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

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

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

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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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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 of sparse signals with censoring sensors in clustered sensor networks

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INFORMATION FUSION 2022年 83卷 1-18页

作者： Li, Chengxi Li, Gang Varshney, Pramod K. Tsinghua Univ Beijing 100084 Peoples R China Syracuse Univ Syracuse NY 13244 USA

In this paper, we explore the distributed detection of sparse signals in energy-limited clustered sensor networks (CSNs). For this problem, the centralized detector based on locally most powerful test (LMPT) methodology that uses the analog data transmitted by all the sensor nodes in CSNs can be easily realized according to the prior work. However, for the centralized LMPT detector, the energy consumption caused by data transmission is excessively high, which makes its implementation in CSNs with limited energy supply impractical. To address this issue, we propose a new detector by combining the advantages of censoring and LMPT strategies, in which both the cluster head (CLH) nodes and the ordinary (ORD) nodes only send data deemed to be informative enough and the fusion center (FC) fuses the received data based on LMPT methodology. The detection performance of the proposed detector, characterized by Fisher Information, is analyzed in the asymptotic regime. Also, we analytically derive the relationship between the detection performance of the proposed censoring-based LMPT (cens-LMPT) detector and the communication rates, both of which are controlled by the censoring thresholds. We present an illustrative example by considering the detection problem with 2-CSNs, i.e., CSNs in which each cluster contains two nodes, and provide corresponding theoretical analysis and simulation results.

关键词： Censoring distributed detection Locally most powerful tests Sparse signals Clustered sensor networks

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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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Behavioral Utility-Based distributed detection With Conditionally Independent Observations

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3717-3730页

作者： Dulek, Berkan Efendi, Emre Varshney, Pramod K. Hacettepe Univ Dept Elect & Elect Engn Beytepe Campus TR-06800 Ankara Turkiye Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

This paper establishes a mathematical framework to analyze the behavioral utility-based distributed detection problem for M-ary hypothesis testing with conditionally independent observations at the local decision agents (DAs). It is assumed that a human acts as the fusion center (FC) and his subjective perception of probabilities and gains/losses are considered using a prospect theoretic approach. In contrast with the classical Bayes risk-based approach, the nonlinear dependence of the behavioral performance metric on the likelihood functions necessitates a novel perspective to analyze the problem. Using geometric properties of the set of all possible probability distributions induced by randomized decision rules, the forms of optimal decision rules at the local DAs and the FC are characterized. In particular, it is shown that randomization between at most two distinct likelihood ratio vector quantizers, each of which partitions the nonnegative orthant into convex polytopes, attains optimal performance. The simplification to the case of binary quantization at a local DA for the binary hypothesis testing problem along with illustrative examples and performance comparisons are presented to corroborate theoretical results.

关键词： Testing Bayes methods Sensors Costs Quantization (signal) Decision making Sensor systems distributed detection behavioral utility prospect theory information fusion likelihood ratio quantizer randomized decision rule

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distributed detection with Unreliable Reporting Channels: Quantize or Not Quantize? 57

Distributed Detection with Unreliable Reporting Channels: Qu...

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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Cao, Lei Viswanathan, Ramanarayanan University of Mississippi Dept. of Electrical and Computer Engineering OxfordMS38677 United States

ISBN: (纸本)9781665451819

It is known that reporting the raw data observed at sensors to the fusion center completely and without any channel distortion always performs better than reporting the quantized data. However, when channel noise exists as it is always in practice, we show whether it is optimal to send the unquantized raw data or quantized data is a very interesting question that requires more research efforts. We compare these two schemes under the constraints of using the same transmission power and the bandwidth where analog modulation is assumed per channel use. The detection performance with one sensor and two sensors is presented in terms of the Bayes error. The performance of using asymptotically many sensors is compared with the Chernoff information. What presented in the paper could open up a new perspective in the design of the optimal distributed detection systems. © 2023 IEEE.

关键词： centralized or decentralized distributed detection quantize or not quantize

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On distributed detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2023年第4期7卷 1692-1708页

作者： Ardeshiri, Ghazaleh Vosoughi, Azadeh Univ Cent Florida Dept Elect & Comp Engn ECE Orlando FL 32816 USA

We consider a network of N sensors, tasked with solving binary distributed detection, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is equipped with a finite-size battery to store randomly arrived energy. Sensors process their observations and transmit their symbols to the FC over orthogonal and Markovian time correlated fading channels. The FC fuses the received symbols and makes a global binary decision. We aim at developing adaptive channel-dependent transmit power control policies such that J-divergence based detection metric is maximized at the FC, subject to total transmit power constraint. Modeling quantized fading channel, energy arrival, and battery dynamics as time-homogeneous finite-state Markov chains, and the network lifetime as a geometric random variable, we formulate our power control optimization problem as a discounted infinite-horizon constrained Markov decision process (MDP) problem, where sensors' transmit powers are functions of the battery states, quantized channel gains, and the arrived energies. We utilize stochastic dynamic programming and Lagrangian approach to find the optimal and sub-optimal power control policies. We demonstrate that our sub-optimal policy provides a close-to-optimal performance with a reduced computational complexity and without imposing signaling overhead on sensors.

关键词： Adaptive channel-dependent power control channel gain quantization distributed detection energy harvesting J-divergence finite size battery geometrically distributed network lifetime limited feedback Markov decision process Markovian time correlated fading model optimal and sub-optimal policies time-homogeneous finite-state Markov chain

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