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On Ordered Transmission Based distributed Gaussian Shift-in-Mean detection Under Byzantine Attacks

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 3343-3356页

作者： Quan, Chen Bulusu, Saikiran Geng, Baocheng Han, Yunghsiang S. Sriranga, Nandan Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 610054 Peoples R China

The ordered transmission based (OT-based) schemes reduce the number of transmissions needed in a distributed detection network without any loss in the probability of error performance. In this paper, we investigate the performance of a conventional OT-based system in the presence of additive Byzantine attacks in Gaussian shift in mean problems. In this work, by launching additive Byzantine attacks, attackers are able to alter the order as well as the data for the binary hypothesis testing problem. We also determine the optimal attack strategy for the Byzantine sensors. Furthermore, we analyze a communication efficient OT-based (CEOT-based) scheme in the presence of additive Byzantine attacks. We obtain the probabilities of error for both the OT-based system and the CEOT-based system under attack and evaluate the number of transmissions they save. We also derive analytical bounds for the number of transmissions saved in both systems under attack. Simulation results show that the additive Byzantine attacks have significant impact on the number of transmissions saved even when the signal strength is sufficiently large. A comparison of detection performance between the conventional OT-based system and the CEOT-based system reveals that the CEOT-based system is more robust to additive Byzantine attacks.

关键词： Sensors Sensor systems Additives Wireless sensor networks Sensor phenomena and characterization Signal processing algorithms Testing Ordered transmissions Byzantine attacks wireless sensor networks distributed detection

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distributed Binary Event detection Under Data-Falsification and Energy-Bandwidth Limitation

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IEEE SENSORS JOURNAL 2016年第16期16卷 6298-6309页

作者： Nurellari, Edmond McLernon, Des Ghogho, Mounir Aldalahmeh, Sami Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England Int Univ Rabat Rabat 11100 Morocco Al Zaytoonah Univ Jordan Amman 11942 Jordan

We address the problem of centralized detection of a binary event in the presence of falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidth constrained under-attack spatially uncorrelated distributed wireless sensor network. The SNs send their quantized test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach a final decision. First (considering that the FC and the attacker do not act strategically), we derive: 1) the FC optimal weight combining;2) the optimal SN to FC transmit power;and 3) the test statistic quantization bits that maximize the probability of detection (P-d). We also derive an expression for the attacker strategy that causes the maximum possible FC degradation. But in these expressions, both the optimum FC strategy and the attacker strategy require a-priori knowledge that cannot be obtained in practice. The performance analysis of sub-optimum FC strategies is then characterized, and based on the (compromised) SNs willingness to collaborate, we also derive analytically the sub-optimum attacker strategies. Then, considering that the FC and the attacker now act strategically, we recast the problem as a minimax game between the FC and the attacker and prove that the Nash equilibrium (NE) exists. Finally, we find this NE numerically in the simulation results and this gives insight into the detection performance of the proposed strategies.

关键词： distributed detection distributed processing falsified sensor nodes wireless sensor networks (WSN)

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distributed IVI-CFAR detection in non-homogeneous environments

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SIGNAL PROCESSING 2004年第7期84卷 1231-1237页

作者： Hammoudi, Z Soltani, F Univ Constantine Fac Sci Ingn Dept Elect Constantine 25000 Algeria

The performance of distributed constant false alarm rate (CFAR) detection using an improved version of the variability index (VI) CFAR as local detectors is analysed. The VI-CFAR which is a combination of the cell-averaging (CA) CFAR, smallest-of (SO) CFAR and the greatest-of (GO) CFAR is robust in non-homogeneous environments. In this work, we improve its robustness by making the VI-CFAR switch to the ordered statistics (OS) CFAR in multiple target situations where the interfering targets are present in both the halves of the reference window. Simulations for the "AND", "OR" and the "MAJORITY" fusion rules are conducted to assess the detection performance and the results are presented and discussed. (C) 2004 Elsevier B.V. All rights reserved.

