7,798 research outputs found
Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks
It has been shown that cooperative localization is capable of improving both
the positioning accuracy and coverage in scenarios where the global positioning
system (GPS) has a poor performance. However, due to its potentially excessive
computational complexity, at the time of writing the application of cooperative
localization remains limited in practice. In this paper, we address the
efficient cooperative positioning problem in wireless sensor networks. A
space-time hierarchical-graph based scheme exhibiting fast convergence is
proposed for localizing the agent nodes. In contrast to conventional methods,
agent nodes are divided into different layers with the aid of the space-time
hierarchical-model and their positions are estimated gradually. In particular,
an information propagation rule is conceived upon considering the quality of
positional information. According to the rule, the information always
propagates from the upper layers to a certain lower layer and the message
passing process is further optimized at each layer. Hence, the potential error
propagation can be mitigated. Additionally, both position estimation and
position broadcasting are carried out by the sensor nodes. Furthermore, a
sensor activation mechanism is conceived, which is capable of significantly
reducing both the energy consumption and the network traffic overhead incurred
by the localization process. The analytical and numerical results provided
demonstrate the superiority of our space-time hierarchical-graph based
cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE
Transactions on Signal Processing, Sept. 201
Efficient Localization of Discontinuities in Complex Computational Simulations
Surrogate models for computational simulations are input-output
approximations that allow computationally intensive analyses, such as
uncertainty propagation and inference, to be performed efficiently. When a
simulation output does not depend smoothly on its inputs, the error and
convergence rate of many approximation methods deteriorate substantially. This
paper details a method for efficiently localizing discontinuities in the input
parameter domain, so that the model output can be approximated as a piecewise
smooth function. The approach comprises an initialization phase, which uses
polynomial annihilation to assign function values to different regions and thus
seed an automated labeling procedure, followed by a refinement phase that
adaptively updates a kernel support vector machine representation of the
separating surface via active learning. The overall approach avoids structured
grids and exploits any available simplicity in the geometry of the separating
surface, thus reducing the number of model evaluations required to localize the
discontinuity. The method is illustrated on examples of up to eleven
dimensions, including algebraic models and ODE/PDE systems, and demonstrates
improved scaling and efficiency over other discontinuity localization
approaches
Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages
This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times
Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections
Wireless sensor networks (WSNs) have attracted considerable attention in
recent years and motivate a host of new challenges for distributed signal
processing. The problem of distributed or decentralized estimation has often
been considered in the context of parametric models. However, the success of
parametric methods is limited by the appropriateness of the strong statistical
assumptions made by the models. In this paper, a more flexible nonparametric
model for distributed regression is considered that is applicable in a variety
of WSN applications including field estimation. Here, starting with the
standard regularized kernel least-squares estimator, a message-passing
algorithm for distributed estimation in WSNs is derived. The algorithm can be
viewed as an instantiation of the successive orthogonal projection (SOP)
algorithm. Various practical aspects of the algorithm are discussed and several
numerical simulations validate the potential of the approach.Comment: To appear in the Proceedings of the SPIE Conference on Advanced
Signal Processing Algorithms, Architectures and Implementations XV, San
Diego, CA, July 31 - August 4, 200
Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization
We consider the online and nonparametric detection of abrupt and persistent
anomalies, such as a change in the regular system dynamics at a time instance
due to an anomalous event (e.g., a failure, a malicious activity). Combining
the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method
with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we
propose a computationally efficient online anomaly detection method that is
applicable to high-dimensional datasets, and at the same time achieve a
near-optimum average detection delay performance for a given false alarm
constraint. We provide new insights to both GEM and CUSUM, including new
asymptotic analysis for GEM, which enables soft decisions for outlier
detection, and a novel interpretation of CUSUM in terms of the discrepancy
theory, which helps us generalize it to the nonparametric GEM statistic. We
numerically show, using both simulated and real datasets, that the proposed
nonparametric algorithm attains a close performance to the clairvoyant
parametric CUSUM test.Comment: to appear in IEEE International Symposium on Information Theory
(ISIT) 201
Change point localization in dependent dynamic nonparametric random dot product graphs
In this paper, we study the change point localization problem in a sequence
of dependent nonparametric random dot product graphs. To be specific, assume
that at every time point, a network is generated from a nonparametric random
dot product graph model (see e.g. Athreya et al., 2017), where the latent
positions are generated from unknown underlying distributions. The underlying
distributions are piecewise constant in time and change at unknown locations,
called change points. Most importantly, we allow for dependence among networks
generated between two consecutive change points. This setting incorporates
edge-dependence within networks and temporal dependence between networks, which
is the most flexible setting in the published literature.
To accomplish the task of consistently localizing change points, we propose a
novel change point detection algorithm, consisting of two steps. First, we
estimate the latent positions of the random dot product model, our theoretical
result being a refined version of the state-of-the-art results, allowing the
dimension of the latent positions to grow unbounded. Subsequently, we construct
a nonparametric version of the CUSUM statistic (Page, 1954, Padilla et al.,
2019) that allows for temporal dependence. Consistent localization is proved
theoretically and supported by extensive numerical experiments, which
illustrate state-of-the-art performance. We also provide in depth discussion of
possible extensions to give more understanding and insights
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