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Hierarchical copula-based distributed detection

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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Providing Spectrum Information Service Using TV White Space via distributed detection System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第8期68卷 7655-7667页

作者： Chen, Zhenjia Zhang, Yonghui Hainan Univ Dept Was Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China

Electromagnetic spectrum resources are a very important resource. With the rapid development of radio technology, electromagnetic spectrum resources have become scarce. Abuse of spectrum resources and interference with primary user often occur. Pre-sensing the electromagnetic environment and performing dynamic spectrum access is the direction of future wireless technology development. This paper focuses on the electromagnetic spectrum information service platform. We design a spectrum detection node in combination with an embedded system. The distributed spectrum detection network is constructed by using the TV White Space as the communication frequency band of the backbone network. From the perspective of signal source, research on detection and identification methods based on radio signal characteristics improves the efficiency of electromagnetic spectrum detection. A phase extreme range estimation method based on radio frequency I/Q data is proposed to improve signal estimation accuracy in a low signal-noise-ratio environment. The interquartile range is proposed to distinguish noise and signal. The experiment results show that distributed electromagnetic spectrum information service platform based on signal characteristics can improve detection accuracy and increase system flexibility.

关键词： Multicast phase extreme range estimation TV White Space (TVWS) distributed detection

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Fusion Rules for distributed detection in Clustered Wireless Sensor Networks With Imperfect Channels

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2019年第3期5卷 585-597页

作者： Aldalahmeh, Sami Ahmed Al-Jazzar, Saleh O. McLernon, Des Zaidi, Syed Ali Raza Ghogho, Mounir Al Zaytoonah Univ Jordan Fac Engn & Technol Amman 11733 Jordan Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England Univ Int Rabat Dept Comp Sci Rabat 11100 Morocco

In this paper, we investigate fusion rules for distributed detection in large random clustered wireless sensor networks with a three-tier hierarchy;the sensor nodes (SNs), the cluster heads (CHs), and the fusion center (FC). The CHs collect the SNs' local decisions and relay them to the FC, which then fuses them to reach the ultimate decision. The SN-CH and the CH-FC channels suffer from additive white Gaussian noise. In this context, we derive the optimal log-likelihood ratio (LLR) fusion rule, which turns out to be intractable. So, we develop a sub-optimal linear fusion rule (LFR) that weighs the cluster's data according to both its local detection performance and the quality of the communication channels. In order to implement it, we propose an approximate maximum likelihood based LFR (LFR-aML), which estimates the required parameters for the LFR. We also derive Gaussian-tail upper bounds for the detection and false alarms probabilities for the LFR. Furthermore, an optimal CH transmission power allocation strategy is developed by solving the Karush-Kuhn-Tucker conditions for the related optimization problem. Extensive simulations show that the LFR attains a detection performance near to that of the optimal LLR and confirms the validity of the proposed upper bounds. Moreover, when compared to equal power allocation, simulations show that our proposed power allocation strategy achieves a significant power saving at the expense of a small reduction in the detection performance.

关键词： Wireless sensor networks (WSNs) cluster head (CH) distributed detection decision fusion power allocation strategy KKT conditions

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ON THE EFFECT OF SPATIAL CORRELATION ON distributed ENERGY detection OF A STOCHASTIC PROCESS

ON THE EFFECT OF SPATIAL CORRELATION ON DISTRIBUTED ENERGY D...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Augusto Maya, Juan Rey Vega, Leonardo CSC CONICET Buenos Aires DF Argentina Univ Buenos Aires Buenos Aires DF Argentina

ISBN: (纸本)9781728176055

We consider the problem of distributed detection over a network of sensors using energy measurements. Assuming that the problem is to detect the presence of stochastic signal with arbitrary temporal correlation, we exactly compute the characteristic function of the energy measurement at each sensing node. As it is shown, when the time-bandwidth product WT is large enough, the exact joint probability density function of the measurements at the sensor sites can be closely approximated by a product of non-trivial density functions that depend of some unknown parameters of the source signal and the sensor network. As those parameters can be easily estimated at each sensor node, we propose a modified generalized likelihood ratio test (GLRT). Through numerical experiments we show that this proposal has performance close to the optimal one which use the exact joint density function and with a significant reduction in the consumption of network resources as bandwidth and transmission power.