关键词： distributed detection non-homogeneous environments

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Change detection for Large distributed Sensor Networks With Multitriggered Local Sensors

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IEEE ACCESS 2022年 10卷 44789-44803页

作者： Quarisa, Matthew W. Blostein, Steven D. Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada

The emergence of large sensor networks and the Internet of Things has reinvigorated interest into distributed quickest change detection. Important shortcomings of existing approaches are ease of design, flexibility in communication, and applicability to larger networks. The new approach proposed in this work features local sensors that can be triggered multiple times, i.e., can reset and continue monitoring after transmitting their decisions. With larger sensor networks as a focus, the system allows for multiple simultaneous transmissions to a fusion center within bandwidth limitations. The proposed system uses the cumulative-sum procedure at local sensors to binarize local decisions, which are then transmitted to the fusion center that also employs cumulative-sum quickest detection. Test overdesign due to sequential test overshoot is avoided, and global and local thresholds are chosen to meet a desired false alarm rate constraint using numerical computation of expected delay performance. The system compares favourably to several existing methods while offering greater flexibility in the amount of fusion center communication.

关键词： Delays Monitoring Quantization (signal) Design methodology Decision making Bayes methods Bandwidth Sequential change detection distributed detection sequential probability ratio test CUSUM sensor networks decision fusion

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Robust Sequential detection in distributed Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第21期66卷 5648-5662页

作者： Leonard, Mark R. Zoubir, Abdelhak M. Tech Univ Darmstadt Inst Telecommun Signal Proc Grp D-64283 Darmstadt Germany

We consider the problem of sequential binary hypothesis testing with a distributed sensor network in a non-Gaussian noise environment. To this end, we present a general formulation of the Consensus + Innovations Sequential Probability Ratio Test (CISPRT). Furthermore, we introduce two different concepts for robustifying the CISPRT and propose four different algorithms, namely the Least-Favorable-Density-CISPRT, the Median-CISPRT, the M-CISPRT, and the Myriad-CISPRT. Subsequently, we analyze their suitability for different binary hypothesis tests before verifying and evaluating their performance in a shift-in-mean and a shift-in-variance scenario for different network connectivities and amounts of noise contamination.

关键词： Sequential hypothesis testing sequential detection robustness distributed sensor networks distributed detection

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Bayesian fusion performance and system optimization for distributed stochastic Gaussian signal detection under communication constraints

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2007年第4期55卷 1238-1250页

作者： Jayaweera, Sudharman K. Wichita State Univ Dept Elect & Comp Engn Wichita KS 67260 USA

The problem of decentralized detection and fusion of a Gaussian signal is considered under the assumption of analog relay-amplifier local processing. It is shown that under an average global (system) power constraint, there always exists an optimal number of nodes that achieves the best possible performance under both orthogonal and nonorthogonal sensor-to-fusion center communication. Any increase in the number of nodes beyond the optimal value leads to degraded performance. This implies that each node needs to maintain a certain minimum received power level at the fusion center in order to make a useful contribution to the final decision. This is contrasted with the monotonic performance unprovement observed under individual node power constraints as well as in the case of deterministic signal detection under a global power constraint. Three communication scenarios are studied in detail: 1) orthogonal;2) equicorrelated;and 3) random signaling waveforms. In each case, error exponents and resulting bounds for Bayesian fusion performance are derived. A sensor system optimization method based on Bhattacharya error exponent, that leads to simple rules for determining the optimal number of nodes under a global average power constraint is also proposed.

关键词： data fusion distributed detection hypothesis testing large-system analysis random spreading random matrix theory sensor networks

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On the performance of distributed Neyman-Pearson detection systems

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2001年第1期31卷 78-83页

作者： Xiang, M Zhao, J Xian Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Northwestern Polytech Univ Coll Marine Engn Xian 710072 Peoples R China

The performance of a distributed Neyman-Pearson detection system is considered. We assume that the derision rules of the sensors are given and that decisions from different sensors are mutually independent conditioned on both hypotheses. The purpose of decision fusion is to improve the performance of the overall system, and we are interested to know under what conditions can a better performance be achieved at fusion center, and under what renditions cannot. We assume that the probabilities of detection and false alarm of the sensors can be different. By comparing the probability of detection at fusion renter with that of each of the sensors, with the probability of false alarm at fusion center constrained equal to that of the sensor, we give conditions for a better performance to be achieved at fusion center.

