![](bg7.png)
signal strength into distance estimates.
In addition, range-free techniques have also been
proposed to solve for sensor localization problem (see
(Bulusu et al., 2004), (He et al., 2003), and (Niculescu
and Nath, 2001)). The centroid of all locations in the
received beacon signals has been proposed for sen-
sor’s location discovery in (Bulusu et al., 2004). In
(Niculescu and Nath, 2001) DV-hop was used as an
alternative solution. A sensor node computes its posi-
tion using hop-counts received from beacons, instead
of distances. Then, the node finds the average dis-
tance per hop through beacon nodes’ communication.
The range-based localization schemes have been
enhanced to address security concerns for sensor
networks (e.g., (Liu et al., 2005a) and (Liu et al.,
2005b)). Both an attack-assistant MMSE-based loca-
tion estimation and a voting-based location estimation
have been proposed to deal with attacks in location
discovery in (Liu et al., 2005a). In the first method,
the key point is to find a consistency set. That is usu-
ally not an easy task. There is the same difficulty
seeking the highest vote area as in the latter method.
Furthermore, in (Liu et al., 2005b) Liu et al. provided
a method to reason about the suspiciousness of each
beacon node at the base station based on the detec-
tion information from beacon nodes. In (Fretzagias
and Papadopouli, 2004), Fretzagias et al. proposed
another voting-based scheme, called the Cooperative
Location Sensing (LCS).
Our median-based method is inspired by the cen-
troid technique (Bulusu et al., 2004) and the MMSE
method. As indicated, a mean value does not reflect
the center of location references. Instead, a median
is used to filter out outliers. In this paper we pro-
pose new median-based schemes for dealing with ma-
licious references. In Algorithms 4-5 we can easily
filter out malicious references and then estimate the
location of a sensor node by using the MMSE method.
6 CONCLUSIONS
In this paper we proposed a suite of secure local-
ization methods, including the secure dynamic local-
ization method (Algorithm 5), for sensor networks.
A median-based technique instead of a mean-based
technique was used to represent the center of loca-
tion references so that malicious reference informa-
tion could be filtered out easily. Our security perfor-
mance analysis has shown that the proposed secure
localization methods can tolerate up to 50%malicious
beacon nodes, and they usually have linear computa-
tion time. This is the best we can achieve. We further
conducted simulations to demonstrate the applicabil-
ity and accuracy of these algorithms. Preliminary val-
idation tests showed that Algorithms 4-5 have a good
accuracy against other algorithms. Detailed valida-
tion results are not provided due to the page limit.
REFERENCES
Bahl, P. and Padmanabhan, V. (2000). An in-building RF-
based user location and tracking system. In Proceed-
ings of the IEEE INFOCOM ’00.
Bernholt, T. and Fried, R. (2003). Computing the update
of the repeated median regression line in linear time.
Information Processing Letters, (88):111–117.
Bulusu, N., Heidemann, J., and Estrin, D. (2004). GPS-less
low cost outdoor localization for very small devices.
IEEE Personal Communications Magazine, 7(5):28–
34.
Doherty, L., Pister, K., and Ghaoui, L. (2001). Convex opti-
mization methods for sensor node position estimation.
In Proceedings of INFOCOM ’01.
Fretzagias, C. and Papadopouli, M. (2004). Cooperative
location-sensing for wireless networks. In Proceed-
ings of IEEE PerCom ’04.
He, T., Huang, C., Blum, B. M., Stankovic, J., and Ab-
delzaher, T. (2003). Range-free localization schemes
for large scale sensor networks. In MobiCom ’03.
Hu, L. and Evans, D. (2004). Localization for mobile sensor
networks. In MobiCom ’04.
Huber, P. J. (1981). Robust statistics. Addison-Wesley Pub-
lishing Company, New York.
Karp, B. and Kung, H. (2003). Greedy perimeter stateless
routing. In MobiCom’03.
Liu, D., Ning, P., and Du, W. (2005a). Attack-resistant lo-
cation estimation in sensor networks. In Proceedings
of IPSN ’05.
Liu, D., Ning, P., and Du, W. (2005b). Detecting malicious
beacon nodes for secure location discovery in wireless
sensor networks. In Proceedings of IPSN ’05.
Mainnwaring, A., Polastre, J., Szewczyk, R., Culler, D.,
and Anderson, J. (2002). Wireless sensor network for
habitat monitoring. In Proceedings of ACM WSNA’02.
Mauve, M., Widmer, J., and hartenstein, H. (2001). A sur-
vey on position-based routing in mobile Ad-Hoc net-
works. IEEE Network Magazine.
Niculescu, D. and Nath, B. (2001). Ad hoc positioning sys-
tem (APS). In Proceedings of IEEE GLOBECOM ’01.
Priyantha, N., Chakraborty, A., and Balakrishnan, H.
(2000). The cricket location-support system. In Pro-
ceedings of MOBICOM ’00.
Want, R., Hopper, A., Falcao, V., and Gibbons, J. (1992).
The active badge location systems. ACM Transactions
on Information Systems.
Wellenhoff, H., Lichtenegger, H., and Collins, J. (1997).
Global Positions System: Theory and Practice, Fourth
Edition. Springer Verlag.
SECRYPT 2008 - International Conference on Security and Cryptography
196