This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
Increasing Transmissioon Power for Higher Base-staation Anonymity
in Wireless
W
Sensor Network
Y
Yousef
Ebrahimi, Mohamed Younis
Department of Computer Science and Electrical Engineering
U
University
of Maryland, Baltimore County
Baltimore, Maryland, USA
yousef2, younis@umbc.edu
Abstract— In a wireless sensor network nod
des probe ambient
conditions and send the measurements over mu
ulti-hop routes to a
base-station. The base-station aggregates thee received reports,
and based on the findings it orchestrates a response either
autonomously or through consultation with a remote commend
center. Thus, the role of the base-station is so crucial that the
d if the base-station
network can be non-functional and/or isolated
breaks down or gets destroyed. No won
nder in a hostile
environment, an adversary will target the base-station to inflict
hat the base-station
the most damage to the network. The fact th
acts as a sink of all data traffic makes it vulneerable to attacks by
tracking packet transmission and detecting its location. This
t
analysis and
paper investigates a novel strategy to counter traffic
boosts the anonymity of the base-station. A sensor transmits at a
higher power in order to increase its numberr of neighbors and
confuse an adversary who is assessing the link
kability of nodes in
quest to identify the route to the base-station
n. Both analytical
and simulation results are provided to cap
pture the effect of
increased transmission power on the base-statiion anonymity.
I. INTRODUCTION
A growing list of applications has drawn innterest in wireless
sensor networks (WSNs) in recent years [1]. Typically a WSN
n
deployed to
is composed of a large number of sensor nodes
monitor their surroundings and report their measurements
m
to a
nearby base-station [2]. The base-statioon interfaces the
network to remote users and often task the sensors and
manage their operation. For example, senssors may detect a
target and informs the base-station, which inn turn aggregate all
data and assesses the fidelity of the deetection and even
performs target identification. Figure 1 show
ws example WSN
architecture. Sensor nodes are battery-opperated and their
lifetime is limited. Therefore, multi-hop traansmission of data
is a very popular optimization strategy inn order to cut on
energy consumption in communications and extend the
lifespan of nodes [2].
Most notable among the WSN appliications are those
serving in hostile environments, such as combat field
reconnaissance, border protection, and secuurity surveillance,
where the network may be subject to an addversary’s attacks.
Given the role that the base-station playys, it is the most
attractive target for an adversary who opts to
t inflict the most
damage to the operation of the WSN. Indeed, destroying the
base-station may deem the network useless.. The fact that the
base-station is the sink of all data traffic maakes it vulnerable.
Popular anonymous routing protocols thaat hide the node
identity would not be a sufficient counterrmeasure since an
adversary can track the individual wireless transmission and
employ traffic analysis techniques to follow the data paths [3].
Since all active routes ends at the base-station, the adversary
may be able to determine the location of the base-station and
launch targeted attacks in order to prevennt the base-station
from functioning properly.
b decoded, traffic
Since the intercepted packets cannot be
analysis would rely on correlating the radioo transmissions of
sensors in order to identify active links between pairs of
directly reachable nodes. Agggregating all knowledge about
these links can lead the adverssary to associate communicating
nodes, predict the data routingg paths, and identify the sink of
all traffic. To detect the indiviidual links, an adversary would
use angle-of-arrival (AOA)) and time-of-arrive (TOA)
estimation technology in order to determine the location of the
mission power. By knowing the
source and estimate the transm
power and location, the adveersary can then determine the
range and guess where the next
n
hop is. To counter such
potential threat, a number of techniques have been proposed in
a
of the base-station
the literature to boost the anonymity
[4][5][6]. Anonymity refers to the undelectability of the
node’s attributes, namely, its location, ID, and role. Most of
the published approaches rely on introducing changes in the
routing topology and traffic paattern in order to complicate the
adversary’s analysis and divergge it away from the base-station.
Unlike prior schemes, this paper explores a novel strategy
o the base-station. The idea is
for boosting the anonymity of
increase the transmission pow
wer of nodes so that the traffic
analysis applied by an adversarry becomes highly complex and
the level of uncertainty about the
t base-station location grows.
