CONTRADICTION COMMUNICATION RANGE OF SENSOR NETWORKS

www.ijiarec.com
NOV 2013
International Journal of Intellectual Advancements
and Research in Engineering Computations
CONTRADICTION COMMUNICATION RANGE OF SENSOR NETWORKS
*1
Mr. G.Parthasarathy, 2
Mr. P.Vijayan,M.E.,
ABSTRACT
The knowledge of sensors’ locations is crucial information for many applications in Wireless Sensor
Networks (WSNs). When sensor nodes are deployed in hostile environments, the localization schemes are
vulnerable to various attacks, e.g., wormhole attack, pollution attack, range enlargement/reduction attack, and etc.
Therefore, sensors’ locations are not trustworthy and need to be verified before they can be used by location-based
applications. Previous verification schemes either require group-based deployment knowledge of the sensor field, or
depend on expensive or dedicated hardware, thus they cannot be used for low-cost sensor networks. In this paper,
we propose a lightweight location verification system that performs both “on-spot” and “in-region” location
verifications. The on-spot verification intends to verify whether the locations claimed by sensors are far from their
true spots beyond a certain distance. We propose two algorithms that detect abnormal locations by exploring the
inconsistencies between sensors’ claimed locations and their neighborhood observations. The in-region verification
verifies whether a sensor is inside an application-specific verification region. Compared to on-spot verification, the
in-region verification is tolerable to large errors as long as the locations of sensors don’t cause the application to
malfunction. To study how to derive the verification region for different applications and design a probabilistic
algorithm to compute in-region confidence for each sensor. Experiment results show that our on-spot and in-region
algorithms can verify sensors’ locations with high detection rate and low false positive rate. They are robust in the
presence of malicious attacks that are launched during the verification process. Moreover, compared with previous
verification schemes, our algorithms are effective and lightweight because they do not rely on the knowledge of
deployment of sensors, and they don’t require expensive or dedicated hardware, so our algorithms can be used in
any low-cost sensor networks.
Index terms: WSN, Lightweight, On-spot, In-region, Sensors.
I INTRODUCTION
Localization in wireless sensor networks, i.e.,
knowing the location of sensor nodes, is very important
for many applications such as environment monitoring,
target tracking, and geographical routing. Since
wireless sensor networks may be deployed in hostile
environment, sensors’ localization is subjected to many
malicious attacks. For example, attackers can
compromise sensors and inject false location
information. They can also interrupt signal transmission
between sensors and contaminate distance
measurements. Hence, the locations estimated in the
localization process are not always correct. Although
some secure localization algorithms L. Hu and D.
Evans, “Using directional antennas to prevent
wormhole attacks”, Y. Hu, A. Perrig, and D. Johnson,
“Packet Leashes: A Defense against Wormhole Attacks
in Wireless Ad Hoc Networks”, L. Lazos, and R.
Poovendran, “SeRLoc: Secure Range-Independent
Localization for Wireless Sensor Networks”, Z. Li, W.
Trappe, Y. Zhang, and B. Nath, “Robust Statistical
Methods for Securing Wireless Localization in Sensor
Networks”, D. Liu, N. Peng, and W.K. Du, “Attack-
Resistant Location Estimation in Sensor Networks”,
were proposed to help enhance sensors’ resistance to
attacks, they cannot completely eliminate wrong
location estimations. Therefore, we consider location
Author for Correspondence:
*1
Mr.G.Parthasarathy, PG Scholar, Department of CSE, Sri Krishna Engineering College Panapakam, Chennai-301, India.
E-mail: sarathyp65@gmail.com
2
Mr.P.Vijayan, M.E., Asst.Professor Department of CSE, Sri Krishna Engineering College, Panapakam, Chennai-301, India.

