30,444 research outputs found
Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors
Most studies that consider the problem of estimating the location of a point
source in wireless sensor networks assume that the source location is estimated
by a set of spatially distributed sensors, whose locations are fixed. Motivated
by the fact that the observation quality and performance of the localization
algorithm depend on the location of the sensors, which could be randomly
distributed, this paper investigates the performance of a recently proposed
energy-based source-localization algorithm under the assumption that the
sensors are positioned according to a uniform clustering process. Practical
considerations such as the existence and size of the exclusion zones around
each sensor and the source will be studied. By introducing a novel performance
measure called the estimation outage, it will be shown how parameters related
to the network geometry such as the distance between the source and the closest
sensor to it as well as the number of sensors within a region surrounding the
source affect the localization performance.Comment: 7 Pages, 5 Figures, To appear at the 2014 IEEE International
Conference on Communications (ICC'14) Workshop on Advances in Network
Localization and Navigation (ANLN), Invited Pape
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling
Measurement samples are often taken in various monitoring applications. To
reduce the sensing cost, it is desirable to achieve better sensing quality
while using fewer samples. Compressive Sensing (CS) technique finds its role
when the signal to be sampled meets certain sparsity requirements. In this
paper we investigate the possibility and basic techniques that could further
reduce the number of samples involved in conventional CS theory by exploiting
learning-based non-uniform adaptive sampling.
Based on a typical signal sensing application, we illustrate and evaluate the
performance of two of our algorithms, Individual Chasing and Centroid Chasing,
for signals of different distribution features. Our proposed learning-based
adaptive sampling schemes complement existing efforts in CS fields and do not
depend on any specific signal reconstruction technique. Compared to
conventional sparse sampling methods, the simulation results demonstrate that
our algorithms allow less number of samples for accurate signal
reconstruction and achieve up to smaller signal reconstruction error
under the same noise condition.Comment: 9 pages, IEEE MASS 201
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