Properly locating sensor nodes is an important building block for a large subset of wireless sensor networks (WSN) applications. As a result, the performance of the WSN degrades significantly when misbehaving nodes report false location and distance information in order to fake their actual location. In this paper we propose a general distributed deterministic protocol for accurate identification of faking sensors in a WSN. Our scheme does rely on a subset of nodes that are not allowed to misbehave and are known to every node in the network. Thus, any subset of nodes is allowed to try faking its position. As in previous approaches, our protocol is based on distance evaluation techniques developed for WSN. On the positive side, we show that when the received signal strength (RSS) technique is used, our protocol handles at most @math faking sensors. Also, when the time of flight (ToF) technique is used, our protocol manages at most @math misbehaving sensors. On the negative side, we prove that no deterministic protocol can identify faking sensors if their number is @math . Thus our scheme is almost optimal with respect to the number of faking sensors. We discuss application of our technique in the trusted sensor model. More precisely our results can be used to minimize the number of trusted sensors that are needed to defeat faking ones.
Relaxing the assumption of trusted nodes makes the problem more challenging, and to our knowledge, has only been investigated very recently @cite_19 . We call this model where no trusted node preexists the (or ) model. The approach of @cite_19 is randomized and consists of two phases: distance measurement and filtering. In the distance measurement phase, sensors measure their distances to their neighbors, faking sensors being allowed to corrupt the distance measure technique. In the filtering phase each correct sensor randomly picks up @math so-called sensors. Next each sensor @math uses trilateration with respect to the chosen pivot sensors to compute the location of its neighbor @math . If there is a match between the announced location and the computed location, the @math link is added to the network, otherwise it is discarded. Of course, the chosen pivot sensors could be faking and lying, so the protocol may only give probabilistic guarantee.
@cite_19
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In an adversarial environment, various kinds of security attacks become possible if malicious nodes could claim fake locations that are different from where they are physically located. In this paper, we propose a secure localization mechanism that detects the existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in quite strong results: even when the number of phantom nodes is greater than that of honest nodes, we can Alter out most phantom nodes. Our analysis and simulations under realistic noisy settings demonstrate our scheme is effective in the presence of a large number of phantom nodes.