Journal ArticleDOI
Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
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TLDR
A binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman-Pearson (NP) framework is analyzed.Abstract:
We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman–Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considering an energy constraint on each node transmission and a limited amount of channel uses, we compute the miss error exponent of the proposed scheme using Large Deviation Theory (LDT) and show that the proposed strategy is asymptotically optimal (when the number of sensors approaches infinity) among linear orthogonal schemes. We also show that the proposed scheme obtains meaningful energy saving in the low signal-to-noise ratio regime, which is the typical scenario of WSNs. Finally, a Monte Carlo simulation of a 2-dimensional process in space validates the analytical results.read more
Citations
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Journal ArticleDOI
On Analog Gradient Descent Learning Over Multiple Access Fading Channels
Tomer Sery,Kobi Cohen +1 more
TL;DR: This work develops a novel Gradient-Based Multiple Access (GBMA) algorithm that can approach the convergence rate of the centralized gradient descent (GD) algorithm in large networks, and establishes a finite-sample bound of the error for both convex and strongly convex loss functions with Lipschitz gradient.
Journal ArticleDOI
Energy-efficient radio resource allocation in software-defined wireless sensor networks
TL;DR: This study investigates an energy-efficient resource allocation algorithm in SDWSNs, in which radio resource allocation could be handled at central controllers with powerful storage and computation capacity and the proposed CABPA performs better than the other algorithms, and it balances the power and bandwidth utilisation.
Journal ArticleDOI
Optimized Sensor Collaboration for Estimation of Temporally Correlated Parameters
TL;DR: This paper addresses the sensor collaboration problem for the estimation of uncorrelated parameters, and shows that the resulting collaboration problem can be transformed into a special nonconvex optimization problem, where a difference of convex functions carries all the non Convexity.
Proceedings ArticleDOI
A Sequential Gradient-Based Multiple Access for Distributed Learning over Fading Channels
Tomer Sery,Kobi Cohen +1 more
TL;DR: A novel Gradient-Based Multiple Access (GBMA) algorithm is developed and it is proved that it can approach the convergence rate of the centralized gradient descent (GD) algorithm in large networks under both convex and strongly convex loss functions with Lipschitz gradient.
Journal ArticleDOI
Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks
Kobi Cohen,Amir Leshem +1 more
TL;DR: A spectrum and energy efficient multiple access (SEEMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC) so that only sensors with highly informative observations transmit their data in each data collection.
References
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Journal ArticleDOI
Detection with distributed sensors
Robert R. Tenney,Nils R. Sandell +1 more
TL;DR: The extension of classical detection theory to the case of distributed sensors is discussed, based on the theory of statistical hypothesis testing, and theoretical results concerning the form of the optimal decision rule are presented.
Journal ArticleDOI
Decentralized detection in sensor networks
TL;DR: A binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center, and it is shown that having a set of identical binary sensors is asymptotically optimal, as the number of observations per sensor goes to infinity.
Journal ArticleDOI
Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network
TL;DR: A theorem of Witsenhausen is shown to imply that an optimal communication strategy is uncoded transmission, i.e., each sensors' channel input is merely a scaled version of its noisy observation.
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