16
verification as a necessary second line-of-defense
against malicious attacks, which becomes the focus of
this paper. We classify previous location verification
algorithms into two categories, namely, on-spot
verification and in-region verification. On-spot
verification is to verify whether a sensor’s true location
is the same as its estimated location (or with very small
errors). Most existing verification algorithms, belong to
this category. To obtain the desired on-spot verification
results, these algorithms either utilize the deployment
knowledge of sensors in the field or make use of some
dedicated hardware to verify distance measurements.
For example, in, it is assumed that some covert base
stations are deployed throughout the field. These
special base stations communicate with one another
through wired links and purposely hide their existences
from being discovered by sensors. Then these base
stations can verify sensors’ locations by checking
whether the distances calculated using sensors’
estimated locations are the same as the distances they
directly measure using RF signals. In, it is required that
sensors are able to measure time in nanoseconds in
order to detect range reductions that directly impact the
localization results. Since existing verification
algorithms either require deployment knowledge or
depend on hardware that are expensive and generally
unavailable in low-cost wireless sensor systems, a
lightweight verification algorithm should be designed
that can effectively perform on-spot verifications.
Besides the on-spot verification, some research effort
has also been devoted to designing in-region location
verification algorithms. Shankar and Wagner first
defined the concept of in-region verification N. Sastry,
U. Shankar, and D. Wagner, “Secure verification of
location claims”, in ACM Workshop on Wireless
Security (WiSe),2003. They also proposed a protocol
named Echo to verify if a sensor is inside a physical
region such as a room, a building or even a sport
stadium. Based on the verification result, it can be
decided whether to assign sensors (i.e., people who take
the sensors) the accessing right to some resources in
that physical region. As the first work, Echo
successfully utilizes in-region verification to facilitate
location-based access, however, it cannot be directly
used Digital Object Indentifier for other location-based
applications, because the verification region may not be
explicitly given and needs to be determined carefully by
analyzing applications’ functions. Secondly, when
performing in-region verification, Echo requires the use
of multiple verifiers that can transmit radio signal and
receive ultrasound signal, and bound their XOR
operations within the magnitude of nanoseconds. Such
verifiers increase the expense and require extra
deployment efforts. In this paper, we designed a
verification system that overcomes the shortcomings of
previous research. The verification system can
effectively verify whether sensors’ estimated locations
are trustable. According to the specific requirements,
the system can provide either on-spot or in-region
verification results. First, to provide on-spot verification
service, two algorithms can be used by our system,
namely, the Greedy Filtering by Matrix (GFM)
algorithm and the Greedy Filtering by Trust ability-
indicator (GFT) algorithm. Both algorithms exploit the
inconsistency between sensors’ estimated locations and
their “neighborhood observations” (the messages that
each sensor can receive from other sensors in its
neighborhood). Second, to perform in-region
verification, a verification region is first calculated
according to the applications’ functions, then a
probabilistic algorithm is used to compute the
confidence that a sensor is inside the verification
region. All algorithms proposed in this paper are
lightweight because they do not require any dedicated
hardware or infrastructures, and they do not incur high
computation overhead at the sensor side, so that the
verification system can be applied to low-cost wireless
sensor networks. Moreover, they are robust against
malicious attacks that are launched by sophisticated
attackers who try to exploit the system’s weakness.
Simulation results prove the effectiveness of our
verification system.
II PROBLEM AND ANALYSIS
In recent years, a large number of localization
schemes T. He, C. Huang, B. Blum, J. Stankovic, and
T. Abdelzaher, “Range- Free Localization Schemes in
Large Scale Sensor Network”, in ACM MobiCom,
2003. D. Moore, J. Leonard, D. Rus, and S. Teller,
“Robust distributed network localization with noisy
range measurements”, in Proceedings of the 2nd ACM
Conference on Embedded Networked Sensor Systems
(ACM SenSys), 2004. D. Nicolescu, and B. Nath, “Ad-
Hoc Positioning Systems (APS)”, in IEEE
GLOBECOM, 2001. D. Niculescu, and B. Nath, “Dv
based positioning in ad hoc networks”, in Journal of
Telecommunication Systems, 2003. A. Youssef and M.
