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Optimal Sensor Decision Rules for Quantized-but-Uncoded distributed detection

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 286-290页

作者： Cao, Lei Viswanathan, Ramanarayanan Univ Mississippi Dept Elect & Comp Engn University MS 38677 USA

In conventional codeword-based distributed detection (CDD), sensors quantize their observations and report codewords to the fusion center (FC) where a final decision is made regarding the truthfulness of the hypotheses. Recently, quantized-but-uncoded DD (QDD) has been proposed, where sensors, after quantization, transmit summarized values instead of codewords to the FC. QDD can adapt well to the power constraint and offers better detection performance than CDD. However, the added degree of freedom in parameter selection in QDD comes with high complexity in optimal system design. The contribution of this letter is a proof showing that in QDD, the optimal sensor decision rules for binary decisions are likelihood-ratio-quantizers (LRQ), regardless of the reporting channel conditions, provided that the sensor observations are conditionally independent given the hypotheses. This property largely simplifies the design of QDD. Performance comparison is presented for CDD, QDD, and a benchmark system that reports original sensor observations, when both sensing and reporting channel noise exist.

关键词： Symbols Optimization Quantization (signal) Sensors Bayes methods System analysis and design Noise Complexity theory Quadrature amplitude modulation Light rail systems Codewords distributed detection likelihood-ratio-quantizer

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Resilient Average Consensus With Adversaries via distributed detection and Recovery

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 415-430页

作者： Yuan, Liwei Ishii, Hideaki Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Univ Tokyo Dept Informat Phys & Comp Tokyo 1138656 Japan

In this article, we study the problem of resilient average consensus (RAC) in multiagent systems where some of the agents are subject to failures or attacks. The objective of RAC is for nonfaulty/normal agents to converge to the average of their initial values despite the erroneous effects from malicious agents. To this end, we propose a successful distributed iterative RAC algorithm for the multiagent networks with general directed topologies. The proposed algorithm has two parts at each iteration: 1) detection and 2) averaging. For the detection part, we propose two distributed algorithms and one of them can detect malicious agents with only the information from direct in-neighbors. For the averaging part, we extend the applicability of an existing averaging algorithm where normal agents can remove the effects from malicious agents so far, after they are detected. Another important feature of our method is that it can handle the case where malicious agents are neighboring and collaborating with each other to mislead the normal ones from averaging. This case cannot be solved by existing detection approaches in related literature. Moreover, our algorithm is efficient in storage usage especially for large-scale networks as each agent only requires the values of neighbors within two hops. Lastly, numerical examples are given to verify the efficacy of the proposed algorithms.

关键词： Average consensus directed topologies distributed detection resilient consensus Average consensus directed topologies distributed detection resilient consensus

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distributed detection With Adaptive Topology and Nonideal Communication Channels

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第6期59卷 2857-2874页

作者： Ahmadi, Hamid R. Vosoughi, Azadeh Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

This paper investigates the increase in detection reliability that an adaptive network (with adaptive topology and channel-dependent decision and fusion rules) can provide, compared with a fixed topology network. We consider a network, consisting of N local detectors and a fusion center (FC), that is tasked with accessing the presence or absence of a phenomenon of interest (POI) at a specific location. The intensity of the signal emitted by the POI when present follows a power law attenuation model. Assuming that sensors transmit binary modulated local decisions over orthogonal nonideal channels, where these channels are modeled as additive white Gaussian noise (AWGN) or fading, we consider two classes of network topologies: 1) sequential topology;and 2) parallel topology. Under Bayesian criterion we derive the optimal channel-dependent rules and error probabilities for these topologies and establish the dependency of the errors on the received signal-to-noise-ratios (SNRs) of the sensing and communication channels. For N = 2, our extensive simulation results indicate that the optimality of parallel network is limited and demonstrate the average performance gain of topology adaptation. The results also reveal that the performance gain and the optimal topology are dictated by the system parameters (e.g., transmit powers and distances) and the design search space (i.e., whether the locations of the local detectors are fixed or optimized).

