Dataset Information

Transmission Optimization Metrics Setup Issues in the Field of Time Constrained Communications.

ABSTRACT: This article introduces a new approach in the field of network optimization based on Transmission Optimization Metric (TOM), which is aimed at improving traffic flow continuity and increasing the chances for traffic flow sustainability in a way that helps to minimize inter-packet gaps. The work is mainly focused on harsh transmission conditions in narrow-band networks. Finally, the presented approach has impact on better resource allocation as fewer attempts are necessary for successful completion of a transmission. A significant part of the article deals with parameterization of coefficients used by the TOM optimization method. Examples of analysis for several topologies of narrow-band wireless networks based on CSMA/CA and TDMA protocols are used to demonstrate various issues related to proper setting of parameters. The introduced TOM metric has the potential to become a standard for optimization, for example, in sensor networks that are characterized by the specific nature of data traffic.

SUBMITTER: Vondrous O

PROVIDER: S-EPMC6163355 | biostudies-other | 2018 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Transmission Optimization Metrics Setup Issues in the Field of Time Constrained Communications.

Vondrouš Ondřej O Kocur Zbyněk Z Hrad Jaromír J

Sensors (Basel, Switzerland) 20180914 9

This article introduces a new approach in the field of network optimization based on Transmission Optimization Metric (TOM), which is aimed at improving traffic flow continuity and increasing the chances for traffic flow sustainability in a way that helps to minimize inter-packet gaps. The work is mainly focused on harsh transmission conditions in narrow-band networks. Finally, the presented approach has impact on better resource allocation as fewer attempts are necessary for successful completi ...[more]

PMID: 30223514

Similar Datasets

Project description:PURPOSE:Interfraction tumor setup variations in radiotherapy are often reduced with image guidance procedures. Clinical target volume (CTV)-planning target volume (PTV) margins are then used to deal with residual errors. We have investigated characterization of setup errors in patient populations with explicit modelling of occurring interfraction time trends. METHODS:The core of a "trendline characterization" of observed setup errors in a population is a distribution of trendlines, each obtained by fitting a straight line through a patient's daily setup errors. Random errors are defined as daily deviations from the trendline. Monte Carlo simulations were performed to predict the impact of offline setup correction protocols on residual setup errors in patient populations with time trends. A novel CTV-PTV margin recipe was derived that assumes that systematic underdosing of tumor edges in multiple consecutive fractions, as caused by trend motion, should preferentially be avoided. Similar to the well-known approach by van Herk et al. for conventional error characterization (no explicit modelling of trends), only a predefined percentage of patients (generally 10%) was allowed to have nonrandom (systematic + trend) setup errors outside the margin. Additionally, a method was proposed to avoid erroneous results in Monte Carlo simulations with setup errors, related to decoupling of error sources in characterizations. The investigations were based on a database of daily measured setup errors in 835 prostate cancer patients that were treated with 39 fractions, and on Monte Carlo-generated patient populations with time trends. RESULTS:With conventional characterization of setup errors in patient populations with time trends, predicted standard deviations of residual systematic errors ( ?res ) after application of an offline correction protocol could be underestimated by more than 50%, potentially resulting in application of too small margins. With the new trendline characterization this was avoided. With the novel CTV-PTV margin recipe with an allowed 10% of patients having nonrandom errors outside the margin, the observed percentage was 10.0% ± 0.2%. When using conventional characterization of errors and the van Herk margin recipe, on average 58.0% ± 24.3% of patients had errors outside the margin, while 10% was prescribed. For populations with no time trends, the novel recipe simplifies to the generally applied M=2.5?+0.7? formula proposed by van Herk et al. CONCLUSIONS: In populations with time trends in setup errors, the use of trendline characterizations in Monte Carlo simulations for establishment of residual errors after a setup correction protocol can avoid application of erroneous margins. The novel margin recipe can be used to accurately control the percentage of patients with nonrandom errors outside the margin. In case of daily image guidance of patients with multiple targets with differential motion, the recipe can be used to establish margins for the targets that are not the primary target for the image guidance (e.g., nodal regions). Probabilistic planning might be improved by using trendline characterization for modelling of setup errors. Population analyses of interfraction setup errors need to take into account potential time trends.

Dataset Information

Transmission Optimization Metrics Setup Issues in the Field of Time Constrained Communications.

Publications

Transmission Optimization Metrics Setup Issues in the Field of Time Constrained Communications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets