17 research outputs found
Distributed Compressed Sensing for Sensor Networks with Packet Erasures
We study two approaches to distributed compressed sensing for in-network data
compression and signal reconstruction at a sink in a wireless sensor network
where sensors are placed on a straight line. Communication to the sink is
considered to be bandwidth-constrained due to the large number of devices. By
using distributed compressed sensing for compression of the data in the
network, the communication cost (bandwith usage) to the sink can be decreased
at the expense of delay induced by the local communication necessary for
compression. We investigate the relation between cost and delay given a certain
reconstruction performance requirement when using basis pursuit denoising for
reconstruction. Moreover, we analyze and compare the performance degradation
due to erased packets sent to the sink of the two approaches.Comment: Paper accepted to GLOBECOM 201
An Overview of Multi-Processor Approximate Message Passing
Approximate message passing (AMP) is an algorithmic framework for solving
linear inverse problems from noisy measurements, with exciting applications
such as reconstructing images, audio, hyper spectral images, and various other
signals, including those acquired in compressive signal acquisiton systems. The
growing prevalence of big data systems has increased interest in large-scale
problems, which may involve huge measurement matrices that are unsuitable for
conventional computing systems. To address the challenge of large-scale
processing, multiprocessor (MP) versions of AMP have been developed. We provide
an overview of two such MP-AMP variants. In row-MP-AMP, each computing node
stores a subset of the rows of the matrix and processes corresponding
measurements. In column- MP-AMP, each node stores a subset of columns, and is
solely responsible for reconstructing a portion of the signal. We will discuss
pros and cons of both approaches, summarize recent research results for each,
and explain when each one may be a viable approach. Aspects that are
highlighted include some recent results on state evolution for both MP-AMP
algorithms, and the use of data compression to reduce communication in the MP
network
Low-power distributed sparse recovery testbed on wireless sensor networks
Recently, distributed algorithms have been proposed
for the recovery of sparse signals in networked systems, e.g. wire-
less sensor networks. Such algorithms allow large networks to
operate autonomously without the need of a fusion center, and are
very appealing for smart sensing problems employing low-power
devices. They exploit local communications, where each node of
the network updates its estimates of the sensed signal also based
on the correlated information received from neighboring nodes.
In the literature, theoretical results and numerical simulations
have been presented to prove convergence of such methods to
accurate estimates. Their implementation, however, raises some
concerns in terms of power consumption due to iterative inter-
node communications, data storage, computation capabilities,
global synchronization, and faulty communications. On the other
hand, despite these potential issues, practical implementations on
real sensor networks have not been demonstrated yet. In this
paper we fill this gap and describe a successful implementation
of a class of randomized, distributed algorithms on a real
low-power wireless sensor network testbed with very scarce
computational capabilities. We consider a distributed compressed
sensing problem and we show how to cope with the issues
mentioned above. Our tests on synthetic and real signals show
that distributed compressed sensing can successfully operate in
a real-world environment
Distributed Quantization for Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discrete
bit-constrained representation. Quantization is typically carried out using
analog-to-digital converters (ADCs), operating in a serial scalar manner. In
some applications, a set of analog signals are acquired individually and
processed jointly. Such setups are referred to as distributed quantization. In
this work, we propose a distributed quantization scheme for representing a set
of sparse time sequences acquired using conventional scalar ADCs. Our approach
utilizes tools from secure group testing theory to exploit the sparse nature of
the acquired analog signals, obtaining a compact and accurate representation
while operating in a distributed fashion. We then show how our technique can be
implemented when the quantized signals are transmitted over a multi-hop
communication network providing a low-complexity network policy for routing and
signal recovery. Our numerical evaluations demonstrate that the proposed scheme
notably outperforms conventional methods based on the combination of
quantization and compressed sensing tools