8,391 research outputs found
Towards Energy Neutrality in Energy Harvesting Wireless Sensor Networks: A Case for Distributed Compressive Sensing?
This paper advocates the use of the emerging distributed compressive sensing
(DCS) paradigm in order to deploy energy harvesting (EH) wireless sensor
networks (WSN) with practical network lifetime and data gathering rates that
are substantially higher than the state-of-the-art. In particular, we argue
that there are two fundamental mechanisms in an EH WSN: i) the energy diversity
associated with the EH process that entails that the harvested energy can vary
from sensor node to sensor node, and ii) the sensing diversity associated with
the DCS process that entails that the energy consumption can also vary across
the sensor nodes without compromising data recovery. We also argue that such
mechanisms offer the means to match closely the energy demand to the energy
supply in order to unlock the possibility for energy-neutral WSNs that leverage
EH capability. A number of analytic and simulation results are presented in
order to illustrate the potential of the approach.Comment: 6 pages. This work will be presented at the 2013 IEEE Global
Communications Conference (GLOBECOM), Atlanta, US, December 201
Imaging With Nature: Compressive Imaging Using a Multiply Scattering Medium
The recent theory of compressive sensing leverages upon the structure of
signals to acquire them with much fewer measurements than was previously
thought necessary, and certainly well below the traditional Nyquist-Shannon
sampling rate. However, most implementations developed to take advantage of
this framework revolve around controlling the measurements with carefully
engineered material or acquisition sequences. Instead, we use the natural
randomness of wave propagation through multiply scattering media as an optimal
and instantaneous compressive imaging mechanism. Waves reflected from an object
are detected after propagation through a well-characterized complex medium.
Each local measurement thus contains global information about the object,
yielding a purely analog compressive sensing method. We experimentally
demonstrate the effectiveness of the proposed approach for optical imaging by
using a 300-micrometer thick layer of white paint as the compressive imaging
device. Scattering media are thus promising candidates for designing efficient
and compact compressive imagers.Comment: 17 pages, 8 figure
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
TopSig: Topology Preserving Document Signatures
Performance comparisons between File Signatures and Inverted Files for text
retrieval have previously shown several significant shortcomings of file
signatures relative to inverted files. The inverted file approach underpins
most state-of-the-art search engine algorithms, such as Language and
Probabilistic models. It has been widely accepted that traditional file
signatures are inferior alternatives to inverted files. This paper describes
TopSig, a new approach to the construction of file signatures. Many advances in
semantic hashing and dimensionality reduction have been made in recent times,
but these were not so far linked to general purpose, signature file based,
search engines. This paper introduces a different signature file approach that
builds upon and extends these recent advances. We are able to demonstrate
significant improvements in the performance of signature file based indexing
and retrieval, performance that is comparable to that of state of the art
inverted file based systems, including Language models and BM25. These findings
suggest that file signatures offer a viable alternative to inverted files in
suitable settings and from the theoretical perspective it positions the file
signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201
Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior
Large-scale data collection by means of wireless sensor network and
internet-of-things technology poses various challenges in view of the
limitations in transmission, computation, and energy resources of the
associated wireless devices. Compressive data gathering based on compressed
sensing has been proven a well-suited solution to the problem. Existing designs
exploit the spatiotemporal correlations among data collected by a specific
sensing modality. However, many applications, such as environmental monitoring,
involve collecting heterogeneous data that are intrinsically correlated. In
this study, we propose to leverage the correlation from multiple heterogeneous
signals when recovering the data from compressive measurements. To this end, we
propose a novel recovery algorithm---built upon belief-propagation
principles---that leverages correlated information from multiple heterogeneous
signals. To efficiently capture the statistical dependencies among diverse
sensor data, the proposed algorithm uses the statistical model of copula
functions. Experiments with heterogeneous air-pollution sensor measurements
show that the proposed design provides significant performance improvements
against state-of-the-art compressive data gathering and recovery schemes that
use classical compressed sensing, compressed sensing with side information, and
distributed compressed sensing.Comment: accepted to IEEE Transactions on Communication
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