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position estimator. The idea is to use the least squares in the absence of attacks and the least median squares in the presence of attacks, since the latter alternative tolerates up to 50% outliers and still provides correct estimates. The authors show that the use of traditional Euclidean distance is not robust to intentional attacks against base stations, and they introduce robustness to ngerprinting localization by means of a median-based distance metric. Liu et al. [31] propose a method that uses the MMSE (Minimum Mean Squared Estimation), which is a data fusion technique for obtaining improved estimations, to identify and remove malicious location information. In this method, sensor locations are estimated using the MMSE-based method. Then, the method veri es whether the estimated location can be estimated from a set of consistent location references. If not, the most inconsistent reference is identi ed and removed, and the node location is estimated again. It repeats this process until all inconsistent references are discarded. The mean square error of the distance measurements is used as an inconsistency indicator. A second method proposed by Liu et al. [31] is a voting-based location estimation technique. In this method, the sensor eld is quantized into a grid of cells, and each reference node votes on the cells to which an unknown node may belong. Then, the method selects the most voted cell(s) and uses the center of these cell(s) as the estimated location. Voting results can be re ned interactively to improve accuracy. This method requires few resources and is suitable for current resource constrained sensor nodes. 18.4.4 Location Veri cation If all previous techniques fail to provide the required security, it is still possible to check the computed positions using redundant information available on the nodes and in the network. This is the core of some proposed works that focus on the reliability of the nal position computations rather than on avoiding or detecting compromised nodes and attacks. For instance, LAD [32] uses deployment knowledge, with a group-based deployment model, to check whether the computed positions of the nodes are consistent with the known model and observations. In reference 33 an algorithm is proposed for in-region veri cation, in which a certain node can check whether another node really is inside the particular region that it claims to be. The proposed protocol, called Echo, uses known physical properties of both radio-frequency and ultrasound to compute distances and check whether a node really can be inside the claimed region. These techniques can be used to provide a layer of security for all three localization system components, since they only verify the result of the overall localization system. 18.4.5 Secure and Simple Algorithms Localization systems are vulnerable mostly due to the number of components available to be attacked. Another way to secure a localization system is to use simple, less
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dependable localization algorithms, such as GPS-free, range-free, and/or one hop algorithms. One example is SeRLoc [25], in which beacon nodes are equipped with a set of higher-power-directional antennas. These nodes send a packet using an asymmetric transmission that contains their position and the sector of the antenna in which the packets are sent. Because it is a range-free single-hop localization algorithm, it is protected against attacks aiming at altering range measurements and against regular compromised nodes. However, it does not protect against wormholes, which are avoided by checking network properties such as sector uniqueness and communication range. A technique similar to SeRLoc is used in HiRLoc [24], which has a greater accuracy but an increased computational and communication complexity. Techniques like these can be used to protect the Localization Algorithm Component. Another simple and secure algorithm is the already-mentioned Localization with a Mobile Beacon (MBL) [23]. Although MBL is not designed to be a secure algorithm and the authors do not mention anything about security in their work, the algorithm used in the MBL is quite simple, where a mobile beacon walks the network and broadcasts its position information to near nodes. In this case, regular nodes only need to listen to the beacon node and they never exchange messages among themselves. By being a simple single-hop algorithm, this localization system is secured against a number of distributed attacks such as the wormhole attack. 18.4.6 Comparison of Current Solutions A widely known fact in network security is that there is no system that is totally safe and secure. There will always be weak points and the question is simply whether they are acceptable. In WSNs, this issue becomes a little more complicated due to resource limitations. In this case, we need to decide on the required level of security, which is application-dependent, and how many resources can be spent in providing these levels of security. Depending on this cost bene t analysis, we can decide which solution or what security techniques will be used to secure the WSN. In Table 18.1, we compare each of the studied proposals, showing which type of security they use as well as some observations about them and their potential weaknesses. As we can see, most security proposals rely on some kind of lightweight cryptography as a second line of defense combined with other security techniques such as misbehavior detection, robust position computation, location veri cation, and simple algorithms combined with extra hardware.
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18.5 CONCLUSIONS In this chapter, localization systems were studied under the viewpoint of security. We showed how an insecure localization system can be attacked in a number of ways to compromise the whole functioning of a WSN and thus lead to incorrect military plans and decision-making. First, the localization systems were divided into three different
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