On the implementation of distributed asynchronous non-linear kernel methods over wireless sensor networks

Garrido-Castellano, Juan A.; Murillo-Fuentes, Juan José

doi:10.1186/s13638-015-0382-6

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Spectrum monitoring is not the only application where RSS measurements play a central role. Others include indoor localization [5], [6], tracking [7], distance estimation [8] and distributed asynchronous regression [9].…”

Section: Introductionmentioning

confidence: 99%

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Santos

Murillo-Fuentes

Djurić

2019

IEEE Trans. Signal Process.

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In this paper, we address the estimation of a timevarying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sensors. We employ a radio propagation model where the path loss exponent and the transmitted power are unknown with Gaussian priors whose hyper-parameters are estimated by applying the empirical Bayes method. We consider the locations of the sensors to be imperfectly known, which entails that they represent another source of error in the model. The propagation model includes shadowing which is considered to be a zero-mean Gaussian process where the correlation of attenuation between two spatial points is quantified by an exponential function of the distance between the points. The location of the transmitter is also unknown and is estimated from the data. We propose to estimate time-varying RSS fields by a recursive Bayesian method and crowdsourcing. The method is based on Gaussian processes (GP), and it produces the joint distribution of the spatial field. Further, it summarizes all the acquired information by keeping the size of the needed memory bounded. We also present the Bayesian Cramér-Rao bound (BCRB) of the estimated parameters. Finally, we illustrate the performance of our method with experimental results on synthetic and real data sets.

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Section: Introductionmentioning

confidence: 99%

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Santos

Murillo-Fuentes

Djurić

2019

IEEE Trans. Signal Process.

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“…Therefore, centralized learning methods have very high communication costs and energy consumption and are liable to cause congestion and failure on nodes near the central fusion center. This will lead to an energy imbalance among the nodes and greatly reduce the lifetime of the WSN [ 8 ]. To avoid and solve these problems, distributed learning methods for classifiers or regression machines, which depend on in-network processing through collaboration between single-hop neighboring nodes, have attracted more and more interest from researchers [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks

Hou

et al. 2016

Sensors

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Abstract:In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates 1 norm regularization ( 1 -regularized) is investigated, and a novel distributed learning algorithm for the 1 -regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost.

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“…Implementation aspects of distributed and asynchronous non-linear kernel methods were addressed in [1]. For their evaluations, they employed MICAz motes running TinyOS 2.1.1, and the practical aspects considered were related to matrix and floating computations, acknowledgment mechanisms, and data loss.…”

Section: Special Issue Motivation and Overviewmentioning

confidence: 99%

Experimental evaluation in wireless communications

García-Naya

Heath

Kaltenberger

et al. 2017

J Wireless Com Network

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This editorial sums up relevant topics on the assessment of wireless communication systems covered by the especial issue entitled "Experimental Evaluation in Wireless Communications." The topics include practical aspects on the implementation of distributed asynchronous non-linear kernel methods over wireless sensor networks; localization methods based on the exploitation of radio-frequency identification (RFID) wireless sensors and cellular networks or on sparsity approximations; channel sounding and assessment of broadband orthogonal frequency-division multiplexing (OFDM)-based wireless systems in high-speed vehicular communications; coexistence analysis of femtocell-based and outdoor-to-indoor systems; techniques for peak-to-average power ratio (PAPR) reduction; new solutions for baseband and radio frequency (RF) hardware impairments in full-duplex wireless systems; and, finally, suitability of interference alignment for broadband indoor wireless communications. Special issue motivation and overviewThe benefits of experimentation are not always realized in research on signal processing for wireless communication systems. In wireless systems, many abstractions are made to facilitate analysis. With unknown channels and realistic hardware, experimentation is a crucial means towards understanding the real-world viability of new ideas. Furthermore, during such experimental work, often new research questions arise as it may turn out that not all aspects have been sufficiently modeled.This special issue brings together research results obtained through experimental evaluation and measurements. The applications embrace cellular transmissions such as Long-Term Evolution Advanced (LTE-A), interference alignment techniques, local area networks, vehicular networks, wireless sensor networks, localization, and other wireless transmission systems.After a rigorous peer review process, the following 12 articles were selected for publication out of 42 submissions. All papers received at least three high quality reviews. We appreciate the careful and methodical *Correspondence: jagarcia@udc.es 1 Department of Electronics and Systems, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain Full list of author information is available at the end of the article reviews. We would like to thank all the authors for submitting their work to our special issue and also all the reviewers for their efforts.The published articles were grouped according to the topics addressed, and they are briefly introduced in the following.Wireless sensor networks received a great attention in this special issue. Implementation aspects of distributed and asynchronous non-linear kernel methods were addressed in [1]. For their evaluations, they employed MICAz motes running TinyOS 2.1.1, and the practical aspects considered were related to matrix and floating computations, acknowledgment mechanisms, and data loss. On the other hand, wireless sensor networks are typically employed in localization applications. In [2], a target sensor was ...

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On the implementation of distributed asynchronous non-linear kernel methods over wireless sensor networks

Cited by 7 publications

References 14 publications

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks

Experimental evaluation in wireless communications

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