FLOTT—A Fast, Low Memory T-TransformAlgorithm for Measuring String Complexity

Rebenich, Niko; Speidel, Ulrich; Neville, Stephen W.; Gulliver, T. Aaron

doi:10.1109/tc.2013.29

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…In doing so, we use three entropy estimators: Shannon entropy (S), (average) T-entropy (T) [12], [23], and the LZW compressor (L) [22].…”

Section: Empirical Evidencementioning

confidence: 99%

Classifying distributions via symbolic entropy estimation

Speidel

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Shannon observed that the normal distribution has maximal entropy among distributions with a density function and a given variance. This sparked a significant body of research in statistics, broadly concerned with goodness-of-fit estimators based on Shannon entropy for a variety of distributions and, in particular, normality testing. The present paper proposes to use compression algorithms and other parsing-based entropy estimators to match samples in sampling order to one of a set of distributions with the observed μ and, where applicable, σ, using the distributions' quantile functions to convert the samples into a string of symbols for entropy estimation. The paper demonstrates with a series of Monte-Carlo simulations that the proposed technique may be able to distinguish between a number of common distributions even if the samples themselves are not i.i.d.

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“…In doing so, we use three entropy estimators: Shannon entropy (S), (average) T-entropy (T) [12], [23], and the LZW compressor (L) [22].…”

Section: Empirical Evidencementioning

confidence: 99%

Classifying distributions via symbolic entropy estimation

Speidel

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Can observed entropy detect congestion in Ad-Hoc networks?

Liu

Speidel

2017

2017 27th International Telecommunication Networks and Applications Conference (ITNAC)

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RAODV Routing Protocol for Congestion Detection and Relief in Ad Hoc Wireless Networks

Liu

Speidel

2021

International Journal of Interdisciplinary Telecommunications and Networking

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Ad hoc wireless networks depend on mutual collaboration among nodes. Congestion in ad hoc wireless networks thus presents more of a challenge than for other network types. This article proposes RAODV (relieving AODV), a modification of the AODV routing protocol, to handle congestion via third party neighbour nodes in dense and static ad hoc networks. RAODV nodes use a T-entropy threshold-based congestion detection algorithm to determine the congestion status of their neighbours. If RAODV determines that congestion is occurring, it then tries to relieve congestion via a local repair modification algorithm that replaces the congested node by a suitable monitoring/third party neighbour node. This article also shows evidence that RAODV results in better network performance than AODV in simulations with random network topologies.

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FLOTT—A Fast, Low Memory T-TransformAlgorithm for Measuring String Complexity

Cited by 3 publications

References 43 publications

Classifying distributions via symbolic entropy estimation

Classifying distributions via symbolic entropy estimation

Can observed entropy detect congestion in Ad-Hoc networks?

RAODV Routing Protocol for Congestion Detection and Relief in Ad Hoc Wireless Networks

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