Distributed adaptive node-specific signal estimation in a wireless sensor network with noisy links

Arce, Fernando de la Hucha; Moonen, Marc; Verhelst, Marian; Bertrand, Alexander

doi:10.1016/j.sigpro.2019.07.013

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…In order to infer information from sensor networks, conventional centralized processing is often used but may require high transmission power, large communication bandwidth, and costly energy consumption in the central unit. Therefore, a distributed approach has been suggested where each node only communicates with its neighbouring nodes, exchanging the information locally, and has its own light processing unit [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Graph Filter Design for Distributed Network Processing: A Comparison between Adaptive Algorithms

Alinaghi

Weiss

Stankovic

et al. 2021

2021 Sensor Signal Processing for Defence Conference (SSPD)

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Graph filters (GFs) have attracted great interest since they can be directly implemented in a diffused way. Thus it is interesting to investigate using GFs to implement signal processing operations in a distributed manner. However, in most GF models, the input signals are assumed to be time-invariant, static, or change at a very low rate. In addition to that, the GF coefficients are usually set to be node-invariant, i.e. the same for all the nodes. Yet, in general, the input signals may evolve with time and the underlying GF may have parameters dependant on the nodes. Therefore, in this paper, we consider dynamic input signals with two sets of GF coefficients, nodevariant, i.e. vary on different nodes, and node-invariant. Then, we apply LMS and RLS algorithms for GF design, along with two others called adapt-then-combine (ATC) and combined RLS (CRLS) to estimate the GF coefficients. We study and compare the performance of the algorithms and show that in the case of node-invariant GF coefficients, CRLS gives the best performance with lowest mean-square-displacement (MSD), whereas, for nodevariant case, RLS represents the best results. The effect of bias in the input signal has also been examined.

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Section: Introductionmentioning

confidence: 99%

Graph Filter Design for Distributed Network Processing: A Comparison between Adaptive Algorithms

Alinaghi

Weiss

Stankovic

et al. 2021

2021 Sensor Signal Processing for Defence Conference (SSPD)

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Distributed parameterized topology-independent noise reduction in acoustic sensor networks

Chang,

Chen,

Yin

2023

Applied Acoustics

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The electro-osmotic flow and heat transfer of generalized Maxwell fluids with distributed-order time-fractional characteristics in microtubules under an alternating field

Feng

et al. 2021

Physics of Fluids

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The electro-osmotic flow and heat transfer of a Maxwell fluid with distributed-order time-fractional characteristics in a microchannel under an alternating field is investigated, while considering viscous dissipation and Joule heating. The unsteady momentum and energy equations are computed numerically directly using the finite volume method. The accuracy of the numerical method is validated by comparison the constructed velocity distribution with the velocity distribution in previous references. With the time going on, oscillation of alternating current with a constant amplitude will afford periodic velocity distribution. The temperature will periodically increase. Furthermore, the velocity and temperature distributions characteristics of a Newtonian fluid, fractional Maxwell fluid, and generalized Maxwell fluid with time distribution are compared. Finally, the effects of different physical parameters K, S, Br, Ha, λ, Ω, ψ1, ψ2, Pr, and δ on the velocity and heat distributions under an alternating field are discussed.

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Distributed adaptive node-specific signal estimation in a wireless sensor network with noisy links

Cited by 4 publications

References 32 publications

Graph Filter Design for Distributed Network Processing: A Comparison between Adaptive Algorithms

Graph Filter Design for Distributed Network Processing: A Comparison between Adaptive Algorithms

Distributed parameterized topology-independent noise reduction in acoustic sensor networks

The electro-osmotic flow and heat transfer of generalized Maxwell fluids with distributed-order time-fractional characteristics in microtubules under an alternating field

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