Pursuing longer transmission ranges increases the node degree
in the network, which represennts the cardinality of the set of
possible next hops for eachh transmission. We show that
increasing the node degree caauses an exponential growth in
the complexity of correlatingg the links among neighboring
nodes to determine the roouting path. In addition, we
analytically formulate the anonnymity as a function of the radio
range. Since boosting the transsmission power may impact the
network lifetime and increase signal interference, the derived
anonymity formulation can serrve in trading-off and guide the
designer setting the most suittable configuration parameters.
The simulation results furtther quantify the effect of
transmission power and connfirm the effectiveness of the
proposed strategy in protectingg the base-station.
Figure 1: An articulation of a WSN
N in a border protection setup
The rest of the paper is organized
o
as follows. The next
section describes the considerred system and threat models.
Section III discusses related work.
w
Section IV analyzes the
effect of the transmission range on the anonymity of the basestation. The validation results are discussed in Section V.
978-1-61284-233-2/11/$26.00 ©2011 IEEE
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
Finally, Section VI concludes the paper.
II. SYSTEM AND THREAT MODELS
This paper considers a WSN with a single base-station (BS)
node. Sensors send their readings periodically to the BS over
multi-hop routes. In other words, the BS is an in-situ user of
the network. An anonymous routing protocol is employed to
achieve unlinkable data delivery [7]. For example, packets
sent to BS are encrypted on a per-hop basis in order to protect
the privacy and integrity of the data. In addition, packet
headers are encrypted to hide the identity (achieve ID
anonymity) of communicating nodes. It is assumed that
contemporary techniques are employed to make the BS
visually hard to identify. These techniques are very popular in
some applications that involve military equipments and
uniform. The BS also avoids exposing itself by long-haul
transmissions and limits its involvement in control traffic, e.g.,
new route discovery or route extension, so that it does not
become distinguishable from sensors.
An adversary is assumed to be remotely located and tries
to detect and destroy the reconnaissance capabilities provided
by the WSN. The goal of the adversary is to identify the
location and attack critical nodes in the sensor network;
namely the BS. The adversary employs proxy antenna within
and in the vicinity of the deployment area and eavesdrops on
the radio transmissions in order to understand the network
structure and identify the BS. Every transmission is assumed
to be intercepted by at least three antennas so that the
adversary can triangulate and find the location of the source
node [8][9]. The adversary can detect and capture packets but
cannot apply cryptanalysis to decrypt the contents of the
packets. The adversary is basically a passive observer of the
network and does not inject his packets into the network and is
assumed to take sufficient precautions to avoid detection.
III. RELATED WORK
Since anonymity is a qualitative measure, some research
efforts have been dedicated to defining means to quantify it
and assess the effectiveness of anti-traffic analysis techniques.
The adversary success depends on the insight provided by the
collected information while observing the network. Therefore,
information theoretic models have been deemed effective
[6][10][11]. These models were mainly designed to fit the
anonymity of the source and have been recently extended to
suit the sink of data, i.e., the BS, in WSNs [12]. The GSAT
model proposed in [3] is specifically designed for assessing
the location anonymity of the BS. However, it does not
converge without additional means, e.g., visual inspection.
This shortcoming has been addressed in [12]. We employ the
evidence theory models [6] in studying the impact of
transmission power on the BS anonymity.
Protecting the identity of the BS is usually achieved
through anonymous routing mechanisms which encrypt the
packets to cipher the routing information [7]. On the other
hand, hiding the structure of the routing topology has been the
main methodology for sustaining the anonymity of the BS
against traffic analysis attacks. Since an adversary that pursues
passive listening will eventually gather sufficient information
to detect the location of the BS, a number of techniques have
been proposed to keep the ambiguity of the structure of the
topology in order to nullify the adversary analysis and boost
the anonymity of the BS position. For example, Deng et al.