关键词： sensor networks distributed detection characteristic function energy detection

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distributed detection of Gauss-Markov signals using diffusion Kalman filtering

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SIGNAL PROCESSING 2018年 153卷 368-378页

作者： Pang, Feifei Dogancay, Kutluyil Zhang, Qunfei Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Univ South Australia Sch Engn Mawson Lakes SA 5095 Australia Univ South Australia Sch Informat Technol & Math Sci Inst Telecommun Res Mawson Lakes SA 5095 Australia

This paper addresses the problem of distributed detection of Gauss-Markov signals in adaptive networks, where nodes make individual decisions by exchanging information with their neighbours and no fusion centre is used. Relying on the connection between the log-likelihood ratio (LLR) and the innovations process, a distributed detection algorithm for Gauss-Markov signals is proposed based on diffusion Kalman filtering. To perform hypothesis testing, a LLR is constructed using the innovations estimated by the diffusion Kalman filter. The detection performance of the new algorithm is analysed by deriving approximate expressions for the probability of false alarm and detection. The mean and mean-square value of the test statistic, and the probability of detection and receiver operating characteristics of the algorithm are demonstrated by way of Monte Carlo simulations, where a good agreement between theoretical and simulation results is observed. Simulations also confirm the improved detection performance of the proposed algorithm over non-cooperative detection and show that the performance of the algorithm converges to that of the centralized scheme. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Gauss-Markov signals Adaptive networks distributed detection Diffusion scheme Kalman filter

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distributed Combining Techniques for distributed detection in Fading Wireless Sensor Networks 2

Distributed Combining Techniques for Distributed Detection i...

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2nd IEEE Middle East and North Africa COMMunications Conference (IEEE MENACOMM)

作者： Aldalahmeh, Sami Al-Jazzar, Saleh O. McLernon, Des Zaidi, Syed Ali Raza Cardenas, Marco Al Zaytoonah Univ Jordan Fac Engn & Technol Amman Jordan Univ Leeds Sch Elect & Elect Engn Leeds W Yorkshire England Autonomous Univ San Luis Potosi Fac Sci Mexico City DF Mexico

ISBN: (纸本)9781728136875

We investigate distributed combining techniques for distributed detection in wireless sensor networks (WSNs) over Rayleigh fading multiple access channel (MAC). The MAC also suffers from with path loss and additive noise. The WSN is modelled as a Poisson point process (PPP). Two distributed transmit combining techniques are proposed to mitigate fading;distributed equal gain transmit combining (ddEGTC) and distributed maximum ratio transmit combining (dMRTC). The performance of the previous methods is analysed using stochastic geometry tools, where the mean and variance of the detector's test statistic are found thus enabling the fitting of the received signal distribution by a log-normal distribution. Surprisingly, simulation results show a that ddEGTC outperforms dMRTC.

关键词： Wireless sensor networks distributed detection multiple access channel distributed transmit combining stochastic geometry

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Anonymous Heterogeneous distributed detection: Optimal Decision Rules, Error Exponents, and the Price of Anonymity

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第11期65卷 7390-7406页

作者： Chen, Wei-Ning Wang, I-Hsiang Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan

