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Multi-Bit distributed detection of Sparse Stochastic Signals Over Error-Prone Reporting Channels

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 881-893页

作者： Mao, Linlin Yan, Shefeng Sui, Zeping Li, Hongbin Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic signals. To achieve a trade-off between detection performance and system constraints, multi-bit quantizers are employed at local sensors. Then, two quantization strategies, namely raw quantization (RQ) and likelihood ratio quantization (LQ), are examined. The multi-bit quantized signals undergo encoding into binary codewords and are subsequently transmitted to the fusion center via error-prone reporting channels. Upon exploiting the locally most powerful test (LMPT) strategy, we devise two multi-bit LMPT detectors in which quantized raw observations and local likelihood ratios are fused respectively. Moreover, the asymptotic detection performance of the proposed quantized detectors is analyzed, and closed-form expressions for the detection and false alarm probabilities are derived. Furthermore, the multi-bit quantizer design criterion, considering both RQ and LQ, is then proposed to achieve near-optimal asymptotic performance for our proposed detectors. The normalized Fisher information and asymptotic relative efficiency are derived, serving as tools to analyze and compensate for the loss of information introduced by the quantization. Simulation results validate the effectiveness of the proposed detectors, especially in scenarios with low signal-to-noise ratios and poor channel conditions.

关键词： distributed detection error-prone channels multi-bit quantizer sparse signal wireless sensor networks

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ERROR EXPONENTS FOR distributed detection OF MARKOV SOURCES

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IEEE TRANSACTIONS ON INFORMATION THEORY 1994年第2期40卷 397-408页

作者： SHALABY, HMH PAPAMARCOU, A UNIV MARYLAND DEPT ELECT ENGNCOLLEGE PKMD 20742 UNIV MARYLAND INST SYST RESCOLLEGE PKMD 20742

The paper considers a binary hypothesis testing system in which two sensors simultaneously observe a discrete-time finite-valued stationary ergodic Markov source and transmit M-ary messages to a Neyman-Pearson central detector. The size M of the message alphabet increases at most subexponentially with the number of observations. The asymptotic behavior of the type II error rate is investigated as the number of observations increases to infinity, and the associated error exponent is obtained under mild assumptions on the source distributions. This exponent is independent of the test level epsilon and the actual codebook sizes M, is achieved by a universally optimal sequence of acceptance regions, and is characterized by an infimum of informational divergence rate over a class of infinite-dimensional distributions. Important differences-due to the observations being Markov-between the asymptotically optimal distributed tests and their nondistributed counterparts are highlighted. The converse results require a blowing-up lemma for stationary ergodic Markov sources, which is also proven.

关键词： HYPOTHESIS TESTING distributed detection ERROR EXPONENT MARKOV SOURCE DIVERGENCE RATE BLOWING-UP LEMMA

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NEW ASYMPTOTIC RESULTS IN PARALLEL distributed detection

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IEEE TRANSACTIONS ON INFORMATION THEORY 1993年第6期39卷 1847-1863页

作者： CHEN, PN PAPAMARCOU, A UNIV MARYLAND INST SYST RESCOLL PKMD 20742

The performance of a parallel distributed detection system is investigated as the number of sensors tends to infinity. It is assumed that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. The boundedness of the second moment of the postquantization log-likelihood ratio is examined in relation to the asymptotic error exponent. It is found that when that second moment is unbounded, the Neyman-Pearson error exponent can become a function of the test level, whereas the Bayes error exponent remains, as previously conjectured by Tsitsiklis, unaffected. Large deviations techniques are also employed to show that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents, but extends to the actual Bayes error probabilities up to a multiplicative constant.

关键词： distributed detection QUANTIZATION ERROR EXPONENTS LARGE DEVIATIONS ASYMPTOTIC EXPANSIONS

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Mitigation of Byzantine Attacks on distributed detection Systems Using Audit Bits

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2018年第1期4卷 18-32页

作者： Hashlamoun, Wael Brahma, Swastik Varshney, Pramod K. Birzeit Univ Dept Elect & Comp Engn Birzeit 71939 Palestine Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

This paper considers the problem of distributed detection in the presence of Byzantines who seek to degrade detection performance by falsifying data. This paper proposes a novelmechanism to mitigate Byzantine attacks by partitioning sensors into groups. Local decisions from sensors in each group are sent to the Fusion Center (FC) via multiple paths, which enable the FC to assess (i. e., to audit) the information that reaches it to improve detection performance. We introduce a weighted Kullback-Leibler Divergence (KLD) metric to measure the detection performance of the FC and show that the proposed mechanism is more robust against Byzantine attacks than previously proposed schemes. We prove that, using the proposed mechanism, the FC becomes blind (i. e., no useful information reaches the FC) only if all the nodes in the network are Byzantines. This paper also characterizes optimal Byzantine attacks in the scenario when the FC cannot be made blind. Further, the paper conducts game theoretic analysis to investigate the scenario when both the Byzantines and the FC act strategically against each other and prove the existence of a Nash Equilibrium. Extensive numerical results are provided throughout the paper that provide insights into the proposed distributed detection mechanism.

