—To protect confidential communications from eaves-
dropping attacks in wireless networks, we propose a novel
anti-eavesdropping scheme named AE-Shelter. In our proposed
scheme, we place a number of friendly jammers at a circular
boundary to protect legitimate communications. The jammers
sending artificial noise can mitigate the eavesdropping capability
of wiretapping the confidential information. We also establish
a theoretical model to evaluate the performance of AE-Shelter.
Our results show that our proposed AE-Shelter scheme can
significantly reduce the eavesdropping risk without significantly
degrading the network performance.
I. INTRODUCTION
Wireless networks are vulnerable to eavesdropping attacks
due to the broadcast nature of wireless medium. Encryption
schemes that are implemented at the upper layers of the net-
work stack are typically used to protect the confidential com-
munications in wireless networks. However, the encryption
protocols may not be feasible for all types of wireless networks
due to hardware constraints such as the inferior computational
capability and the limited power of wireless nodes [1]. For
example, it is shown in [2] that one of light-weight encryption
schemes used in RFID-based anti-theft devices for cars can be
broken less than 6 minutes. The security can be enhanced by
using more sophisticated ciphers [3], which nonetheless are
impractical to wireless sensor networks (WSNs) or Internet
of Things (IoT) [4], [5] since they are often computational
intensive and power-consuming, inevitably increasing the cost
and the size of nodes (or tags).
Compared with encryption schemes implemented at the
upper layers of the network protocol stack, physical-layer
security schemes can potentially enhance the network security
while maintaining relatively lower cost [3], [6]–[8]. There is a
substantial body of studies on designing encryption algorithms
by exploiting inherent channel randomness characteristics be-
tween the transmitter and the receiver [9]–[11]. However, the
schemes are still resource intensive (i.e., intensive comput-
ing and high power consuming) and cannot be used to the
resource-constrained wireless networks such as WSNs or IoT.
Recently, protective jamming has received increasing re-
search attention [12]–[15]. Such techniques aim at reducing
the eavesdropping capability of wiretapping the confidential
information without significant increment of the resource
consumption. However, most of these schemes can only be
applied to specific scenarios. For example, the work of [12]
Fig. 1. AE-Shelter is used to protect confidential communications (for
simplicity, we only draw several jammers).
only considers using at most 2 jammers while [13] makes
an assumption that Gaussian channel was used. Besides, the
jamming scheme proposed in [14] can only be used in Wireless
Local Area Networks (WLANs) while the scheme proposed in
[15] is mainly targeted for IoT. To the best of our knowledge,
there is a lack of protective schemes, which can be applied to
various scenarios.
In this paper, we propose a novel anti-eavesdropping scheme
named Anti-Eavesdropping-Shelter (AE-Shelter) to protect le-
gitimate communications from eavesdropping. In particular,
we place multiple jammers at a circular boundary around
the protected area, in which legitimate communications can
take place. The jammers emit jamming signals to prevent
eavesdroppers from wiretapping confidential information. Take
Fig. 1 as an example. In this scenario, a source node Alice
is transmitting confidential data to a destination Bob while
a malicious eavesdropper Eve is attempting to wiretap the
transmission between Alice and Bob. In our AE-Shelter, N
jammers that are sending artificial noise signals are uniformly
distributed at the border of the circle, which provides a virtual
shelter to protect the confidential communication between
Alice and Bob.
Our proposed AE-Shelter has many merits compared with
other existing anti-eavesdropping schemes. Firstly, AE-Shelter
is less resource-intensive (i.e., no extensive computing re-
source needed) and it does not require any modifications on
existing network infrastructure or wireless nodes. Secondly,
AE-Shelter is jamming-efficient. In particular, given the same
number of jammers as other existing jamming schemes, AE-
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Fig. 2. Network model of AE-Shelter
Shelter has the larger anti-eavesdropping area than other exist-
ing jamming schemes. This is because the circle has the largest
coverage area with the given circumference compared with
other shapes (recall that jammers are uniformaly distributed at
the circle). Thirdly, AE-Shelter is so general that it can be used
in various scenarios. This is due to the fact that the protected
area in any shapes can be fully contained within a circle [16].