We explore the fundamental limits of heterogeneous distributed detection in an anonymous sensor network with n sensors and a single fusion center. The fusion center collects the single observation from each of the n sensors to detect a binary parameter. The sensors are clustered into multiple groups, and different groups follow different distributions under a given hypothesis. The key challenge for the fusion center is the anonymity of sensors-although it knows the exact number of sensors and the distribution of observations in each group, it does not know which group each sensor belongs to. It is hence natural to consider it as a composite hypothesis testing problem. First, we propose an optimal test called mixture likelihood ratio test, which is a randomized threshold test based on the ratio of the uniform mixture of all the possible distributions under one hypothesis to that under the other hypothesis. Optimality is shown by first arguing that there exists an optimal test that is symmetric, that is, it does not depend on the order of observations across the sensors, and then proving that the mixture likelihood ratio test is optimal among all symmetric tests. Second, we focus on the Neyman-Pearson setting and characterize the error exponent of the worst-case type-II error probability as n tends to infinity, assuming the number of sensors in each group is proportional to n. Finally, we generalize our result to find the collection of all achievable type-I and type-II error exponents, showing that the boundary of the region can be obtained by solving an optimization problem. Our results elucidate the price of anonymity in heterogeneous distributed detection, and can be extended to M-ary hypothesis testing with heterogeneous observations generated according to hidden latent variables.

关键词： distributed detection anonymity error exponent large deviation

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An Energy Allocation Method of Nodes in Energy Harvesting distributed detection System 4

An Energy Allocation Method of Nodes in Energy Harvesting Di...

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4th International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Liu Fei Liu Xiangyang Qi Zhengwei Natl Univ Def Technol Coll Informat & Commun Xian 710106 Shanxi Peoples R China

ISBN: (纸本)9781728151328

In the distributed detection system with energy harvesting, the uncertainty of node energy makes the optimal allocation of sensing and communication energy a challenge. This paper analyzed the influence of sensing uncertainty and communication uncertainty on detection system performance. The joint optimization of single slot node energy between sensing and communication was carried out by Monte Carlo simulation. The energy allocation method of nodes with the highest detection probability received by fusion center was determined, which laid a foundation for the establishment of node energy management strategy.

关键词： energy harvesting distributed detection energy allocation

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Key Generation for Secure distributed detection in IoT using Polar Quantization 53

Key Generation for Secure Distributed Detection in IoT using...

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53rd Asilomar Conference on Signals, Systems, and Computers (ACSSC)

作者： Hentila, Henri Koivunen, Visa Poor, H. Vincent Aalto Univ Dept Signal Proc & Acoust Sch Elect Engn Espoo Finland Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781728143002

The problem of securing the communication in an IoT sensor network from malicious eavesdroppers is studied. One-time pad encryption is found to be a suitable encryption method for this type of setting, reducing the problem to the generation of secret keys to be used for the encryption. We propose a novel physical-layer security based key generation protocol where a Gaussian source derived from the wireless communicaton channel between key generating pairs is transformed into an erasure channel. The proposed method is shown to outperform other similar methods when allowing for a higher BER in the generated keys and constraining the rate of communication. We also analyze how the BER of the generated keys affects the underlying inference task of the sensor network.

关键词： distributed detection physical-layer security secret key generation AWGN polar quantization

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Online Linear Compression with Side Information for distributed detection of High Dimensional Signals 20

Online Linear Compression with Side Information for Distribu...

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20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Khanduri, Prashant Theagarajan, Lakshmi Narasimhan Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Indian Inst Technol Palakkad Dept Elect Engn Palakkad 678557 Kerala India

ISBN: (纸本)9781538665282

In this paper, we propose a novel near-optimal linear compression strategy at the local sensors for the distributed detection of unknown high dimensional signals in a wireless sensor network (WSN). The WSN consists of multiple sensors distributed in a region of interest (RoI) and a fusion center (FC). The signal is assumed to be unknown to the local sensors and the FC;however, we assume that the sensors have some side information about the signal to be detected. Specifically, the sensors possess the knowledge of the signs of the individual components of the signal vector. Using this sign information, we design a linear compression strategy which is employed by the local sensors to compress the collected spatio-temporal data before forwarding it to the FC. We analytically show that the proposed compression strategy can achieve near-optimal error exponents. Further, the proposed compression strategy provides robust performance which is unaffected by the signal dimension as opposed to other state-of-the-art compression strategies whose error exponents are shown to decay with the signal dimension.

关键词： Wireless sensor networks distributed detection linear compression dimensionality reduction

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