关键词： decision fusion rule distributed detection Neyman-Pearson criterion probability of detection

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A distributed approach for track occupancy detection

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CONTROL ENGINEERING PRACTICE 2014年第1期22卷 69-80页

作者： Chang, Ming Dong, Wei Ji, Yindong Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Res Inst Informat Technol Beijing 100084 Peoples R China

This paper investigates the problem of track occupancy detection in distributed settings. Track occupancy detection determines which tracks are occupied in a railway system. For each track, the Neyman-Pearson structure is applied to reach the local decision. Globally, it is a multiple hypotheses testing problem. The Bayesian approach is employed to minimize the probability of the global decision error. Based on the prior probabilities of multiple hypotheses and the approximation of the receiving operation characteristic curve of the local detector, a person-by-person optimization method is implemented to obtain the fusion rule and the local strategies off line. The results are illustrated through an example constructed from in situ devices. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Track occupancy detection Neyman-Pearson Generalized likelihood ratio test Bayesian approach distributed detection

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detection Performance Analysis of distributed-Processing Multistatic Radar System with Different Multivariate Dependence Models in Local Decisions

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IEICE TRANSACTIONS ON COMMUNICATIONS 2022年第9期E105B卷 1097-1104页

作者： Van Hung Pham Tuan Hung Nguyen Morishita, Hisashi Le Quy Don Tech Univ Dept Radar Technol 236 Hoang Quoc Viet Hanoi Vietnam Natl Def Acad Dept Elect & Elect Engn Yokosuka Kanagawa 2398686 Japan

In a previous study, we proposed a new method based on copula theory to evaluate the detection performance of distributed-processing multistatic radar systems, in which the dependence of local decisions was modeled by a Gaussian copula with linear dependence and no tail dependence. However, we also noted that one main limitation of the study was the lack of investigations on the tail-dependence and nonlinear dependence among local detectors' inputs whose densities have long tails and are often used to model clutter and wanted signals in high-resolution radars. In this work, we attempt to overcome this shortcoming by extending the application of the proposed method to several types of multivariate copula-based dependence models to clarify the effects of tail-dependence and different dependence models on the system detection performance in detail. Our careful analysis provides two interesting and important clarifications: first, the detection performance degrades significantly with tail dependence;and second, this degradation mainly originates from the upper tail dependence, while the lower tail and nonlinear dependence unexpectedly improve the system performance.

关键词： multistatic radar distributed detection K-distributed clutter multivariate copula tail-dependence

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distributed Active Sonar detection in Dependent K-distributed Clutter

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IEEE JOURNAL OF OCEANIC ENGINEERING 2009年第3期34卷 343-357页

作者： Abraham, Douglas A. CausaSci LLC Arlington VA 22205 USA

distributed active sonar systems exploit the simultaneous detection of targets on multiple receiver platforms to improve system performance. False alarm performance modeling for such systems typically assumes independence from sensor to sensor;however, false alarms from clutter (e. g., shipwrecks, mud volcanoes, or rock outcroppings) are expected to produce statistically dependent data. In this paper, a clutter model exhibiting intersensor dependence and having K-distributed marginal probability density functions (pdfs) is proposed and analyzed. Under the constraint of equal sensor-level performance, the m-of-n fusion processor is seen to be optimal for most cases of interest for all but the heaviest tailed clutter. The system-level probability of false alarm is derived for the m-of-n fusion processor and approximations are developed for the system-level probability of detection for common target models. In analyzing fusion performance, the AND processor (m = n) is seen to perform best in heavy clutter when the target model has some measure of intersensor consistency while m/n approximate to 0.25 is seen to perform best for the highly variable Gaussian target model. The performance of AND, OR (m = 1), and median fusion processors is seen to degrade as the clutter pdf tails increase, although the effect is confounded between clutter tail heaviness and intersensor dependence. The importance of accounting for intersensor dependence, however, is illustrated by an up to 10-dB overestimation of performance when heavy tailed clutter data are incorrectly assumed to be independent across sensors.

关键词： Active sonar clutter data fusion dependent data distributed detection K-distribution

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