In this paper, we also develop a theoretical framework to
quantitatively evaluate the effectiveness of our proposed AE-
Shelter. The primary research contributions of this paper can
be summarized as follows.
•We propose AE-Shelter to protect confidential commu-
nications from eavesdropping attacks. AE-Shelter is so
general that it can be used in various scenarios.
•We introduce the eavesdropping risk and the transmission
probability of legitimate communications to evaluate the
effectiveness of our propose AE-Shelter.
•We establish a theoretical model to analyze the eaves-
dropping risk and the transmission probability based on
stochastic geometry [17].
•Our numerical results also show that AE-Shelter can
significantly decrease the eavesdropping risk compared
with non-AE-Shelter scenario and meanwhile maintains
low decrement on the transmission probability.
The remaining paper is organized as follows. We first
present the system model in Section II. Section III then
presents the analysis of the eavesdropping risk. We next inves-
tigate the impacts on legitimate communications in Section IV.
Section V presents the analytical results. Finally, we conclude
the paper in Section VI.
II. PRELIMINARIES
A. Our approach
Fig. 2 illustrates a typical application scenario where our
AE-Shelter can establish a virtual fence preventing mali-
cious eavesdropping attacks. In particular, we consider the
circular shelter with radius of R, in which legitimate users
are randomly distributed according to homogeneous Poisson
point process (PPP) with density λ. There is an eavesdropper
Ewho is laway from the border of the shelter and is
trying to wiretap the confidential communications within the
shelter. Note that the eavesdropper can successfully wiretap
the legitimate communications if and only if the legitimate
transmitters fall in the eavesdropper’s eavesdropping region,
which is essentially the intersection of the large circle with
radius Rand the small circle with radius de(i.e., the dark blue
shade region as shown in Fig. 2). We define the radius deof the
small circle as the eavesdropping range of the eavesdropper.
The detailed calculation of eavesdropping range dewill be
given in Section III-B.
In our AE-Shelter scheme, we uniformly deploy a number
of protective jammers at the boundary of the shelter. The pro-
tective jammers emit artificial noise to prevent eavesdroppers
from wiretapping confidential information. In this manner, the
eavesdropping risk will be minimized.
B. Channel model
In this paper, we assume that the radio channel is mainly
affected by Rayleigh fading and path loss, which is more
practical than that in [13], [14]. Let Ptbe the transmitting
power of legitimate transmitters. Then, the received power at
a distance rfrom a transmitter is Pthr−α, where the random
variable hfollows an exponential distribution with mean 1
μ,
which we represent as h∼exp(μ),andαis the path loss
factor.
C. Upper bound on the number of jammers
In AE-Shelter scheme, there is a question - how many
jammers should be deployed at the border of the shelter?
Theoretically, we can deploy as many jammers as possible
at the boundary of the shelter, which however is not cost-
effective and can also cause the interference to legitimate com-
munications. In this paper, we consider two constraints when
we deploy jammers: i) the distribution of jammers should be
uniform so that we can offer an omnibearing protection since
we have no pre-knowledge of the eavesdropper’s location;
ii) any two adjacent jammers should be separated in a large
enough space to ensure the cost-effectiveness. However, it
is not trivial to solve the second constraint. In particular,
we introduce the jamming range of each jammer1,whichis
denoted by r0, as shown in Fig. 2. Then, we assume that the
jamming region (i.e., the circle with radius r0as shown in Fig.
2) of each jammer does not overlap with those of its adjacent
neighbors.
Note that it is essentially tight to deploy jammers with
consideration of the above two constraints together. We can
obtain the upper bound on the number of jammers based on
this tight placement of jammers. In practice, we shall distribute
the jammers in a looser manner at the boundary of the shelter.
