1,425 research outputs found
On Distributed and Acoustic Sensing for Situational Awareness
Recent advances in electronics enable the development of small-sized, low-cost, low-power, multi-functional sensor nodes that possess local processing capability as well as to work collaboratively through communications. They are able to sense, collect, and process data from the surrounding environment locally. Collaboration among the nodes are enabled due to their integrated communication capability. Such a system, generally referred to as sensor networks are widely used in various of areas, such as environmental monitoring, asset tracking, indoor navigation, etc.
This thesis consists of two separate applications of such mobile sensors. In this first part, we study decentralized inference problems with dependent observations in wireless sensor networks. Two separate problems are addressed in this part: one pertaining to collaborative spectrum sensing while the other on distributed parameter estimation with correlated additive Gaussian noise. In the second part, we employ a single acoustic sensor with co-located microphone and loudspeaker to reconstruct a 2-D convex polygonal room shape.
For spectrum sensing, we study the optimality of energy detection that has been widely used in the literature. This thesis studies the potential optimality (or sub-optimality) of the energy detector in spectrum sensing. With a single sensing node, we show that the energy detector is provably optimal for most cases and for the case when it is not theoretically optimal, its performance is nearly indistinguishable from the true optimal detector. For cooperative spectrum sensing where multiple nodes are employed, we use a recently proposed framework for distributed detection with dependent observations to establish the optimality of energy detector for several cooperative spectrum sensing systems and point out difficulties for the remaining cases.
The second problem in decentralized inference studied in this thesis is to investigate the impact of noise correlation on decentralized estimation performance. For a tandem network with correlated additive Gaussian noises, we establish that threshold quantizer on local observations is optimal in the sense of maximizing Fisher information at the fusion center; this is true despite the fact that subsequent estimators may differ at the fusion center, depending on the statistical distribution of the parameter to be estimated. In addition, it is always beneficial to have the better sensor (i.e. the one with higher signal-to-noise ratio) serve as the fusion center in a tandem network for all correlation regimes. Finally, we identify different correlation regimes in terms of their impact on the estimation performance. These include the well known case where negatively correlated noises benefit estimation performance as it facilitates noise cancellation, as well as two distinct regimes with positively correlated noises compared with that of the independent case.
In the second part of this thesis, a practical problem of room shape reconstruction using first-order acoustic echoes is explored. Specifically, a single mobile node, with co-located loudspeaker, microphone and internal motion sensors, is deployed and times of arrival of the first-order echoes are measured and used to recover room shape. Two separate cases are studied: the first assumes no knowledge about the sensor trajectory, and the second one assumes partial knowledge on the sensor movement. For either case, the uniqueness of the mapping between the first-order echoes and the room geometry is discussed. Without any trajectory information, we show that first-order echoes are sufficient to recover 2-D room shapes for all convex polygons with the exception of parallelograms. Algorithmic procedure is developed to eliminate the higher-order echoes among the collected echoes in order to retrieve the room geometry. In the second case, the mapping is proved for any convex polygonal shapes when partial trajectory information from internal motion sensors is available.. A practical algorithm for room reconstruction in the presence of noise and higher order echoes is proposed
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
A Bayesian Framework for Collaborative Multi-Source Signal Detection
This paper introduces a Bayesian framework to detect multiple signals
embedded in noisy observations from a sensor array. For various states of
knowledge on the communication channel and the noise at the receiving sensors,
a marginalization procedure based on recent tools of finite random matrix
theory, in conjunction with the maximum entropy principle, is used to compute
the hypothesis selection criterion. Quite remarkably, explicit expressions for
the Bayesian detector are derived which enable to decide on the presence of
signal sources in a noisy wireless environment. The proposed Bayesian detector
is shown to outperform the classical power detector when the noise power is
known and provides very good performance for limited knowledge on the noise
power. Simulations corroborate the theoretical results and quantify the gain
achieved using the proposed Bayesian framework.Comment: 15 pages, 9 pictures, Submitted to IEEE Trans. on Signal Processin
Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling
Measurement samples are often taken in various monitoring applications. To
reduce the sensing cost, it is desirable to achieve better sensing quality
while using fewer samples. Compressive Sensing (CS) technique finds its role
when the signal to be sampled meets certain sparsity requirements. In this
paper we investigate the possibility and basic techniques that could further
reduce the number of samples involved in conventional CS theory by exploiting
learning-based non-uniform adaptive sampling.
Based on a typical signal sensing application, we illustrate and evaluate the
performance of two of our algorithms, Individual Chasing and Centroid Chasing,
for signals of different distribution features. Our proposed learning-based
adaptive sampling schemes complement existing efforts in CS fields and do not
depend on any specific signal reconstruction technique. Compared to
conventional sparse sampling methods, the simulation results demonstrate that
our algorithms allow less number of samples for accurate signal
reconstruction and achieve up to smaller signal reconstruction error
under the same noise condition.Comment: 9 pages, IEEE MASS 201
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
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