17,213 research outputs found
Distributed Sparse Signal Recovery For Sensor Networks
We propose a distributed algorithm for sparse signal recovery in sensor
networks based on Iterative Hard Thresholding (IHT). Every agent has a set of
measurements of a signal x, and the objective is for the agents to recover x
from their collective measurements at a minimal communication cost and with low
computational complexity. A naive distributed implementation of IHT would
require global communication of every agent's full state in each iteration. We
find that we can dramatically reduce this communication cost by leveraging
solutions to the distributed top-K problem in the database literature.
Evaluations show that our algorithm requires up to three orders of magnitude
less total bandwidth than the best-known distributed basis pursuit method
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
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