7,648 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
Rician MIMO Channel- and Jamming-Aware Decision Fusion
In this manuscript we study channel-aware decision fusion (DF) in a wireless
sensor network (WSN) where: (i) the sensors transmit their decisions
simultaneously for spectral efficiency purposes and the DF center (DFC) is
equipped with multiple antennas; (ii) each sensor-DFC channel is described via
a Rician model. As opposed to the existing literature, in order to account for
stringent energy constraints in the WSN, only statistical channel information
is assumed for the non-line-of sight (scattered) fading terms. For such a
scenario, sub-optimal fusion rules are developed in order to deal with the
exponential complexity of the likelihood ratio test (LRT) and impractical
(complete) system knowledge. Furthermore, the considered model is extended to
the case of (partially unknown) jamming-originated interference. Then the
obtained fusion rules are modified with the use of composite hypothesis testing
framework and generalized LRT. Coincidence and statistical equivalence among
them are also investigated under some relevant simplified scenarios. Numerical
results compare the proposed rules and highlight their jammingsuppression
capability.Comment: Accepted in IEEE Transactions on Signal Processing 201
Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering
The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs
In this letter, we propose space-time spreading (STS) of local sensor
decisions before reporting them over a wireless multiple access channel (MAC),
in order to achieve flexible balance between diversity and multiplexing gain as
well as eliminate any chance of intrinsic interference inherent in MAC
scenarios. Spreading of the sensor decisions using dispersion vectors exploits
the benefits of multi-slot decision to improve low-complexity diversity gain
and opportunistic throughput. On the other hand, at the receive side of the
reporting channel, we formulate and compare optimum and sub-optimum fusion
rules for arriving at a reliable conclusion.Simulation results demonstrate gain
in performance with STS aided transmission from a minimum of 3 times to a
maximum of 6 times over performance without STS.Comment: 5 pages, 5 figure
Channel Impulse Response-based Distributed Physical Layer Authentication
In this preliminary work, we study the problem of {\it distributed}
authentication in wireless networks. Specifically, we consider a system where
multiple Bob (sensor) nodes listen to a channel and report their {\it
correlated} measurements to a Fusion Center (FC) which makes the ultimate
authentication decision. For the feature-based authentication at the FC,
channel impulse response has been utilized as the device fingerprint.
Additionally, the {\it correlated} measurements by the Bob nodes allow us to
invoke Compressed sensing to significantly reduce the reporting overhead to the
FC. Numerical results show that: i) the detection performance of the FC is
superior to that of a single Bob-node, ii) compressed sensing leads to at least
overhead reduction on the reporting channel at the expense of a small
( dB) SNR margin to achieve the same detection performance.Comment: 6 pages, 5 figures, accepted for presentation at IEEE VTC 2017 Sprin
Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing
Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs
Massive MIMO for Wireless Sensing with a Coherent Multiple Access Channel
We consider the detection and estimation of a zero-mean Gaussian signal in a
wireless sensor network with a coherent multiple access channel, when the
fusion center (FC) is configured with a large number of antennas and the
wireless channels between the sensor nodes and FC experience Rayleigh fading.
For the detection problem, we study the Neyman-Pearson (NP) Detector and Energy
Detector (ED), and find optimal values for the sensor transmission gains. For
the NP detector which requires channel state information (CSI), we show that
detection performance remains asymptotically constant with the number of FC
antennas if the sensor transmit power decreases proportionally with the
increase in the number of antennas. Performance bounds show that the benefit of
multiple antennas at the FC disappears as the transmit power grows. The results
of the NP detector are also generalized to the linear minimum mean squared
error estimator. For the ED which does not require CSI, we derive optimal gains
that maximize the deflection coefficient of the detector, and we show that a
constant deflection can be asymptotically achieved if the sensor transmit power
scales as the inverse square root of the number of FC antennas. Unlike the NP
detector, for high sensor power the multi-antenna ED is observed to empirically
have significantly better performance than the single-antenna implementation. A
number of simulation results are included to validate the analysis.Comment: 32 pages, 6 figures, accepted by IEEE Transactions on Signal
Processing, Feb. 201
Deterministic Bayesian Information Fusion and the Analysis of its Performance
This paper develops a mathematical and computational framework for analyzing
the expected performance of Bayesian data fusion, or joint statistical
inference, within a sensor network. We use variational techniques to obtain the
posterior expectation as the optimal fusion rule under a deterministic
constraint and a quadratic cost, and study the smoothness and other properties
of its classification performance. For a certain class of fusion problems, we
prove that this fusion rule is also optimal in a much wider sense and satisfies
strong asymptotic convergence results. We show how these results apply to a
variety of examples with Gaussian, exponential and other statistics, and
discuss computational methods for determining the fusion system's performance
in more general, large-scale problems. These results are motivated by studying
the performance of fusing multi-modal radar and acoustic sensors for detecting
explosive substances, but have broad applicability to other Bayesian decision
problems
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