For example, we shall avoid deploying jammers at the site
with obstacles.
We next derive the upper bound on the number of jammers
based on this tight placement. As shown in Fig. 2, it is obvious
that the maximum number of jammers depends on the size of
the shelter and the size of the jamming region. In practice, the
size of the shelter the size of the jamming region. Therefore,
the maximum number of jammers denoted by Nis bounded
by 2π
2θ=π
θ.
1Without loss of generality, we also assume each jammer has the same
setting.
IEEE ICC 2017 Ad-Hoc and Sensor Networking Symposium
According to the triangular relation of the ΔACJ as shown
in Fig. 2, we obtain the angle θas follows,
θ=arcsin|CJ|
R,(1)
where |CJ|is equal to the r0.
We next derive the jamming range r0. According to the
channel model defined in Section II-B, we have Pj·h·r−α,
where Pjis the emitting power of a jammer. We carefully tune
Pjso that the receiving power at distance ris no lower than
a threshold Tj. Thus, we have
Pj·h·r−α≥Tj.(2)
Inequality (2) can be represented as,
r≤Pj·h
Tj1
α
.(3)
We define the right-hand-side (RHS) of Inequality (3) as
rh=Pj·h
Tj1
α, which is a random variable since his a
random variable. We then derive the r0as follows,
r0=E(rh)=EPj·h
Tj1
α=1
α·Pj
μTj1
α
·Γ1
α,
(4)
where E(·)denotes the expectation and Γ(·)denotes the
standard gamma function.
III. EAVESDROPPING RISK
A. Eavesdropping probability
To measure the eavesdropping risk, we introduce the eaves-
dropping probability [15] denoted by PE.
Definition 1: Eavesdropping Probability PEis the proba-
bility that at least one transmission has been wiretapped by
an eavesdropper.
According to the definition of the eavesdropping probability,
we have PE=1−P(Y=0),whereYis a random
variable representing the number of transmitters wiretapped
by an eavesdropper. Since legitimate transmitters are randomly
distributed according to PPP with density λ,wehaveP(Y=
0) = e−λ·S,whereSrepresents the area of the eavesdropping
region.
As shown in Fig. 2, the area of eavesdropping region S
can be calculated according to the circle-circle intersection. In
particular, we have,
S=R2arccos x
R−xR2−x2
+de2arccos L−x
de
−(L−x)de2−(L−x)2,(5)
where we represent L=R+lfor simplicity, x=L2−d2
e+R2
2L
and dedenotes the eavesdropping range. As shown in Fig. 2,
deis the radius of the eavesdropping region. We then derive
dein the following section.
(
(a) Case 1 mis even
(b) Case 2 mis odd
Fig. 3. Two cases of different m
B. Eavesdropping range
As shown in [12], an eavesdropper can successfully decode
the information from transmitters if and only if the signal-to-
interference-noise ratio (SINR) of the eavesdropper, denoted
by SINRe, is no less than a given threshold Te.Inother
words, the following condition is satisfied,
SINRe=Pt·h·r−α
σ2+Ie
j
≥Te,(6)
where Ptdenotes the transmitting power, σ2denotes the
Gaussian noise level, and Ie
jis the cumulative interference
from all the jammers to the eavesdropper.
Let LHS be equal to RHS in Eq. (6). We then have the
eavesdropping range de(i.e., the maximum eavesdropping
distance) as follows,
de=EhPt
(σ2+Ie
j)Te1/α
=Pt
μ(σ2+Ie
j)Te1/α
.(7)
We next derive Ie
jin the following section.