关键词： Byzantine attacks distributed detection game theory Kullback-Leibler divergence

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Hierarchical copula-based distributed detection

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SIGNAL PROCESSING 2019年 158卷 100-106页

作者： Javadi, S. Hamed Mohammadi, Abdolreza Farina, Alfonso Univ Bojnord Dept Elect Engn Bojnord Iran Selex ES Rome Italy

detection is one of the main tasks of wireless sensor networks (WSN) in many applications. The data of network nodes are usually assumed to be statistically independent since considering dependence results in intractable decision rules. However, detection performance degrades when the correlation rises. In this paper, a practically popular configuration of WSNs is considered where homogeneous nodes - each of them consisting of several heterogeneous sensors - send their processed data to a fusion center (FC). Here, data fusion should be implemented in two levels: 1. Nodes fuse the data of their sensors. 2. The FC takes the final decision by fusing the data of the nodes. We propose to implement a copula-based local decision rule in nodes. At the FC, it is shown that the correlation among nodes' decisions can be resolved by simply adding the logarithm of the sum of nodes' decisions to the statistic of the FC. The proposed distributed detection scheme is evaluated in a practical example of fire detection. Simulations show that considering correlation by implementing the proposed methods would improve detection performance. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Copula theory Decision fusion Dependency distributed detection Fire detection Wireless sensor networks

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Large Deviations Performance of Consensus plus Innovations distributed detection With Non-Gaussian Observations

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第11期60卷 5987-6002页

作者： Bajovic, Dragana Jakovetic, Dusan Moura, Jose M. F. Xavier, Joao Sinopoli, Bruno Univ Tecn Lisboa IST ISR Lisbon Portugal Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as centralized, with the same optimal asymptotic detection performance, but, below the threshold, distributed detection is suboptimal with respect to centralized detection. We determine this threshold and quantify the performance loss below threshold. Finally, we show the dependence of the threshold and of the performance on the distribution of the observations: the asymptotic performance of distributed detectors over the same random network with different observations' distributions, for example, Gaussian, Laplace, or quantized, may be different, even though the asymptotic performance of the corresponding centralized detectors is the same.

关键词： Chernoff information consensus plus innovations distributed detection information flow large deviations non-Gaussian distributions performance analysis random network

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Design of Binary Quantizers for distributed detection Under Secrecy Constraints

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2016年第10期64卷 2636-2648页

作者： Nadendla, V. Sriram Siddhardh Varshney, Pramod K. Syracuse Univ Syracuse NY 13244 USA

In this paper, we investigate the design of distributed detection networks in the presence of an eavesdropper (Eve). We consider the problem of designing binary sensor quantizers that maximize the Kullback-Leibler (KL) divergence at the fusion center (FC), when subject to a tolerable constraint on the KL divergence at Eve. We assume that the channels between the sensors and the FC (likewise the channels between the sensors and the Eve) are modeled as binary symmetric channels (BSCs). In the case of i.i.d. received symbols at both the FC and Eve, we prove that the structure of the optimal binary quantizers is a likelihood ratio test (LRT). We also present an algorithm to find the threshold of the optimal LRT, and illustrate it for the case of Additive white Gaussian noise (AWGN) observation models at the sensors. In the case of non-i.i.d. received symbols at both FC and Eve, we propose a dynamic-programming based algorithm to find efficient quantizers at the sensors. Numerical results are presented to illustrate the performance of the proposed network.

关键词： distributed detection wireless sensor networks eavesdroppers Kullback-Leibler divergence secrecy

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Power Constrained Partially Coherent distributed detection Over Fading Multiaccess Channels

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IEEE SENSORS JOURNAL 2013年第7期13卷 2729-2736页

作者： Xu, Zhenhua Huang, Jianguo Zhang, Qunfei Northwestern Polytech Univ Dept Elect & Commun Engn Xian 710072 Peoples R China

In this paper, we consider the problem of power constrained partially coherent distributed detection over fading multiaccess channels. The deflection coefficient maximization is used to optimize the performance of detectors. For a partially coherent distributed detection system, the phase synchronization is imperfect at the fusion center;the residual phase error is modeled by the Tikhonov probability distribution function. Two cases of the channel gain information are considered separately at the fusion center: one case with perfect channel gain [the corresponding method is referred to as partially coherent deflection coefficient maximization (PC-DCM)], the other case with statistical information of channel gain (the corresponding method is referred to as PC-DCM-CS, with CS being the abbreviation for "channel statistics"). We derive the closed-form solutions to the considered problems. Monte Carlo simulations are carried out to verify the performance of the proposed methods. Simulation results show that the newly proposed methods could significantly improve the detection performance of the fusion system under a low signal-to-noise ratio and low false alarm probability.

关键词： Deflection coefficient maximization distributed detection multiaccess channel partially coherent power constraint

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Subspace-Aided Data-Driven Robust distributed detection With Cooperative Fault Sensing for Large-Scale Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第8期20卷 10387-10397页

作者： Li, Biao Yang, Ying Peking Univ Coll Engn Dept Mech & Engn Sci State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Traditional data-driven fault detection methods based on subspace identification encounter difficulties when performing distributed fault detection on large-scale systems, mainly due to the presence of the unknown interaction term in the residual generator constructed for each subsystem. To tackle these problems, this article proposes a robust distributed fault detection method based on subspace identification for large-scale systems. First, the initial identification error of the residual generator constructed by each subsystem is obtained by using the input and output data information of the local and neighbors, and then a one-step correction theorem is introduced to minimize the error further. In addition, a robust residual generator that is sensitive to faults and robust to noise is constructed by studying all dimensions of the parity space. The effectiveness and superiority of the proposed method are illustrated by comparing the existing methods in two case studies of numerical simulation and real finishing mill system in hot strip rolling.

关键词： Cooperative fault sensing data-driven distributed detection large-scale systems robust residual generator subspace identification

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Minimum Byzantine Effort for Blinding distributed detection in Wireless Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 647-661页

作者： Lin, Hsuan-Yin Chen, Po-Ning Han, Yunghsiang S. Varshney, Pramod K. Natl Chiao Tung Univ Inst Commun Engn Hsinchu 30010 Taiwan Simula UiB N-5008 Bergen Norway Dongguan Univ Technol Sch Elect Engn & Intelhgentizat Dongguan 523808 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

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.

关键词： distributed detection distributed inference network security wireless sensor networks Byzantine attacks

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