Wireless Sensor Networks (WSNs) are vulnerable to Byzantine attacks in which malicious sensors send falsified information to the Fusion Center (FC) with the goal of degrading inference performance. In this paper, we consider Byzantine attacks for the location estimation task in WSNs using binary quantized data. Posterior Cramér-Rao Lower Bound (PCRLB) is used to characterize the performance of the network. Two kinds of attack strategies are considered: Independent and Collaborative attacks. We determine the fraction of Byzantine attackers in the network that make the FC incapable of utilizing sensor information to estimate the target location. Optimal attacking strategies for given attacking resources are also derived. Furthermore, we propose two schemes for the mitigation of Byzantine attacks. The first scheme is based on a Byzantine Identification method under the assumption of identical local quantizers. We show with simulations that the proposed scheme identifies most of the Byzantines. In order to improve the performance, we propose a second scheme in conjunction with our identification scheme where dynamic non-identical threshold quantizers are used at the sensors. We show that it not only reduces the location estimation error but also makes the Byzantines `ineffective\u27 in their attack strategy
To form a collocated triad of orthogonally oriented dipole(s) and/or loop(s), 20 different compositions are possible. For each such composition: 1) closed-form formulas are produced here to estimate the azimuth-elevation direction-of-arrival and the polarization-parameters from an ambiguous steering vector subject to an unknown complex-value multiplicative coefficient; or 2) reasoning is given why such estimation is inviable.
This paper considers the problem of Byzantine attacks on cooperative spectrum sensing in cognitive radio networks. Our major contribution is a technique to learn about the cognitive radio (CR) potential malicious behavior over time and thereby identifies the Byzantines and then estimates their probabilities of false alarm (P f a ) and detection (PD). We show that for a given set of data over time, the Byzantines can be identified for any α (percentage of Byzantines). It has also been shown that these estimates of P f a and PD of the Byzantines are asymptotically unbiased and converge to their true values at the rate of O(T −1/2 ). We then use these probabilities to adaptively design the fusion rule. We calculate the Probability of error (Qe) and compare it with the minimum probability of error possible.
For a triad of short dipoles (or of small loops), in perpendicular orientation relative to each other but collocated in space, this study derives a lower bound for their error in direction-of-arrival estimation and polarisation estimation, accounting for the possibility of failure in any individual dipoles (or loops).
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