C. Interference of of Protective Jammers
To simplify our analysis, we ignore the interference from
the jammers outside the eavesdropping region since they are
far from the eavesdropper and have less impacts on the
eavesdropper. We first need to bound the number of jammers
falling into the eavesdropping region. As shown in Fig. 2,
the number of jammers falling in the eavesdropping region,
denoted by m, is bounded by
β
θ=⎡
⎢
⎢
⎢
arccos R2+L2−d2
e
2RL
θ⎤
⎥
⎥
⎥
,(8)
where βis calculated according to the triangular relation of
the ΔACE as shown in Fig. 2 and L=R+l.
We next calculate Ie
jaccording to different cases that the
jamming regions are included in the eavesdropping region. In
particular, we have:
Case 1 mis even:
As shown in Fig. 3(a), we denote the distance between each
jammer and the eavesdropper as rj(n),wheren=1,2, ..., m
2
IEEE ICC 2017 Ad-Hoc and Sensor Networking Symposium
(only a half of mdue to the symmetry). Then, we have,
rj(1) = L2+R2−2LR β−arcsin r0
R,
rj(2) = L2+R2−2LR β−3arcsinr0
R,
··· ,
rjm
2=L2+R2−2LR β−(m−1) arcsin r0
R.
Then the interference from jammers to the eavesdropper can
be expressed as follows,
Ie
j=2·Pj
μ
m
2
n=1
(rj(n))−α
=2Pj
μ
m
2
n=1 L2+R2−2LR β−(2n−1) arcsin r0
R−α
.
(9)
Case 2 mis odd:
As shown in Fig. 3(b), the distance between each jammer
and the eavesdropper can be expressed as follows,
rj(1) = L2+R2−2LR β−arcsin r0
R,
rj(2) = L2+R2−2LR β−3 arcsin r0
R,
··· ,
rjm−1
2=L2+R2−2LR β−(m−2) arcsin r0
R,
rjm+1
2=L2+R2−2LR β−marcsin r0
R.
Then the interference from jammers to the eavesdropper is
given by,
Ie
j=2Pj
μ
m−1
2
n=1
(rj(n))−α+Pj
μrjm+1
2−α
=2Pj
μ
m−1
2
n=1 L2+R2−2LR β−(2n−1) arcsin r0
R−α
+Pj
μr0m+1
2−α
.
(10)
More specifically, when m=1,wehaveIe
j=Pj(rj(1))−α.
It is shown in the above analysis that m,Ie
j,deand Sare
co-related with each other. Thus, we cannot obtain Sdirectly.
To solve S, we develop an algorithm (see Algorithm 1).
IV. IMPACTS ON LEGITIMATE COMMUNICATIONS
We then investigate the impacts of AE-Shelter on the legiti-
mate communications. In particular, we define the transmission
probability [15] denoted by PCas follows.
Definition 2: Transmission Probability is the probability
that a legitimate transmitter can successfully transmit with
another legitimate receiver.
To ensure the legitimate transmission, we require that SINR
at the legitimate receiver denoted by SINRcmust be no less
Algorithm 1 Calculate S
1: m←0,Ie
j←0;
2: Calculate de=Pt
μ(σ2)Te1/α
;{according to Eq. (7)}
3: while m<arccos( R2+L2−d2
e
2RL )
θ;{according to Eq. (8)}do
4: m←m+1;
5: Calculate Ie
jwith m;{according to two cases of m}
6: Calculate de=Pt
μ(σ2+Ie
j)Te1/α
;{according to Eq. (7)}
7: end while
8: Calculate S;{according to Eq. (5)}
9: return S;
than Tc, which is the threshold of the receiving power for a
successful reception. Thus, we have
PC(r)=P[SINRc≥Tc|r]=PPt·h0·r−α
σ2+It+Ic
j
≥Tc|r,
(11)
where It=
M
n=1
Pthnrn−αis the cumulative interference
from Mlegitimate transmitters and Ic
j=
N
k=1
PjhkR−αis
the cumulative interference from Njammers to the reference
receiver.
According to the exponential distribution formed by
Rayleigh factor hand the property that the sums of indepen-
dent exponential random variables follows Erlang distribution
[18], we have
Ic
j=EPjR−α
N
t=1
ht=PjR−α∞
0
(μx)Ne−μx
(N−1)! dx.