Copyrights © International Journal of Intellectual Advancements and Research in Engineering Computations, www.ijiarec.com

16
Youssef, “A Taxonomy of Localization Schemes for
Wireless Sensor Networks”, In ICWN, pages 444-450,
2007. Y. Zeng, J. Cao, J. Hong, S. Zhang, and L. Xie,
“Secure localization and location verification in
wireless sensor networks: A survey”, the Journal of
Supercomputing, pages 1-17, 2010. Because
localization schemes can be compromised by malicious
attackers, who can launch wormhole attack, range
enlargement/reduction attacks, many secure localization
schemes are designed. For instance, Lazos et al.
proposed SeRLoc , SeRLoc: Secure Range-Independent
Localization for Wireless Sensor Networks”, which
utilizes directional antennas equipped on anchors to
detect wormholes. As an improvement to the SeRLoc,
they later introduced High-resolution Robust
Localization for WSNs (HiRLoc). This algorithm also
makes use of directional antennas but the
communication range of locations is variable. Other
approaches use stochastic methods to filter out bad
location references. Since these algorithms run on
sensors which have very limited resources, some
improvement methods have been developed for fast
computing of region intersections. These secure
localization schemes mainly aim to enhance innocent
sensors’ ability to correctly localize themselves, in
presence of malicious attacks. However, since sensors
may not be innocent, i.e., they are easily be
compromised, they report false locations directly to the
command center. Therefore, location verification is
necessary to defend against such attacks. The location
verification problem was first addressed, in which
Sastry, et al proposed Echo protocol to verify if a
device is inside some physical region, such as a room or
a football stadium. The Echo protocol is mainly to
provide location-based access control, and cannot be
directly applied for location verification in other
applications. Capkun and Hubaux proposed the
Verifiable Multilateration (VM) technique to verify
whether a sensor’s estimated location is at its true
location using the distance-bounding protocol. Some
other solutions K. Rasmussen and S. Capkun, “Location
privacy of distance bounding protocols”, in Proc. of the
15th ACM Conference on Computer and
Communications Security (CCS’08), pp. 149-160, Oct
2008. J. T. Chiang, J. Haas, and Y.-C. Hu, “Secure and
precise location verification using distance bounding
and simultaneous multilateration”, in Proceeding of the
2nd ACM Conference on Wireless Network Security
(WiSec’09), pp. 181-192, 2009. . Chandran, V. Goyal,
R. Moriarty, and R. Ostrovsky, “Position based
cryptography,” in Proceeding of CRYPTO ’09, pp. 391-
407, 2009. were also proposed to perform location
verifications by exploring the trusted network
infrastructure. Most of these solutions utilize distance
bounding techniques, in which the verifier challenges
sensors and measures the elapsed time before they
receive sensors’ responses. Recently, Capkun et al.
proposed covert base stations (CBS) which can keep
their existence and communications unknown to
sensors. These algorithms provide on-spot verification
results, i.e., if a sensor’s claimed location is the same as
its true location, thus they require some extra expensive
hardware to be deployed through the field. Some
lightweight verification schemes that do not require
extra infrastructures have also been proposed. Du, et al.
presented the location anomaly detection (LAD)
scheme that examines the consistency between
sensors’ estimated locations and the deployment
knowledge of the sensor field. Ekici, et al. proposed the
probabilistic location verification (PLV) algorithm that
explores the probabilistic relation between hop-counts
and Euclidean distance between source and destination.