[10] proposed a set of techniques that make the traffic pattern
more disperse and introduce random fake paths starting from
random positions in the WSN to confuse the adversary. On the
other hand, the decoy sink protocol, proposed in [4], creates a
dummy BS away from the real BS. All the data is first
forwarded to the dummy BS and then re-routed to the real BS.
Mehta [5] goes further to propose the creation of multiple fake
sinks that are spread evenly throughout the network. In [12],
the BS selectively forwards some of the received packets in
random directions with varying time-to-live parameter in order
to make the BS appear like a relay node rather than a sink.
Furthermore, the BS is relocated in order to disturb the
analysis and confuse the adversary.
It is important to note that all approaches for increasing
the BS anonymity impose significant overhead. The overhead
includes the additional control traffic needed for topology
management, the extra usage of energy and bandwidth in
forwarding packets with useless or redundant payload, forced
motion of assets, etc. In addition to its implementation
simplicity, our proposed strategy limits the overhead to the
increased power in the transmission and avoids changes in the
routing topology and position of nodes.
IV. INCREASING BASE-STATION ANONYMITY
A. Approach Overview
The availability of programmable range radio transceivers has
made it attractive for designers to adjust the output power of a
transmitter to the least level that would achieve sufficient
signal-to-noise ratio at the receiver. Designers have relied on
this feature to preserve the sensor’s onboard energy.
Moreover, since the power loss is a quadratic function of the
distance, disseminating the sensor data to the BS over multihop paths is very common in WSNs in order to further reduce
the node’s energy consumed in communication and prolong
the network lifetime [2]. However, such a practice makes it
easier for the adversary to detect active links between nodes
and correlate them to determine the route to the BS. Basically,
the adversary will intercept a transmission by multiple
antennas, locate the source and determine the transmission
power and then guess where the receiver might be. Figure 2
explains how the adversary may analyze the intercepted
transmission from node “A” to find out where the BS is
positioned. The shaded area defines the boundary where the
next hop may be located. When the transmission of “B” gets
intercepted, the adversary can detect the existence of the link
AÆB. Repeating this process reveals where the BS is located.
The key factor for how quickly the adversary’s traffic
analysis converges is the number of possible links that a
transmission implies. For example, if the analysis reveals that
node “A” has only one neighbor “B”, a transmission from “A”
will be directed to “B”. Increasing the node degree makes the
analysis more complicated. In other words, having high node
force the adversary to analyze a general forest rather than a
simple tree. Now, considering the fact that the BS is not
transmitting to relay a data packet, determining the existence
of a sink is easier when the transmitting node does have very
few neighbors. To illustrate this point, let us consider Figure 1
again. If node “D” has only “C” as a neighbor and “D”
transmits a packet soon after C transmits, an adversary would
highly suspect that “D” is a neighbor of the sink node. By
boosting the degree of node “D” the adversary would have
low confidence in drawing such a conclusion.
The above discussion explains the intuition for the
proposed strategy. Basically, increasing the output power of
would increase the distance that transmission will reach and
De
tra term
th nsm ing
e a is
A
ar
ea dve sion 's lo
to rsa ra ca
be ry ng tio
foc to i e e n a
us de nab nd
ed nti
le
on fy th
e
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
A
BS
F
E
C
B
Growing the range
increases the node
degree and grows
the set of potential
receivers
BS
E
D
C
B's transmission may be
correlated with that made
earlier by of A and may lead
the adversary to conclude
that B is the next hop on the
data path from A to the BS
F
A
D
B
Increasing the trasmission
range gives the impression
that D is targeting E and
makes it harder to detect
that a packet is forwarded
towards the BS
Figure 2: Illustration of how an adversary can Figure 3: Increasing transmission range Figure 4: Illustration of the impact of varying
analyze the traffic to detect the location of the introduces more communication links and the transmission range on the adversary’s
BS.
increases the level of uncertainty for analysis.
adversary’s traffic analysis
extend the list of possible recipients. Figure 3 shows the effect
of using a long transmission range for the nodes in Figure 2.
Now, node D and E can reach each other and thus the
transmission of D may imply that E is the recipient and not the
BS. The balance of this section provides detailed analysis to
formulate the effect of the transmission power on anonymity.