(12)
Following the similar approach in [19], we can have the
transmission probability PC(r)as follows,
PC(r)=exp
−Tcσ2
Ptr−α
0
+Ic
j·
M
i=1
riα
Tc+riα.(13)
Let PM
Cbe the averaged transmission probability in the
shelter with Mtransmitters, which can be calculated as
follows,
PM
C=R
0
PC(r)fr(r|M)dr
=exp−Tcσ2
Ptr−α
0
+Ic
j·R
0
fr(r)rα
Tc+rαdrM
.
(14)
Since the distance distribution can be expressed as fr(r)=
2πr
πR2=2r
R2[20] (recall that the receiver is located at the center
IEEE ICC 2017 Ad-Hoc and Sensor Networking Symposium
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Eavesdropping Probability PE
No AE−Shelter
AE−Shelter
(a) α=3,Pt=1
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Eavesdropping Probability PE
No AE−Shelter
AE−Shelter
(b) α=4,Pt=1
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Eavesdropping Probability PE
No AE−Shelter
AE−Shelter
(c) α=3,Pt=2
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Eavesdropping Probability PE
No AE−Shelter
AE−Shelter
(d) α=4,Pt=2
Fig. 4. Eavesdropping Probability PEwhen the the density of transmitters λvaries from 0.01 to 0.3.
of the shelter), PM
Cis expressed as follows,
PM
C=exp−Tcσ2
Ptr−α
0
+Ic
j·2
R2R
0
ri1+α
Tc+riαdrM
=exp−Tcσ2
Ptr−α
0
+Ic
j
·2Rα
2+α·2F11,1+ 2
α,2+ 2
α,−Rα
TcM
,
(15)
where 2F1is the Gauss hypergeometric function [21], which
is given by
2F1(a,b,c,d) = Γ(c)
Γ(b)Γ(c−b)×
1
0
ub−1(1 −u)c−b−1(1 −du)−adu.
Finally we have the transmission probability PCas follows,
PC=
∞
M=0
PM
C·p(M),(16)
where p(M)is the probability mass function of Mas defined
in in this equation: p(M)= (λπR2)M
M!e−λπR2.
V. R ESULTS
In this section, we analyze the effectiveness of AE-Shelter
and investigate the impacts of AE-Shelter on legitimate com-
munications. In particular, we first conduct numerical studies
to evaluate the eavesdropping probability PEin Section V-A
and then present the numerical results on the transmission
probability PCin Section V-B.
We choose the following common parameters when con-
ducting numerical evaluations: i) the radius of the shelter is
R=2; ii) the distance between the eavesdropper and the
shelter l=1; iii) the noise signal power σ2=0.01;iv)
the Rayleigh fading factor μ=1; v) the transmitting power
of jammers Pj=0.5, vi) the power attenuation threshold
of jammers Tj=0dB; vii) the SINR threshold of the
legitimate receiver and the SINR threshold of the eavesdropper
are Tc=5dB and Te=5 dB, respectively. Besides, we also
consider that the density of legitimate users λvaries form 0.01
to 0.3and the path loss factor αis ranging from 3to 4.
A. Effectiveness of AE-Shelter
We first investigate the effectiveness of AE-Shelter in terms
of eavesdropping probability PE. Fig. 4 presents the analytical
results. In each set of results, we compare AE-Shelter scheme
with No AE-Shelter scheme (i.e., no protection ). As shown in
Fig. 4, we can see that PEof No AE-Shelter scheme is always
higher than that of AE-Shelter in each group of analytical
results. This implies that our proposed AE-Shelter is quite
effective to mitigate the eavesdropping risk.