关键词： Bayesian criterion distributed detection network topology nonideal channels

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distributed detection Over Adaptive Networks: Refined Asymptotics and the Role of Connectivity

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2016年第4期2卷 442-460页

作者： Matta, Vincenzo Braca, Paolo Marano, Stefano Sayed, Ali H. Univ Salerno Dept Informat & Elect Engn & Appl Math I-84084 Fisciano SA Italy NATO STO Ctr Maritime Res & Expt I-19126 La Spezia Italy Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

We consider distributed detection problems over adaptive networks, where dispersed agents learn continually from streaming data by means of local interactions. The requirement of adaptation allows the network of detectors to track drifts in the underlying hypothesis. The requirement of cooperation allows each agent to deliver a performance superior to what would be obtained if it were acting individually. The simultaneous requirements of adaptation and cooperation are achieved by employing diffusion algorithms with constant step-size mu. By conducting a refined asymptotic analysis based on the mathematical framework of exact asymptotics, we arrive at a revealing understanding of the universal behavior of distributed detection over adaptive networks: as functions of 1/mu, the error (log-) probability curves corresponding to different agents stay nearly-parallel to each other (as already discovered in [1] and [2]), however, these curves are ordered following a criterion reflecting the degree of connectivity of each agent. Depending on the combination weights, the more connected an agent is, the lower its error probability curve will be. The analysis provides explicit analytical formulas for the detection error probabilities and these expressions are also verified by means of extensive simulations. We further enlarge the reference setting from the case of doubly-stochastic combination matrices considered in [1] and [2] to the more general and demanding setting of right-stochastic combination matrices;this extension poses new and interesting questions in terms of the interplay between the network topology, the combination weights, and the inference performance. The potential of the proposed methods is illustrated by application of the results to canonical detection problems, to typical network topologies, for both doubly-stochastic and right-stochastic combination matrices. Interesting and somehow unexpected behaviors emerge, and the lesson learned is that connectivity

关键词： distributed detection adaptive network diffusion strategy large deviations analysis exact asymptotics

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distributed detection in wireless sensor networks using dynamic sensor thresholds

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INTERNATIONAL JOURNAL OF distributed SENSOR NETWORKS 2008年第1期4卷 5-12页

作者： Ray, Priyadip Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

This paper presents a new approach for distributed target detection in wireless sensor networks (WSNs). Contrary to the conventional practice where every sensor uses an identical threshold for decision-making, an unequal and dynamic local sensor threshold selection scheme is proposed. This threshold selection scheme is based on a recently proposed statistical metric for multiple testing problems called the False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like the system level probability of detection and the probability of false alarm are studied. Simulation results are provided to demonstrate the effectiveness of this approach.

关键词： wireless sensor networks distributed detection decision fusion false discovery rate

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distributed detection in UWB Wireless Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2010年第2期58卷 804-813页

作者： Bai, Kai Tepedelenlioglu, Cihan Arizona State Univ Dept Elect Engn Fulton Sch Engn Tempe AZ 85287 USA

In this paper, we study distributed detection in ultrawideband (UWB) wireless sensor networks (WSNs) over frequency selective channels. Several schemes with different requirements on channel state information (CSI) are proposed, analyzed, and compared. We study the error exponent and asymptotic optimality for these schemes under different energy, and time-bandwidth product requirements. Our study shows that to achieve a nonzero error exponent, the no-feedback scheme requires large energy and time-bandwidth product but has advantages of no synchronization requirement and no feedback overhead. On the other hand, schemes requiring feedback are suitable for energy and bandwidth limited settings but require quasi-synchronization.

关键词： distributed detection error exponent large deviation sensor networks ultrawideband (UWB)

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distributed detection Fusion in Clustered Sensor Networks Over Multiple Access Fading Channels

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2022年 8卷 317-329页

作者： Aldalahmeh, Sami A. Ciuonzo, Domenico Al Zaytoonah Univ Jordan Dept Commun & Comp Engn Amman 11733 Jordan Univ Naples Federico II Dept Elect Engn & Informat Technol DIETI I-80138 Naples Amman Italy

In this paper, we tackle decision fusion for distributed detection in a randomly-deployed clustered Wireless Sensor Networks (WSNs) operating over a non-ideal multiple access channels (MACs), i.e. considering Rayleigh fading, path loss and additive noise. To mitigate fading, we propose the distributed equal gain transmit combining (dEGTC) and distributed maximum ratio transit combining (dMRTC). The first and second order statistics of the received signals were analytically computed via stochastic geometry tools. Then the distribution of the received signal over the MAC are approximated by Gaussian and log-normal distributions via moment matching. This enabled the derivation of moment matching optimal fusion rules (MOR) for both distributions. Moreover, suboptimal simpler fusion rules were also proposed, in which all the CHs data are equally weighed, which is termed moment matching equal gain fusion rule (MER). It is shown by simulations that increasing the number of clusters improve the performance. Moreover, MOR-Gaussian based algorithms are better under freespace propagation whereas their lognormal counterparts are more suited in the ground-reflection case. Also, the latter algorithms show better results in low SNR and SN numbers conditions. We have proved that the received power at the CH in MAC is proportional O(lambda R-2(2)) and to O(lambda(2) ln(2) R) in the free-space propagation and the ground-reflection cases respectively, where lambda. is SN deployment intensity and R is the cluster radius. This implies that having more clusters decreases the required transmission power for a given SNR at the receiver.

