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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

Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable classification despite unreliable crowd workers. Coding-theory based techniques also allow us to pose easyto-answer binary questions to the crowd workers. We consider three different crowdsourcing models: systems with independent crowd workers, systems with peer-dependent reward schemes, and systems where workers have common sources of information. For each of these models, we analyze classification performance with the proposed codingbased scheme. We develop an ordering principle for the quality of crowds and describe how system performance changes with the quality of the crowd. We also show that pairing among workers and diversification of the questions help in improving system performance. We demonstrate the effectiveness of the proposed coding-based scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that use of good codes may improve the performance of the crowdsourcing task over typical majority-voting approaches.

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.

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Abstract-Consider designing an effective crowdsourcing system for an M -ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final result. We consider the novel scenario where workers have a reject option so they may skip microtasks when they are unable or choose not to respond. For example, in mismatched speech transcription, workers who do not know the language may not be able to respond to microtasks focused on phonological dimensions outside their categorical perception. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance. We evaluate system performance in both exact and asymptotic forms. Further, we consider the setting where there may be a set of greedy workers that complete microtasks even when they are unable to perform it reliably. We consider an oblivious and an expurgation strategy to deal with greedy workers, developing an algorithm to adaptively switch between the two based on the estimated fraction of greedy workers in the anonymous crowd. Simulation results show improved performance compared with conventional majority voting.

In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted relaxation of the norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

Abstract-In this work, we consider the task of target localization using quantized data in Wireless Sensor Networks (WSNs). We propose a computationally efficient localization scheme by modeling it as an iterative classification problem. We design coding theory based iterative approaches for target localization where at every iteration, the Fusion Center (FC) solves an M -ary hypothesis testing problem and decides the Region of Interest (ROI) for the next iteration. The coding theory based iterative approach works well even in the presence of Byzantine (malicious) sensors in the network. We further consider the effect of non-ideal channels. We suggest the use of soft-decision decoding to compensate for the loss due to the presence of fading channels between the local sensors and the FC. We evaluate the performance of the proposed schemes in terms of the Byzantine fault tolerance capability and probability of detection of the target region. We also present performance bounds which help us in designing the system. We provide asymptotic analysis of the proposed schemes and show that the schemes achieve perfect region detection irrespective of the noise variance when the number of sensors tends to infinity. Our numerical results show that the proposed schemes provide a similar performance in terms of Mean Square Error (MSE) as compared to the traditional Maximum Likelihood Estimation (MLE) but are computationally much more efficient and are resilient to errors due to Byzantines and non-ideal channels.