In our earlier version of this paper which was published
in ICDCS’07, we introduced Greedy Filtering by
Matrix (GFM) algorithm and Trustability Indicator (TI)
algorithm , to explore the consistency between
sensors’locations and their neighborhood observations
to detect location anomalies. Recently, Talasila, et al.
proposed a location authentication protocol , which is
named LINK (Location verification through Immediate
Neighbors Knowledge). In their algorithm, a centralized
Location Certification Authority (LCA) receives a
number of messages from sensors and their neighbors,
then decides whether the claim is authentic based on
spatio-temporal correlation between the users, trust
scores,However, most of previous lightweight
algorithms focus on detecting location anomalies,
namely, verifying if sensors’ claimed locations are far
away from their true locations. They do not take into
consideration the application’s requirements on the
accuracies of sensors’ locations. From this sense, ours
is the first work that attempts to study an application’s
location-related functions and propose lightweight
verification algorithm to facilitate the application’s
operations.
III SOLUTION AND MECHANISM
The proposed greedy filtering algorithms GFM

16
and GFT rely on the inconsistencies between sensors’
geographical relationships according to their claimed
locations and those implied by their neighborhood
observations. First, GFM explores the inconsistency
directly by comparing the elements in two matrixes, the
Estimation Matrix and the Observation Matrix.GFT
detects the location anomalies indirectly based on the
indicators which indicate the inconsistencies. Second,
the GFM algorithm filters out bad locations as soon as
one of the metric values is not accepted according to the
threshold. Sensors not revoked will be verified in the
final round. In GFT algorithm, instead, the good
location claims are accepted first, and after multiple
rounds when indicators of remaining sensors become
stable, the sensors with indicators below the threshold
will be revoked. The probabilistic location verification
(PLV) algorithm that explores the probabilistic relation
between hop-counts and Euclidean distance between
source and destination. The location authentication
protocol which is named LINK (Location verification
through Immediate Neighbors Knowledge). In their
algorithm, a centralized Location Certification
Authority (LCA) receives a number of messages from
sensors and their neighbors, then decides whether the
claim is authentic based on spatio-temporal correlation
between the users, trust scores
SYSTEM ARCHITECTURE
Verification Center
Sensor Moving
SenSorMoving
NeighbourHood Observation
Verification
Report
Out Of Communcation Range
Sensor
Modules
Sensor Location Search:
Sensor Location Search Module is the main
module of this application to find out the how many
sensor are connected in local area network. As the first
work, successfully utilizes in-region verification to
facilitate location-based access, however, it cannot be
directly used. For other location-based applications,
because the verification region may not be explicitly
given and needs to be determined carefully by
analyzing applications’ functions.
Sensor Location
Search
Sensor A
Sensor B
SenSor C
Neighborhood observation:
The communication range of a sensor is a
circle centered at the sensor’s true location and has a
certain radius. We assume all sensors’ communication
ranges have the same radius. Each sensor broadcasts its
ID within its communication range, and passively
overhears IDs broadcast by other sensors. We say

16
sensor A can observe sensor B, if A can receive the ID
message from B. And we name the list of IDs that a
sensor observes the sensor’s neighborhood observation.
SenSor A SensorBObserve
A can receive the ID message fromB
Bcan receive the ID message fromA
On Spot Verification:
A sensor’s localization error is less than a
certain distance. Let True and Lest denote the true
location and the estimated location of a sensor, then the
verification fails if the following condition holds true:
|Ltrue − Lest| > D, where D is named the Anomaly
Degree. The value of D should be set properly with the
considerations of the application requirements and the
value of “normal” localization errors that are present in
no-attack environment. It considers D as an input
parameter and assumes its value has already been given
to our system.
SensorA SensorA
SenSor moving
Communication
Range
Distance
In-region Verification:
Basically, if two sensors observe each other,
then the VC considers them to be a pair of “confirmed”
neighbors. Then, VC derives a probability distribution
for each sensor, which indicates how probably the
sensor is at each point in the field. The distribution
function can be either continuous or discrete. In the
continuous version, the in-region confidence is
computed by taking the integral of the distribution
function within the verification region; In the discrete
version, the in region confidence is the sum of the
probabilities of all points within the verification region.

