2,179 research outputs found
Distributed Learning in Wireless Sensor Networks
The problem of distributed or decentralized detection and estimation in
applications such as wireless sensor networks has often been considered in the
framework of parametric models, in which strong assumptions are made about a
statistical description of nature. In certain applications, such assumptions
are warranted and systems designed from these models show promise. However, in
other scenarios, prior knowledge is at best vague and translating such
knowledge into a statistical model is undesirable. Applications such as these
pave the way for a nonparametric study of distributed detection and estimation.
In this paper, we review recent work of the authors in which some elementary
models for distributed learning are considered. These models are in the spirit
of classical work in nonparametric statistics and are applicable to wireless
sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference
on Communication, Control and Computing, University of Illinois, 200
Consistency in Models for Distributed Learning under Communication Constraints
Motivated by sensor networks and other distributed settings, several models
for distributed learning are presented. The models differ from classical works
in statistical pattern recognition by allocating observations of an independent
and identically distributed (i.i.d.) sampling process amongst members of a
network of simple learning agents. The agents are limited in their ability to
communicate to a central fusion center and thus, the amount of information
available for use in classification or regression is constrained. For several
basic communication models in both the binary classification and regression
frameworks, we question the existence of agent decision rules and fusion rules
that result in a universally consistent ensemble. The answers to this question
present new issues to consider with regard to universal consistency. Insofar as
these models present a useful picture of distributed scenarios, this paper
addresses the issue of whether or not the guarantees provided by Stone's
Theorem in centralized environments hold in distributed settings.Comment: To appear in the IEEE Transactions on Information Theor
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Networks
Cognitive radio networks (CRN) sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to force a wrong decision. Providing security to the sensing process is also complex, as it usually involves introducing limitations to the CRN applications. The most common limitation is the need for a static trusted node that is able to authenticate and merge the reports of all CRN nodes. This paper overcomes this limitation by presenting a protocol that is suitable for fully distributed scenarios, where there is no static trusted node
Distributed Binary Detection over Fading Channels: Cooperative and Parallel Architectures
This paper considers the problem of binary distributed detection of a known
signal in correlated Gaussian sensing noise in a wireless sensor network, where
the sensors are restricted to use likelihood ratio test (LRT), and communicate
with the fusion center (FC) over bandwidth-constrained channels that are
subject to fading and noise. To mitigate the deteriorating effect of fading
encountered in the conventional parallel fusion architecture, in which the
sensors directly communicate with the FC, we propose new fusion architectures
that enhance the detection performance, via harvesting cooperative gain
(so-called decision diversity gain). In particular, we propose: (i) cooperative
fusion architecture with Alamouti's space-time coding (STC) scheme at sensors,
(ii) cooperative fusion architecture with signal fusion at sensors, and (iii)
parallel fusion architecture with local threshold changing at sensors. For
these schemes, we derive the LRT and majority fusion rules at the FC, and
provide upper bounds on the average error probabilities for homogeneous
sensors, subject to uncorrelated Gaussian sensing noise, in terms of
signal-to-noise ratio (SNR) of communication and sensing channels. Our
simulation results indicate that, when the FC employs the LRT rule, unless for
low communication SNR and moderate/high sensing SNR, performance improvement is
feasible with the new fusion architectures. When the FC utilizes the majority
rule, such improvement is possible, unless for high sensing SNR
Fusion of threshold rules for target detection in wireless sensor networks
We consider a network of sensors distributed in a target area providing environmental measurements that are subject to normally distributed, independent additive noise. Each sensor node applies a threshold rule to the measurements to decide the presence of a target; the distance to the target together with the threshold value determines its hit and false alarm probabilities or rates using a signal attenuation model. We propose a centralized threshold-OR fusion rule for combining the individual sensor node decisions. Under the statistical independence of sensor measurements, we derive fusion threshold bounds using Chebyshevâs inequality based on individual hit and false alarm probabilities but without requiring a priori knowledge of the underlying probability distributions. We derive conditions to ensure that the fused method achieves a higher hit rate and lower false alarm rate compared to the weighted averages of individual sensor parameters. The simulations using Monte Carlo method illustrate significant detection performance improvements of the proposed fusion approach
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Distributed anonymous function computation in information fusion and multiagent systems
We propose a model for deterministic distributed function computation by a
network of identical and anonymous nodes, with bounded computation and storage
capabilities that do not scale with the network size. Our goal is to
characterize the class of functions that can be computed within this model. In
our main result, we exhibit a class of non-computable functions, and prove that
every function outside this class can at least be approximated. The problem of
computing averages in a distributed manner plays a central role in our
development
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