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distributed detection of a non-cooperative target via generalized locally-optimum approaches

INFORMATION FUSION 2017年 36卷 261-274页

作者： Ciuonzo, D. Rossi, P. Salvo Univ Naples Federico II DIETI Via Claudio 21 I-80125 Naples Italy Norwegian Univ Sci & Technol Dept Elect & Telecommun Trondheim Norway

In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum detection CLOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Decision fusion distributed detection GLRT LOD Bayesian approach Target detection

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distributed detection Over Channels with Memory

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IEEE SIGNAL PROCESSING LETTERS 2015年第12期22卷 2494-2498页

作者： Biswas, Nilanjan Ray, Priyadip Varshney, Pramod K. Indian Inst Technol GS Sanyal Sch Telecommun Kharagpur 721302 W Bengal India Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In this letter, we investigate distributed detection over channels with memory. We model the wireless channel between each sensor and the fusion center by a two state Markov chain and derive the optimal fusion rule. We demonstrate the improved detection performance of the derived fusion rule for burst error channels, over a channel aware fusion rule for a memoryless channel and the Chair-Varshney fusion rule. We further show that when the number of sensor decisions is large, as well as when the channel is memoryless, the derived fusion rule reduces to the well known counting rule.

关键词： Burst-error channel channel-aware decision fusion distributed detection

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distributed detection and Fusion of Weak Signals in Fading Channels with Non-Gaussian Noises

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IEEE COMMUNICATIONS LETTERS 2012年第2期16卷 220-223页

作者： Park, Jintae Shevlyakov, Georgy Kim, Kiseon WCU GIST Dept Nanobio Mat & Elect Sch Informat & Commun Kwangju 500712 South Korea St Petersburg State Polytech Univ Dept Appl Math St Petersburg 195251 Russia

distributed detection and information fusion have received recent research interest due to the success of emerging wireless sensor network (WSN) technologies. For the problem of distributed detection in WSNs under energy constraints, a weak signal model in the canonical parallel fusion scheme with additive non-Gaussian noises and fading channels is considered. To solve this problem in the Neyman-Pearson setting, a unified asymptotic fusion rule generalizing the maximum ratio combiner (MRC) fusion rule is proposed. Explicit formulas for the threshold and detection probability applicable for wide classes of fading channels and noise distributions are written out. Both asymptotic analysis and Monte Carlo modeling are used to examine the performance of the proposed detection fusion rule.

关键词： distributed detection decision fusion weak signal non-Gaussian noise wireless sensor networks

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distributed detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios

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IEEE SIGNAL PROCESSING LETTERS 2019年第12期26卷 1738-1742页

作者： Li, Chengxi He, You Wang, Xueqian Li, Gang Varshney, Pramod K. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Naval Aeronaut Univ Inst Informat Fus Yantai 264001 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.

关键词： Detectors Quantization (signal) Fuses Q measurement Testing Probability density function Indexes distributed detection locally most powerful tests sparse signals 1-bit quantization

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distributed detection OF NARROW-BAND SIGNALS

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IEEE TRANSACTIONS ON INFORMATION THEORY 1995年第2期41卷 519-523页

作者： BLUM, RS Lehigh Univ Bethlehem PA USA

distributed signal detection schemes have recently received significant attention, but most research has focused on cases with independent observations at the different sensors. Cases with dependent narrowband signals, which are of practical interest in communication, radar, and sonar problems, are studied here. The focus is on applications where the observations consist of a weak common signal in Possibly non-Gaussian additive noise which is independent from sensor to sensor. We find that the best (locally optimum) sensor test statistics for such cases are often different from the best test statistics for cases with isolated sensors and that these sensor test statistics may be nonsymmetric for highly symmetric problems. The possible difference between the best weak-signal distributed sensor test statistic and the best weak-signal test statistic far an isolated sensor can be shown to be due to the distributed sensors attempting to approximate the correlation terms found in the corresponding centralized tests.

关键词： distributed detection DECENTRALIZED detection NARROW-BAND SIGNALS LOCALLY OPTIMUM

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distributed detection in a large wireless sensor network

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INFORMATION FUSION 2006年第4期7卷 380-394页

作者： Niu, Ruixin Varshney, Pramod K. Cheng, Qi Syracuse Univ Dept EECS Syracuse NY 13244 USA

A distributed detection and decision fusion scheme is proposed for a wireless sensor network (WSN) consisting of a large number of sensors. At the fusion center, the total number of detections reported by local sensors are employed for hypothesis testing. Based on the assumption that the received signal power decays as the distance from the target increases, system level detection performance measures, namely probabilities of detection and false alarm, are derived analytically through approximation by using the central limit theorem (CLT). If the number of sensors is sufficiently large, the proposed fusion rule can provide very good system level detection performance, in the absence of the knowledge of local sensors' performances and at low signal to noise ratio (SNR). It is shown that for all the different system parameters we have explored, this fusion rule is equivalent to the optimal fusion rule, which requires much more prior information. To achieve a better system level detection performance, the local sensor level decision threshold should be designed optimally. Numerical methods are employed to find the optimal local sensor level threshold for different sets of system parameters. Guidelines on selecting the optimal local sensor level decision threshold are also provided. (C) 2005 Elsevier B.V. All rights reserved.