Besides, Fig. 4 also shows that the eavesdropping probabil-
ity PEis affected by the channel conditions, such as Rayleigh
fading and path loss effect, in both AE-Shelter scheme and No
AE-Shelter scheme. For example, when the path loss factor α
is increasing from 3to 4with the same transmitting power
Pt=1(if we align Fig. 4(a) and Fig. 4(b) together), the
eavesdropping probability PEdrops significantly in No AE-
Shelter scheme while it increases in AE-Shelter scheme.
Moreover, tuning the transmitting power Ptcan also affect
the eavesdropping probability PE. Take Fig. 4 as an example
again. When the transmitting power Ptincreases from 1
to 2 (aligning Fig. 4(b) and Fig. 4(d) together), we can
find that the eavesdropping probability PEincreases. This is
because the higher transmitting power Ptindicates the higher
SINR of legitimate communication resulting the higher risk of
eavesdropping. This implies that the appropriate power control
can also improve the security, which has been confirmed in one
of the previous studies [22].
B. Impacts on legitimate communications
We then analyze the impacts of AE-Shelter on legitimate
communications. In particular, Fig. 5 presents the numerical
results of transmission probability PC. Similarly, in each set of
results, we compare AE-Shelter scheme in contrast to No AE-
Shelter scheme in terms of PC. More specifically, it is shown
in Fig. 5 that PCof AE-Shelter scheme is just slightly lower
than that of No AE-Shelter in each set of analytical results.
This implies that our proposed AE-Shelter scheme has small
impacts on legitimate communications. This effect is more
noticeable when α=4.
Similar to the eavesdropping probability, transmission prob-
ability PCis also affected by the channel conditions. For
example, when the path loss factor αis increasing from 3
to 4with the same transmitting power Pt=2(aligning
IEEE ICC 2017 Ad-Hoc and Sensor Networking Symposium
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Transmission Probability PC
No AE−Shelter
AE−Shelter
(a) α=3,Pt=1
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Transmission Probability PC
No AE−Shelter
AE−Shelter
(b) α=4,Pt=1
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Transmission Probability PC
No AE−Shelter
AE−Shelter
(c) α=3,Pt=2
0 0.05 0.1 0.15 0.2 0.25 0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Density λ
Transmission Probability PC
No AE−Shelter
AE−Shelter
(d) α=4,Pt=2
Fig. 5. Transmission probability PCwhen the the density of transmitters λvaries from 0.01 to 0.3.
Fig. 5(c) with Fig. 5(d)), the transmission probability PC
increases in both AE-Shelter scheme and No AE-Shelter
scheme. Besides, adjusting the transmitting power Ptcan also
affect the transmission probability PC. For example, when
the transmitting power Ptis increasing from 1 to 2 (aligning
Fig. 5(b) and Fig. 5(d) together), the transmission probability
PCis also increasing. This is due to the fact that the higher
transmitting power Ptleads to the higher SINRcresulting
the higher transmission probability as indicated in [23].
VI. CONCLUSION
In this paper, we propose a novel anti-eavesdropping scheme
named AE-Shelter to mitigate the eavesdropping attacks in
wireless networks. In AE-Shelter, multiple jammers are placed
at a circular boundary around the protected area. The jammers
can emit jamming signals to prevent eavesdroppers from
wiretapping the confidential communications. Our analytical
results show that AE-Shelter can significantly decrease the
eavesdropping risk compared with No AE-Shelter scheme and
meanwhile maintains low impacts on legitimate communica-
tions. Besides, AE-Shelter also have other merits including 1)
no modification on existing wireless nodes, 2) less resource-
intensive and 3) general (can be used in various scenarios). In
the future, AE-Shelter is expected to be integrated with other
security schemes to further improve the system security.
ACKNOWLEDGEMENT
The work described in this paper was partially supported by Macao
Science and Technology Development Fund under Grant No. 096/2013/A3.
The authors would like to thank Gordon K.-T. Hon for his constructive
comments.
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IEEE ICC 2017 Ad-Hoc and Sensor Networking Symposium
