Nonlinear diffusion adaptation with bounded transmission over distributed networks

Lee, Jongmin; Tepedelenlioğlu, Cihan; Banavar, Mahesh K.; Spanias, Andreas

doi:10.1109/icc.2015.7249394

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…BasiciterativeLMStypemehodshavebeenquitepopularinseveralsignalprocessingestimation applications (Widrow,1985;Spanias,1993;Spanias,2016).However,thereisasignificantnumberof studiesrelatedtoconsensus-baseddistributedparameterestimation.See (Tsitsiklis,1984;Tsitsiklis etal.,1986;BertsekasandTsitsiklis,1989;Yin,1991)forearlyresearch,whichinspirednumerous applications. Distributed least-mean-square algorithm is introduced to estimate a linear system parameterinvariousscenarios (LopesandSayed,2008;Sayed,2009;Mateosetal.,2011;Stankovic et al, 2014;Lee et al, 2015). In these studies, sensors observe random data at every iteration, generatedbyalinearsystemwithaparametervector.In (Karetal.,2012),theauthorsproposedthe consensusplusinnovationschemefordistributedparameterestimationwithsingle-andmixed-time scales.Theyconsidernonlinearaswellaslinearsystemmodelsandprovideconvergenceanalysis.…”

Section: Introductionmentioning

confidence: 99%

Distributed Quantiles Estimation of Sensor Network Measurements

Lee

Tepedelenlioğlu

Spanias

et al. 2020

International Journal of Smart Security Technologies

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A quantile is a value below which random draws from a distribution falls with a given probability. In a centralized setting where the cumulative distribution function (CDF) is unknown, empirical CDF can be used to estimate quantiles after data aggregation. In a distributed sensor network, it is challenging to estimate quantiles, as each sensor observes local measurement data with limited storage and transmission power, which makes it difficult to obtain the global ECDF. This paper proposes consensus-based quantile estimation for such networks, even when communication links are corrupted by independent random noise. The state-values are recursively updated with two steps: a local update based on measurement data and current state and averaging updated states with local nodes. The estimated state sequence is shown to be asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Applications on the distributed estimation of trimmed mean; computation of median, maximum, or minimum values; and identification of outliers through simulation are also provided.

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Section: Introductionmentioning

confidence: 99%

Distributed Quantiles Estimation of Sensor Network Measurements

Lee

Tepedelenlioğlu

Spanias

et al. 2020

International Journal of Smart Security Technologies

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“…See [6]- [9] for the early works, which inspired numerous applications. Distributed least-mean-square (LMS) algorithm is introduced to estimate a linear system parameter in various scenarios [10]- [14]. In these works, sensors observe random data at very iteration, generated by a linear system with a parameter vector.…”

Section: Introductionmentioning

confidence: 99%

Consensus-based Distributed Quantile Estimation in Sensor Networks

Lee,

Tepedelenlioglu,

Spanias

2018

Preprint

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A quantile is defined as a value below which random draws from a given distribution falls with a given probability. In a centralized setting where the cumulative distribution function (CDF) is unknown, the empirical CDF (ECDF) can be used to estimate such quantiles after aggregating the data. In a fully distributed sensor network, however, it is challenging to estimate quantiles. This is because each sensor node observes local measurement data with limited storage and data transmission power which make it difficult to obtain the global ECDF. This paper proposes consensus-based quantile estimation for such a distributed network. The states of the proposed algorithm are recursively updated with two-steps at each iteration: one is a local update based on the measurement data and the current state, and the other is averaging the updated states with neighboring nodes. We consider the realistic case of communication links between nodes being corrupted by independent random noise. It is shown that the estimated state sequence is asymptotically unbiased and converges toward the sample quantile in the mean-square sense. The two step-size sequences corresponding to the averaging and local update steps result in a mixed-time scale algorithm with proper decay rates in order to achieve convergence. We also provide applications to distributed estimation of trimmed mean, computation of median, maximum, or minimum values and identification of outliers through simulation.

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A brief survey of time- and frequency-domain adaptive filters

Spanias

2016

2016 7th International Conference on Information, Intelligence, Systems &Amp; Applications (IISA)

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Nonlinear diffusion adaptation with bounded transmission over distributed networks

Cited by 5 publications

References 24 publications

Distributed Quantiles Estimation of Sensor Network Measurements

Distributed Quantiles Estimation of Sensor Network Measurements

Consensus-based Distributed Quantile Estimation in Sensor Networks

A brief survey of time- and frequency-domain adaptive filters

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