6,021 research outputs found
Decision Fusion with Unknown Sensor Detection Probability
In this correspondence we study the problem of channel-aware decision fusion
when the sensor detection probability is not known at the decision fusion
center. Several alternatives proposed in the literature are compared and new
fusion rules (namely 'ideal sensors' and 'locally-optimum detection') are
proposed, showing attractive performance and linear complexity. Simulations are
provided to compare the performance of the aforementioned rules.Comment: To appear in IEEE Signal Processing Letter
Rician MIMO Channel- and Jamming-Aware Decision Fusion
In this manuscript we study channel-aware decision fusion (DF) in a wireless
sensor network (WSN) where: (i) the sensors transmit their decisions
simultaneously for spectral efficiency purposes and the DF center (DFC) is
equipped with multiple antennas; (ii) each sensor-DFC channel is described via
a Rician model. As opposed to the existing literature, in order to account for
stringent energy constraints in the WSN, only statistical channel information
is assumed for the non-line-of sight (scattered) fading terms. For such a
scenario, sub-optimal fusion rules are developed in order to deal with the
exponential complexity of the likelihood ratio test (LRT) and impractical
(complete) system knowledge. Furthermore, the considered model is extended to
the case of (partially unknown) jamming-originated interference. Then the
obtained fusion rules are modified with the use of composite hypothesis testing
framework and generalized LRT. Coincidence and statistical equivalence among
them are also investigated under some relevant simplified scenarios. Numerical
results compare the proposed rules and highlight their jammingsuppression
capability.Comment: Accepted in IEEE Transactions on Signal Processing 201
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs
In this letter, we propose space-time spreading (STS) of local sensor
decisions before reporting them over a wireless multiple access channel (MAC),
in order to achieve flexible balance between diversity and multiplexing gain as
well as eliminate any chance of intrinsic interference inherent in MAC
scenarios. Spreading of the sensor decisions using dispersion vectors exploits
the benefits of multi-slot decision to improve low-complexity diversity gain
and opportunistic throughput. On the other hand, at the receive side of the
reporting channel, we formulate and compare optimum and sub-optimum fusion
rules for arriving at a reliable conclusion.Simulation results demonstrate gain
in performance with STS aided transmission from a minimum of 3 times to a
maximum of 6 times over performance without STS.Comment: 5 pages, 5 figure
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Fusing Censored Dependent Data for Distributed Detection
In this paper, we consider a distributed detection problem for a censoring
sensor network where each sensor's communication rate is significantly reduced
by transmitting only "informative" observations to the Fusion Center (FC), and
censoring those deemed "uninformative". While the independence of data from
censoring sensors is often assumed in previous research, we explore spatial
dependence among observations. Our focus is on designing the fusion rule under
the Neyman-Pearson (NP) framework that takes into account the spatial
dependence among observations. Two transmission scenarios are considered, one
where uncensored observations are transmitted directly to the FC and second
where they are first quantized and then transmitted to further improve
transmission efficiency. Copula-based Generalized Likelihood Ratio Test (GLRT)
for censored data is proposed with both continuous and discrete messages
received at the FC corresponding to different transmission strategies. We
address the computational issues of the copula-based GLRTs involving
multidimensional integrals by presenting more efficient fusion rules, based on
the key idea of injecting controlled noise at the FC before fusion. Although,
the signal-to-noise ratio (SNR) is reduced by introducing controlled noise at
the receiver, simulation results demonstrate that the resulting noise-aided
fusion approach based on adding artificial noise performs very closely to the
exact copula-based GLRTs. Copula-based GLRTs and their noise-aided counterparts
by exploiting the spatial dependence greatly improve detection performance
compared with the fusion rule under independence assumption
- …