4,230 research outputs found
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Distributed detection, localization, and estimation in time-critical wireless sensor networks
In this thesis the problem of distributed detection, localization, and estimation
(DDLE) of a stationary target in a fusion center (FC) based wireless sensor network
(WSN) is considered. The communication process is subject to time-critical
operation, restricted power and bandwidth (BW) resources operating over a shared
communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm
is proposed to solve the DDLE problem consisting of two dependent stages:
distributed detection and distributed estimation. The WSN performs distributed
detection first and based on the global detection decision the distributed estimation
stage is performed. The communication between the SNs and the FC occurs over a
shared channel via a slotted Aloha MAC protocol to conserve BW.
In distributed detection, hard decision fusion is adopted, using the counting
rule (CR), and sensor censoring in order to save power and BW. The effect of
Rayleigh fading on distributed detection is also considered and accounted for by
using distributed diversity combining techniques where the diversity combining is
among the sensor nodes (SNs) in lieu of having the processing done at the FC.
Two distributed techniques are proposed: the distributed maximum ratio combining
(dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show
superior detection performance when compared to conventional diversity combining
procedures that take place at the FC.
In distributed estimation, the segmented distributed localization and estimation
(SDLE) framework is proposed. The SDLE enables efficient power and BW
processing. The SOLE hinges on the idea of introducing intermediate parameters
that are estimated locally by the SNs and transmitted to the FC instead of the
actual measurements. This concept decouples the main problem into a simpler set
of local estimation problems solved at the SNs and a global estimation problem
solved at the FC. Two algorithms are proposed for solving the local problem: a
nonlinear least squares (NLS) algorithm using the variable projection (VP) method
and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve
the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust
hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through
local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS),
NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm
combination delivers the best localization and estimation performance. In fact , the
target can be localized with less than one meter error.
The SNs send their local estimates to the FC over a shared channel using the
slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel.
However, Aloha is known for its relatively high medium access or contention delay
given the medium access probability is poorly chosen. This fact significantly
hinders the time-critical operation of the system. Hence, multi-packet reception
(MPR) is used with slotted Aloha protocol, in which several channels are used for
contention. The contention delay is analyzed for slotted Aloha with and without
MPR. More specifically, the mean and variance have been analytically computed
and the contention delay distribution is approximated. Having theoretical expressions
for the contention delay statistics enables optimizing both the medium access
probability and the number of MPR channels in order to strike a trade-off between
delay performance and complexity
Distributed Detection over Fading MACs with Multiple Antennas at the Fusion Center
A distributed detection problem over fading Gaussian multiple-access channels
is considered. Sensors observe a phenomenon and transmit their observations to
a fusion center using the amplify and forward scheme. The fusion center has
multiple antennas with different channel models considered between the sensors
and the fusion center, and different cases of channel state information are
assumed at the sensors. The performance is evaluated in terms of the error
exponent for each of these cases, where the effect of multiple antennas at the
fusion center is studied. It is shown that for zero-mean channels between the
sensors and the fusion center when there is no channel information at the
sensors, arbitrarily large gains in the error exponent can be obtained with
sufficient increase in the number of antennas at the fusion center. In stark
contrast, when there is channel information at the sensors, the gain in error
exponent due to having multiple antennas at the fusion center is shown to be no
more than a factor of (8/pi) for Rayleigh fading channels between the sensors
and the fusion center, independent of the number of antennas at the fusion
center, or correlation among noise samples across sensors. Scaling laws for
such gains are also provided when both sensors and antennas are increased
simultaneously. Simple practical schemes and a numerical method using
semidefinite relaxation techniques are presented that utilize the limited
possible gains available. Simulations are used to establish the accuracy of the
results.Comment: 21 pages, 9 figures, submitted to the IEEE Transactions on Signal
Processin
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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