The next section summarizes the metric for assessing
anonymity that the analysis uses.
B. Anonymity Assessment
A number of metrics have been proposed in [12] to
quantitatively assess the anonymity of the BS. The most
notable among these metrics is the evidence theoretical
approach. Evidence theory is a branch of generalized
information theory. The evidence theory model for traffic
analysis considers each captured packet as an evidence of a
communication link between a transmitter and a receiver. The
probable set of destinations is determined based on the
location of the packet source and the output power of the
source radio, i.e., reachable range, while transmitting the
packet. An adversary keeps on collecting and correlating
evidences in order to draw a conclusion about the existence of
end-to-end communication paths. Evidence for a path between
two nodes is equal to the minimum evidence available on the
individual links on the path. For example if S is the set of
nodes in a network then the evidence E for any link relation L
defined on S is given by:
, | | 2,
… 1
min
The total evidence is equal to the number of packets
observed by the adversary. The normalized value of evidences
for link L is given by Enorm(L) = E(L)/Total Evidence. It
expresses the proportion to which all relevant evidences
support the claim that a particular element of S belongs to the
link relation L. Dijiang Haung [11] further introduced a
discord function, called Belief, for representing the anonymity
of a node based on the collected evidences. The Belief denotes
the adversary confidence that a node u is the end point of a
path P and is defined as:
… 2
The weighted belief of u refers to the sum of belief measures
for all paths ending at u, each multiplied by the path length.
To simplify the analysis, a square-shaped cell structure is
considered for representing the deployment area. The size of a
cell is related to the error that the adversary may experience
when localizing the source of a radio transmission. Therefore,
whenever a packet is transmitted by a sensor node the
adversary can accurately determine the source cell from which
the packet was transmitted. The adversary can then establish
the probable area in which the recipient of the packet lies
depending on the source position, transmission range and
granularity of the cell. It is important to note that the number
of cells has dramatic effect on the complexity of the traffic
analysis as we show in the next section.
C. Anonymity-Power Analysis
An adversary intercepts each transmission, locates the source
and estimates the output power of the operation amplifier of
the radio transceiver. The output power is then used to
identify the cells in which the receiver may be located. Now,
the adversary has an evidence for the relationship between the
transmitting cell and each reachable cell. As explained in
[6][12], after collecting enough evidence, the adversary will
correlated the links among neighbor cells in order to form
aggregate paths that leads to the BS. In essence this process
implies finding all paths that end to the location of BS and
estimating the believe function Bel(P) for each path P. The
paths with the highest Belief stand out as the most probably
routes leading to the BS. To protect the BS, one would like to
boost its anonymity by complicating the traffic analysis that an
adversary may conduct and by making the BS hard to detect.
The following discusses how increasing the transmission
range contributes to these two goals.
Complexity of Traffic analysis: To analyze the complexity of
the evidence aggregation process, we model the cells as nodes
in a graph G. Two cells ci and cj are connected in G if there
was a transmission made by a sensor in ci with a range that
covers cj. To determine the cell that most probably hosts the
BS, one can apply depth first search from each cell (vertex) cm
to determine all paths with non-zero Belief that lead to cm. The
complexity of traversing a graph is O b , where b is the
branching factor and d is the depth to search. For an N×N grid,
this will be repeated for N2 cells which yields the overall
execution time complexity of N
O b . Since in WSNs,
data are usually disseminated over the shortest multi-hop
paths, an adversary may be able to limit the length of each
path in the analysis to that of the shortest path, which for a
grid, cannot exceed the diagonal of the grid. Thus, the
complexity formula can be refined to be:
….. (3)
O bN
N
The branching factor b depends on the degree of the
vertices in G, which depends on the transmission range. Figure
4 shows how the transmission from region around the center,
i.e., the black colored cell, with different range affects the
analysis of the adversary. The recipient of the packet may lie
in any of the grey colored region. Note that the transmitter and
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
recipient may lie in the same cell, and hence the source cell is
always included in the set of probable recipients. The number
of probable recipient cells in the left and right configurations
of Figure 4 is 25 and 9, respectively.