关键词： distributed detection decision fusion stochastic geometry multiple access channels fading path-loss wireless sensor networks

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distributed detection of information flows

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2008年第3期3卷 390-403页

作者： He, Ting Tong, Lang IBM TJ Watson Res Ctr Hawthorne NY 10532 USA Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

distributed detection of information flows is considered in which traffic sensors at different locations of a network observe transmission epochs. The traffic sensors communicate their measurements to a fusion center via channels with rate constraints, and the fusion center performs hypothesis testing for information flow detection. Under a nonparametric flow model where relayed packets can be perturbed up to bounded delays and multiplexed with chaff noise, flow detectability is characterized through a notion called consistency-rate function that shows the level of detectable flows under capacity constraints on the fusion channels. Achievability results are presented by constructing detection systems consisting of quantization, data transmission, and detection subsystems. In particular, slot-by-slot quantization schemes at the local sensors and threshold detection schemes at the fusion center are proposed to provide consistent detection with quantifiable performance.

关键词： distributed detection information flow information-theoretic limits traffic analysis

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distributed detection in wireless sensor networks using a multiple access channel

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2007年第3期55卷 822-833页

作者： Li, Wenjun Dai, Huaiyu N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

distributed detection in a one-dimensional (1-D) sensor network with correlated sensor observations, as exemplified by two problems-detection of a deterministic signal in correlated Gaussian noise and detection of a first-order autoregressive [AR(1)] signal in independent Gaussian noise, is studied in this paper. In contrast with the traditional approach where a bank of dedicated parallel access channels (PAC) is used for transmitting the sensor observations to the fusion center, we explore the possibility of employing a shared multiple access channel (MAC), which significantly reduces the bandwidth requirement or detection delay. We assume that local observations are mapped according to a certain function subject to a power constraint. Using the large deviation approach, we demonstrate that for the deterministic signal in correlated noise problem, with a specially chosen mapping rule, MAC fusion achieves the same asymptotic performance as centralized detection under the average power constraint (APC), while there is always a loss in error exponents associated with PAC fusion. Under the total power constraint (TPC), MAC fusion still results in exponential decay in error exponents with the number of sensors, while PAC fusion does not. For the AR signal problem, we propose a suboptimal MAC mapping rule which performs closely to centralized detection for weakly correlated signals at almost all signal-to-noise ratio (SNR) values, and for heavily correlated signals when SNR is either high or low. Finally, we show that although the lack of MAC synchronization always causes a degradation in error exponents, such degradation is negligible when the phase mismatch among sensors is sufficiently small.

关键词： autoregressive processes distributed detection error exponents multiple access channel sensor networks

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distributed detection in Sensor Networks With Limited Range Multimodal Sensors

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2010年第2期58卷 843-858页

作者： Ermis, Erhan Baki Saligrama, Venkatesh Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA

We consider a multiobject detection problem over a sensor network (SNET) with limited range multimodal sensors. The general problem complements the widely considered decentralized detection problem where all sensors observe the same object. We develop a distributed detection approach based on recent development of the false discovery rate (FDR) and the associated Benjamini-Hochberg (BH) procedure, which rank orders scalar test statistics. We first develop scalar test statistics for multidimensional data to handle multimodal sensor observations and establish its optimality in terms of the BH procedure. We then propose a distributed algorithm for an idealized model to detect the sensors that are in the immediate vicinity of an object. We show that the number of binary messages that need to be transmitted (communication cost) is upper bounded by the number of sensors that are in the vicinity of objects and is independent of the total number of sensors in the SNET. This brings forth an important principle for evaluating the performance of an SNET, namely, the need for scalability of communications and performance with respect to the number of objects or events in an SNET irrespective of the network size. We then account for nonideal models by developing robust extensions to our developments under the idealized model. The robustness properties ensure that both the error performance and communication cost degrade gracefully with interference.

关键词： distributed detection false discovery rate multivariate observations robust test statistics optimal test statistics

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