1,721,050 research outputs found
Distributed detection of a non-cooperative target via generalized locally-optimum approaches
No full textIn 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 (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity
Channel-aware decision fusion in MIMO wireless sensor networks
No full textThis chapter deals with a distributed version of the binary-hypothesis test which formalizes the case in which a wireless sensor network (WSN) is used for detecting a binary event, and a fusion center (FC) with multiple antennas collects the information for a robust decision. The presence of multiple antennas at both transmit and receive sides resembles a multiple-input-multiple-output (MIMO) system and allows for utilization of array-processing techniques providing spectral efficiency and fading mitigation. The problem is here referred to as MIMO decision fusion. Coherent decision fusion, i.e., the case when instantaneous channel state information (CSI) is available at the FC, is first studied: “Decode-and-Fuse” and “Decode-then-Fuse” approaches are introduced and compared. Successively, noncoherent decision fusion, i.e., the case when statistical CSI is available at the FC, is analyzed: the focus is on the energy test and related optimality characteristics
Distributed Detection Fusion in Clustered Sensor Networks over Multiple Access Fading Channels
No full textIn 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 free-space 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(λ 2 R2) and to O(λ 2 ln 2 R) in the free-space propagation and the ground-reflection cases respectively, where λ 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
Data fusion in wireless sensor networks: A statistical signal processing perspective
No full textThe role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks. The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols
Energy Detection for Decision Fusion in Wireless Sensor Networks over Ricean-Mixture Fading
No full textGeneralized score-tests for decision fusion with sensing model uncertainty
No full textThis chapter investigates distributed detection of a phenomenon of interest (POI) via decision fusion in wireless sensor networks (WSNs). The decisions are collected by a fusion center (FC), which is in charge of performing a more accurate global decision. So as to account for a realistic scenario, it is assumed that the POI presents a signature with limited spatial extent, and its exact location and emitted amplitude (or energy) are not known. More specifically, when the POI is present, the sensors observe a signal with an attenuation depending on the distance between the sensor and the (unknown) target position, embedded in Gaussian noise. The unavailability of a completely specified model defeats the applicability of the well-known (optimal) likelihood-ratio (LR) test (LRT). As a consequence, in the general case, the FC is usually in charge of solving a composite hypothesis test and the generalized LRT (GLRT) is commonly employed. Unfortunately, in these scenarios, its complexity is typically high. Accordingly, the present chapter discusses the development of generalized score tests as alternatives with reduced computational complexity. After a brief recall of the GLRT for the considered problems, fusion rules corresponding to generalized versions of well-known score tests are introduced, based on Davies’framework, since the resulting problems include nuisance parameters only under the POI-present hypothesis. The focus is on two relevant signal models, i.e., the cases of random and deterministic unknown signals, leading to one-sided and two-sided testing, respectively. Finally, a convincing (semi-theoretical) rationale for threshold-optimization is presented and analyzed
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