From an adversary point of view, using a fine-grained
grid gives a very much better understanding about the location
of the BS. For example if we have an area of size 1000×1000
and the adversary assumes cell size of 250×250, there will be
16 cells. Thus, the execution time complexity will be 16
O b and the accuracy of the location of the BS will be
250×250, which is a low resolution. Obviously, using
100×100 grid boosts the accuracy of the location estimates,
yet at a very excessive price in terms of an execution time
. Increasing the transmission
complexity of 100 O b
power boosts the number of potential neighbors and pumps the
value of b significantly higher which massively complicates
the analysis for the adversary. If the transmission range of a
sensor in cell “S” covers a neighboring cell in each direction:
b = 2×3+ 2×1 = 2×(3+1)
If it reaches two cells in each direction
b = 2×5+2×3 + 2× 3+ 2×1 = 2× (5+3) + 2×(3+1)
Generalizing, for a transmission range that covers up k cells
away from S:
2
2
1
2
2
1
1
8
… 4
Combing (3) and (4), the complexity for the traffic analysis
can be represented as a function of the transmission range as:
Complexity
8
… 5
where
/
, L = Length of the monitored area,
and
/
. Equation (5) can be
employed for conducting power and anonymity tradeoff. The
increase in the transmission power can be assessed based on
how high the complexity of the traffic analysis ought to be
boosted. For example, when the mission of the network is very
critical, or few additional anti-traffic analysis measures are
feasible, the price for increasing the power in terms of reduced
node lifetime may be justified.
BS undelectability: To demonstrate the positive impact on BS
undelectability that an increased transmission range can
achieve, let’s revisit how the evidences are used by the
adversary. Equation (1) assigns a high evidence value for links
between cells with large set of evidences. The Belief defined
in Equation (2) depends on the collective evidences of the
links that are part of the path. Thus, Equation (1) is in fact the
key for highlighting the importance of a link and consequently
the path. Having a gradient in the evidence values among the
individual cells plays a major role in making some links to
stand out and some to lose significance. Using the least power
to communicate makes the impact of a transmission on the
collected evidences more localized to the vicinity of the
source. A network-wide view indicates that the localized
impacts cause the overall variations in evidence values among
the cells. Increasing the output power widens the scope and
more cells grow their evidences. As the power gets increased,
more cells are covered by the individual transmissions and the
variations among the evidence values diminish. To illustrate
this point, assume that a node has a range that covers the entire
area. In that case, all cells will have the same evidence values
and there will not any pattern that an adversary can notice. We
validate this analysis through simulation in the next section.
V. SIMULATION RESULTS
A. Experiment Setup and Anonymity Metrics
The effect of the transmission power and the above analysis
were validated in a simulated environment. In the simulation
experiments, 150 sensor nodes are uniformly spread over an
area of 1000 × 1000 units. The BS is also randomly positioned
within the area. Sensor nodes probe their surrounding and
periodically report their values to the BS over least cost multihop paths that are set using the communication energy as link
costs. Three configurations for the cell sizes are considered in
the experiments; namely, 16 cells of size 250 × 250 units, 25
cells of size 200 × 200 units and 36 cells of 166 × 166 units.
Naturally the cell size depends on the adversary’s capabilities.
If the adversary can determine the source of signals with
higher precision he often divides the deployment region into
larger number of small sized cells. On the other hand, with
only low precision tracking he would hope to find an
approximate BS position by dividing the area into larger cells.
Two traffic analysis strategies were considered for the
adversary. In the first strategy, the adversary sets the cell size
up front. This entails computation-intensive traffic analysis
given the high complexity, as shown in Section V, and
therefore only large cells are considered in the simulation
experiments. The second involves a zooming strategy where
the adversary applies course-grained analysis using large cells
and then refine by decreasing the cell size as progress is made.
For example, setting a 2×2 grid will lead to suspecting one cell
that will be further probed by dividing it into smaller cells.