关键词： wireless sensor networks distributed detection decision fusion signal attenuation model

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distributed detection Over Blockchain-Aided Internet of Things in the Presence of Attacks

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023年 18卷 3445-3460页

作者： Jiang, Yiming Zhang, Jiangfan Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

distributed detection over a blockchain-aided Internet of Things (BIoT) network in the presence of attacks is considered, where the integrated blockchain is employed to secure data exchanges over the BIoT as well as data storage at the agents of the BIoT. We consider a general adversary model where attackers jointly exploit the vulnerability of IoT devices and that of the blockchain employed in the BIoT. The optimal attacking strategy which minimizes the Kullback-Leibler divergence is pursued. It can be shown that this optimization problem is nonconvex, and hence it is generally intractable to find the globally optimal solution to such a problem. To overcome this issue, we first propose a relaxation method that can convert the original nonconvex optimization problem into a convex optimization problem, and then the analytic expression for the optimal solution to the relaxed convex optimization problem is derived. The optimal value of the relaxed convex optimization problem provides a detection performance guarantee for the BIoT in the presence of attacks. In addition, we develop a coordinate descent algorithm which is based on a capped water-filling method to solve the relaxed convex optimization problem, and moreover, we show that the convergence of the proposed coordinate descent algorithm can be guaranteed.

关键词： Blockchains Internet of Things Biological system modeling Task analysis Convex functions Cloud computing Performance evaluation Blockchain double-spending attack distributed detection Kullback-Leibler divergence capped water-filling

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distributed detection for Centralized and Decentralized Millimeter Wave Massive MIMO Sensor Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第8期70卷 7665-7680页

作者： Chawla, Apoorva Singh, Rakesh Kumar Patel, Adarsh Jagannatham, Aditya K. Hanzo, Lajos Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 UP India Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi 175005 HP India Univ Southampton Dept Elect & Comp Sci Southampton SO17 1BJ Hants England

Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a partially connected structure (PCS) and a fully connected structure (FCS), respectively. The decision rules are based on the transmission of local binary sensor decisions and also take into account the accuracy of local detection at the individual sensors. Closed-form analytical expressions are derived for the probabilities of false alarm and correct detection to analyze the system's performance. Furthermore, the asymptotic distributed detection (DD) performance corresponding to both antenna architectures is analyzed in the large-scale antenna regime along with the pertinent power scaling laws. Additionally, digital signaling matrices are designed for enhancing the system performance. Our simulation results quantify the performance gains of the proposed architectures, which closely match the analytical results.

关键词： Millimeter wave distributed detection massive multiple-input multiple-output centralized distributed hybrid combining Neyman-Pearson criterion wireless sensor networks

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distributed detection Over Adaptive Networks Using Diffusion Adaptation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第5期59卷 1917-1932页

作者： Cattivelli, Federico S. Sayed, Ali H. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

We study the problem of distributed detection, where a set of nodes is required to decide between two hypotheses based on available measurements. We seek fully distributed and adaptive implementations, where all nodes make individual real-time decisions by communicating with their immediate neighbors only, and no fusion center is necessary. The proposed distributed detection algorithms are based on diffusion strategies [C. G. Lopes and A. H. Sayed, "Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis," IEEE Trans. Signal Process., vol. 56, no. 7, pp. 3122-3136, July 2008;F. S. Cattivelli and A. H. Sayed, " Diffusion LMS Strategies for distributed Estimation," IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1035-1048, March 2010;F. S. Cattivelli, C. G. Lopes, and A. H. Sayed, " Diffusion Recursive Least-Squares for distributed Estimation Over Adaptive Networks," IEEE Trans. Signal Process., vol. 56, no. 5, pp. 1865-1877, May 2008] for distributed estimation. Diffusion detection schemes are attractive in the context of wireless and sensor networks due to their scalability, improved robustness to node and link failure as compared to centralized schemes, and their potential to save energy and communication resources. The proposed algorithms are inherently adaptive and can track changes in the active hypothesis. We analyze the performance of the proposed algorithms in terms of their probabilities of detection and false alarm, and provide simulation results comparing with other cooperation schemes, including centralized processing and the case where there is no cooperation. Finally, we apply the proposed algorithms to the problem of spectrum sensing in cognitive radios.

关键词： Adaptive networks cognitive radios diffusion LMS diffusion networks diffusion RLS distributed detection distributed estimation hypothesis testing

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distributed detection with fuzzy censoring sensors in the presence of noise uncertainty

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NEUROCOMPUTING 2019年 351卷 196-204页

作者： Mohammadi, A. Javadi, S. H. Ciuonzo, D. Persico, V Pescape, A. Univ Bojnord Dept Elect Engn Bojnord Iran Univ Napoli Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy Network Measurement & Monitoring NM2 SRL Naples Italy

distributed detection is an essential task of wireless sensor networks (WSNs) with stringent limitations of energy and processing resources. In most of detection scenarios, the optimal threshold depends on the noise power which itself consists of some uncertainty in practice. In this paper, we use fuzzy hypothesis test (FHT) for modeling the noise power uncertainty. Moreover, we propose an optimal censoring scheme by applying the Neyman-Pearson lemma on the FHT. It is proved that the optimal censoring method is obtained by simply comparing the energy of the observed data with a threshold. Interestingly, we show that the threshold would depend on the local communication constraint as well as the noise uncertainty bound. We evaluate the performance of the proposed censoring algorithm by resorting to the equal gain combining (EGC) as the relevant fusion rule, using common receiver operating curves. Simulation results show the effectiveness of the proposed method in counteracting the uncertainty. (C) 2019 Published by Elsevier B.V.

关键词： Censoring sensor Cognitive radio distributed detection Energy detector Equal gain combining Fuzzy hypothesis test

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