16
SensorAVerification Center SensorB
probability distribution for each sensor
Verification Center
Verification Center (VC) that verifies if
sensors’ estimated locations are acceptable. The VC
resides at the base station or control center, and can be
safely protected from the attackers. Each sensor reports
its estimated location and its neighborhood observation
to the VC. We assume each sensor shares a pair wise
key with the VC, so they can encrypt the message and
authenticate themselves. Such pair wise keys can either
be preloaded off-line into sensors’ memories, or
distributed online using some existing key distribution
algorithms finally; any routing protocol may be
potentially used to route sensors’ reports to the VC
except the location-based routings, and because
sensors’ locations are not trustworthy and wrong
locations will lead to loops or even delivery failures.
Verification Center
SenSorA
SenSorB
SenSor Share A
PairWaise Key
SenSor Share A
PairWaise Key
SenSor Report
Maintanance
IV CONCLUSION
In this paper, we propose a lightweight
location verification system that performs both “on-
spot” and “in-region” location verifications. The GFM
and GFT algorithms verify whether the locations
claimed by sensors are far from their true spots beyond
a certain distance. Sensors’ neighborhood observations
are explored for the information inconsistencies. The
in-region verification verifies whether a sensor is
inside an application specific verification region. A
probabilistic method is designed to provide the
confidence that a sensor is inside the verification
region. Our work takes the first step to integrate the
application requirements in determining the trust ability
of sensors’ estimated locations. Moreover, our proposed
verification system is more lightweight, effective and
robust compared to previous works. It does not require
any dedicated or expensive infrastructures in the field;
it yields satisfactory verification results to a variety of
applications, which is approved by the simulation
results; furthermore, it is resilient to malicious attacks
and can be used in hostile environments.
REFERENCE
[1]. W. Du, J. Deng, Y. S. Han, and P. K.
Varshney, “pairwise key predistribution
scheme for wireless sensor networks”, in Proc.
ACM CCS, pp. 42-51, 2003.
[2]. W. Du, J. Deng, Y. S. Han, S. Chen, and P. K.
Varshney, “key management scheme for
wireless sensor networks using deployment
knowledge”, in Proc. IEEE INFOCOM, 2004.
[3]. D. Liu, N. Peng, and W.K. Du, “Attack-
Resistant Location Estimation in Sensor
Networks”, in ACM/IEEE IPSN, 2005.

16
[4]. Z. Li, W. Trappe, Y. Zhang, and B. Nath,
“Robust Statistical Methods for Securing
Wireless Localization in Sensor Networks”, in
ACM/IEEE IPSN, 2005.
[5]. S. Brands and D. Chaum, “Distance-bounding
protocols”, in Workshop on the theory and
application of cryptographic techniques on
Advances in cryptology. Springer-Verlag New
York, Inc., 1994, pp. 344C359.
[6]. S. Capkun and J. P. Hubaux, “Secure
Positioning of Wireless Devices with
Application to Sensor Networks”, in IEEE
INFOCOM, 2005.
[7]. N. Tippenhauer and S. Capkun, “Id-based
secure distance bounding and localization”, in
Proceeding of ESORICS ’09, pp. 621-636,
2009.
[8]. S. Capkun, M. Cagalj, and M. Srivastava,
“Secure Localization With Hidden and Mobile
Base Statioins”, in IEEE INFOCOM, 2006.
[9]. W. Du, L. Fang, and P. Ning, “LAD:
Localization anomaly detection for wireless
sensor networks”, in Proceedings of IEEE
International Parallel and Distributed
Processing Symposium (IPDPS), 2005.
[10]. E. Ekici, J. McNair, and D. Al-Abri, “A
Probabilistic Approach to Location
Verification in Wireless Sensor Networks”, in
Proceedings of IEEE International Conference
on Communications (ICC), 2006.
[11]. L. Lazos and R. Poovendran, “Hirloc: high-
resolution robust localization for wireless
sensor networks”, in IEEE Journal on Selected
Areas in Communications, 24(2):233-246,
2006.

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