Ultimately the network has to guess or estimate the
adversary’s capabilities and make some assumption about the
cell sizes. We have considered the above three different cell
sizes to study the effect of the cell size on anonymity.
Anonymity is measured using evidence theory, discussed in
Section V. We report the evidence of the individual nodes to
validate our earlier observation about difference among the
cells under high transmission power. The number of data paths
is also reported as indication of the execution time complexity.
The results of the individual experiments are averaged over 30
runs. All results are subjected to 90% confidence interval
analysis and found to stay within 10% of the sample mean.
B. Simulation Results
As discussed in section V, increasing the transmission range
tends to even the evidence values among the individual cells.
The simulation results in Figure 5 validate this effect. In this
experiment the area has been divided into 36 cells (6×6 grid)
and transmission range has been increased by the size of 1, 2,
3, 4, and 5 cells. Figure 5 clearly shows that increasing the
transmission range has resulted in flattening the curve and
reducing the variations among the evidence values of the cells.
As argued earlier this increases the anonymity of the BS since
there are fewer hints for the adversary to pursue.
Recall that the Belief value reflects the adversary confidence
that a certain node is indeed the end point of a path. Figure 6
shows the weighted belief for the cell that hosts the BS, which
assess the BS anonymity. In Figure 6, boosting the
transmission power increases the number of cells considered
in the adversary’s analysis, which results in a drop of the
belief values. Increasing the range by one cell in a 5x5 grid
makes about 17% gain in anonymity (the belief value drops
about 17% for adversary). Comparable gains are observed
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
Figure 5: Effect of increasing transmission Figure 6: Effect of transmission power on Figure 7: Weighted Belief versus transmission
range (measured in cell count) on the evidence Weighted Belief under normal adversary power for zooming adversary attack strategy.
measure for individual cells (x-axis).
strategy.
when the adversary applies the zooming strategy, as indicated
by Figure 7. This confirms the effectiveness of the
transmission power increase on the BS anonymity, even when
the adversary pursues advances traffic analysis strategies.
Figure 8 shows the number of paths considered in the
evidence calculation as we increase the transmission range,
measured as a multiple of the cell size. Recall that link
relations among directly reachable nodes are aggregated in
order to detect end-to-end data routes. As mentioned in
Section V, due to the high complexity, an adversary assumes
that the shortest data routes are used in the network, meaning
that the investigated paths cannot be longer than the diagonal
of the grid. Even with this major simplification, Figure 8
shows that, the number of paths growing exponentially with
the increase in transmission range, which in return requires
major computational recourses for an adversary to perform the
traffic analysis. The same observations can be made about
Figure 9, when the adversary applies the zooming strategy.
Although zooming achieves 2-order of magnitude reduction in
the complexity, the analysis is still very complicated and
exhaustive.
Figure 8: Number of paths included in evidence deduction for
zooming adversary model.
Figure 9: number of paths considered in traffic analysis as
transmission range measured in multiple of cell sizes, is increased.
This plot reflects the complexity of the analysis when the adversary
uses a pre-set grid size (normal strategy).
VI. CONCLUSION
The major role that the base-station (BS) plays in wireless
sensor networks (WSNs) makes it a focal point of adversary's
attack. An adversary can intercept the radio transmissions
made by the sensor nodes and apply traffic analysis techniques
to aggregate the collected information in order to identify the
BS location. Increasing the anonymity of the BS is an
effective countermeasure for the adversary's attempts. This
paper has studied how increasing the transmission power of
sensor nodes can be exploited as a means for boosting the BS
anonymity. It has been shown that extending the range makes
it harder for an adversary to detect patterns in the intercepted
transmissions that can support his analysis. In addition,
increasing the power boosts the network connectivity and
causes a major growth in the complexity of the adversary’s
analysis. The simulation results have confirmed the
effectiveness of the proposed strategy. Future work includes
extending the study to capture the effectiveness of selective
and random power increase by subset of the nodes.
Acknowledgement: This work is supported by the National
Science Foundation, contract # 00002270 and 00005844.
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