2 research outputs found
Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery
To strike a balance between energy efficiency and data quality control, this
paper proposes a sensor censoring scheme for distributed sparse signal recovery
via compressive-sensing based wireless sensor networks. In the proposed
approach, each sensor node employs a sparse sensing vector with known support
for data compression, meanwhile enabling making local inference about the
unknown support of the sparse signal vector of interest. This naturally leads
to a ternary censoring protocol, whereby each sensor (i) directly transmits the
real-valued compressed data if the sensing vector support is detected to be
overlapped with the signal support, (ii) sends a one-bit hard decision if empty
support overlap is inferred, (iii) keeps silent if the measurement is judged to
be uninformative. Our design then aims at minimizing the error probability that
empty support overlap is decided but otherwise is true, subject to the
constraints on a tolerable false-alarm probability that non-empty support
overlap is decided but otherwise is true, and a target censoring rate. We
derive a closed-form formula of the optimal censoring rule; a low complexity
implementation using bi-section search is also developed. In addition, the
average communication cost is analyzed. To aid global signal reconstruction
under the proposed censoring framework, we propose a modified l_1-minimization
based algorithm, which exploits certain sparse nature of the hard decision
vector received at the fusion center. Analytic performance guarantees,
characterized in terms of the restricted isometry property, are also derived.
Computer simulations are used to illustrate the performance of the proposed
scheme.Comment: 30 pages, 9 figure
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions