Browsing by Author "Varshney, Pramod K."

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A Low-Complexity Maximum-Likelihood Decoder for Tail-Biting Convolutional Codes
(2018-05-01)
Han, Yunghsiang S.
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Wu, Ting-Yi
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Chen, Po-Ning
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Varshney, Pramod K.
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電機工程學系
Due to the growing interest in applying tail-biting convolutional coding techniques in real-time communication systems, fast decoding of tail-biting convolutional codes has become an important research direction. In this paper, a new maximumlikelihood decoder for tail-biting convolutional codes is proposed. It is named bidirectional priority-first search algorithm (BiPFSA) because priority-first search algorithm has been used both in forward and backward directions during decoding. Simulations involving the antipodal transmission of (2, 1, 6) and (2, 1, 12) tail-biting convolutional codes over additive white Gaussian noise channels shows that BiPFSA not only has the least average decoding complexity among the state-of-the-art decoding algorithms for tail-biting convolutional codes but can also provide a highly stable decoding complexity with respect to growing information length and code constraint length. More strikingly, at high SNR, its average decoding complexity can even approach the ideal benchmark complexity, obtained under a perfect noise-free scenario by any sequential-type decoding. This demonstrates the superiority of BiPFSA in terms of decoding efficiency.
Fault-tolerance analysis of a wireless sensor network with distributed classification codes
(2006)
Chen, Po-Ning
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Wang, Tsang-Yi
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Han, Yunghsiang S.
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Varshney, Pramod K.
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Yao, Chien
In this work, we analyze the performance of a wireless sensor network with distributed classification codes, where independence across sensors, including local observations, local classifications and sensor-fusion link noises, is assumed. In terms of large deviations technique, we establish the necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity. With the necessary and sufficient condition and the upper performance bounds, the relation between the fault-tolerance capability of a distributed classification code and its pair-wise Hamming distances is characterized.
Local Threshold Design for Target Localization Using Error Correcting Codes in Wireless Sensor Networks in the Presence of Byzantine Attacks
(2017-07-01)
Wei, Chun-Yi
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Chen, Po-Ning
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Han, Yunghsiang S.
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Varshney, Pramod K.
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電信工程研究所
In this paper, we revisit the received signal strength (RSS)-based target localization technique presented in Vempaty et al., where a simple threshold quantizer was employed to quantize the RSS values prior to sending them to the fusion center. It was shown that the probability of misclassification of the distributed classification fusion using error correcting codes scheme vanishes as the number of sensors tends to infinity. This result was obtained based on an intuitive threshold design at the local sensors, and the question of how much a careful design of local thresholds can help improve the overall performance was not addressed. In this paper, we demonstrate the significance of threshold design for accurate and robust target localization in wireless sensor networks, particularly, when the number of sensors is finite. With this objective, we derive an upper bound on the probability of misclassification as a function of RSS thresholds by using the union inequality. The RSS thresholds that algorithmically minimize the derived misclassification error bound are then numerically obtained over a mirror-based homomorphic sensor deployment structure. Simulations over fading wireless links show that the scheme based on newly found optimized RSS thresholds considerably outperforms the previous scheme using the thresholds that are intuitively selected, especially in the presence of Byzantine attacks that severely impact information security.
Minimum Byzantine Effort for Blinding Distributed Detection in Wireless Sensor Networks
(2020-01-01)
Lin, Hsuan-Yin
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Chen, Po-Ning
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Han, Yunghsiang S.
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Varshney, Pramod K.
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電信工程研究所
In this work, we consider the general problem of distributed detection in the presence of Byzantines using wireless sensor networks. Instead of attempting to mitigate Byzantine attacks as a system designer, we investigate the issue from the perspective of a Byzantine attacker. The probability for each individual sensor to be compromised (compromising probability) required to blind the system operation is adopted as the attack measure. Under the system setting that the fusion center (FC) declares the most likely hypothesis to be true based on the local sensors, a Byzantine attack policy that can blind the FC with the minimum compromising probability for each individual sensor is derived under the assumption that the Byzantine attacker knows the statistics of the local outputs. The closed-form expression for a blind-achieving Byzantine transition probability that is used to alter the statistics of the local outputs of compromised sensors is also established. Our results indicate that the statistics of the local outputs is essential for the minimization of an attacker's effort.
Optimal Byzantine Attack for Distributed Inference with M-ary Quantized Data
(2016)
Chen, Po-Ning
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Han, Yunghsiang S.
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Lin, Hsuan-Yin
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Varshney, Pramod K.
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電機工程學系
In many applications that employ wireless sensor networks (WSNs), robustness of distributed inference against Byzantine attacks is important. In this work, distributed inference is considered when local sensors send M-ary data to the fusion center. The optimal Byzantine attack policy is then derived under the assumption that the Byzantine adversary has the knowledge of the statistics of local quantization outputs. Our analysis indicates that the fusion center can be blinded such that the detection error is as poor as a random guess when an adequate fraction of sensors are compromised.
Performance analysis and code design for minimum hamming distance fusion in wireless sensor networks
(2007-05-01)
Yao, Chien
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Chen, Po-Ning
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Wang, Tsang-Yi
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Han, Yunghsiang S.
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Varshney, Pramod K.
Distributed classification fusion using error-correcting codes (DCFECC) has recently been proposed for wireless sensor networks operating in a harsh environment. It has been shown to have a considerably better capability against unexpected sensor faults than the optimal likelihood fusion. In this paper, We analyze the performance of a DCFECC code with minimum Hamming distance fusion. No assumption on identical distribution for local observations, as well as common marginal distribution for the additive noises of the wireless links, is made. In addition, sensors are allowed to employ their own local classification rules. Upper bounds on the probability of error that are valid for any finite number of sensors are derived based on large deviations technique. A necessary and sufficient. condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity is also established. With the necessary and sufficient condition and the upper error bounds, the relation between the fault-tolerance capability of a DCFECC code and its pair-wise Hamming distances is characterized, and can be used together with any code search criterion in finding the code with the desired fault-tolerance capability. Based on the above results, we further propose a code search criterion of much less complexity than the minimum Hamming distance fusion error criterion adopted earlier by the authors. This makes the code construction with acceptable fault-tolerance capability for a network with over a hundred of sensors practical. Simulation results show that the code determined based on the new criterion of much less complexity performs almost identically to the best code that minimizes the minimum Hamming distance fusion error. Also simulated and discussed are them performance trends of the codes searched based on the new simpler criterion with respect to the network size and the number of hypotheses.
Target Localization Using Sensor Location Knowledge in Wireless Sensor Networks
(2018-06-01)
Wei, Chun-Yi
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Lin, Hsuan-Yin
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Chen, Po-Ning
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Han, Yunghsiang S.
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Varshney, Pramod K.
Incorporating error correcting coding techniques into target localization provides an immediate advantage that existing decoding algorithms can he used to determine which area the target is most likely located in. The important knowledge of exact sensor positions is, however, ignored in these decoding algorithms. This letter revisits the problem and shows that based on the weighted average of sensor positions with binary weightings from local decisions, a newly proposed decoding criterion can achieve a much better accuracy in target localization than the soft- and hard-decision rules particularly when a certain number of sensors are under Byzantine attacks.