The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'agrégation de données, qui consiste à résumer une grande quantité de données, est une méthode efficace pour économiser des ressources de communication limitées, telles que les radiofréquences et l'énergie des nœuds de capteurs. L'agrégation de paquets dans un réseau local sans fil et l'agrégation de données détectées dans des réseaux de capteurs sans fil en sont des exemples typiques. Nous proposons et analysons deux modèles de files d'attente de schémas d'agrégation de données statistiques fondamentales : intervalle constant et nombre d'agrégation constant. Nous représentons chaque schéma d'agrégation par un modèle de réseau de file d'attente tandem avec une porte au niveau du processus d'agrégation et une file d'attente de serveur unique au niveau du processus de transmission. Nous dérivons analytiquement la distribution stationnaire et la transformée de Laplace-Stieltjes du temps système pour chaque processus d'agrégation et de transmission et du temps système total. Nous évaluons ensuite numériquement les caractéristiques du temps moyen stationnaire du système et clarifions que chaque modèle a un paramètre d'agrégation optimal (c'est-à-dire un intervalle d'agrégation optimal ou un nombre d'agrégation optimal), qui minimise le temps total moyen du système. De plus, nous dérivons le paramètre d'agrégation optimal explicite pour un modèle de transmission D/M/1 avec chaque schéma d'agrégation et précisons qu'il fournit une approximation précise de celui de chaque modèle d'agrégation. L'intervalle d'agrégation optimal a été déterminé uniquement par le taux de transmission, tandis que le nombre d'agrégation optimal a été déterminé uniquement par les taux d'arrivée et de transmission avec des constantes proportionnelles explicitement dérivées. Ces résultats peuvent fournir une base théorique et une ligne directrice pour la conception de dispositifs d'agrégation, tels que des passerelles IoT.
Hideaki YOSHINO
Nippon Institute of Technology
Kenko OTA
Nippon Institute of Technology
Takefumi HIRAGURI
Nippon Institute of Technology
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Hideaki YOSHINO, Kenko OTA, Takefumi HIRAGURI, "Queueing Delay Analysis and Optimization of Statistical Data Aggregation and Transmission Systems" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 10, pp. 2186-2195, October 2018, doi: 10.1587/transcom.2018EBP3010.
Abstract: Data aggregation, which is the process of summarizing a large amount of data, is an effective method for saving limited communication resources, such as radio frequency and sensor-node energy. Packet aggregation in wireless LAN and sensed-data aggregation in wireless sensor networks are typical examples. We propose and analyze two queueing models of fundamental statistical data aggregation schemes: constant interval and constant aggregation number. We represent each aggregation scheme by a tandem queueing network model with a gate at the aggregation process and a single server queue at a transmission process. We analytically derive the stationary distribution and Laplace-Stieltjes transform of the system time for each aggregation and transmission process and of the total system time. We then numerically evaluate the stationary mean system time characteristics and clarify that each model has an optimal aggregation parameter (i.e., an optimal aggregation interval or optimal aggregation number), that minimizes the mean total system time. In addition, we derive the explicit optimal aggregation parameter for a D/M/1 transmission model with each aggregation scheme and clarify that it provides accurate approximation of that of each aggregation model. The optimal aggregation interval was determined by the transmission rate alone, while the optimal aggregation number was determined by the arrival and transmission rates alone with explicitly derived proportional constants. These results can provide a theoretical basis and a guideline for designing aggregation devices, such as IoT gateways.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3010/_p
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@ARTICLE{e101-b_10_2186,
author={Hideaki YOSHINO, Kenko OTA, Takefumi HIRAGURI, },
journal={IEICE TRANSACTIONS on Communications},
title={Queueing Delay Analysis and Optimization of Statistical Data Aggregation and Transmission Systems},
year={2018},
volume={E101-B},
number={10},
pages={2186-2195},
abstract={Data aggregation, which is the process of summarizing a large amount of data, is an effective method for saving limited communication resources, such as radio frequency and sensor-node energy. Packet aggregation in wireless LAN and sensed-data aggregation in wireless sensor networks are typical examples. We propose and analyze two queueing models of fundamental statistical data aggregation schemes: constant interval and constant aggregation number. We represent each aggregation scheme by a tandem queueing network model with a gate at the aggregation process and a single server queue at a transmission process. We analytically derive the stationary distribution and Laplace-Stieltjes transform of the system time for each aggregation and transmission process and of the total system time. We then numerically evaluate the stationary mean system time characteristics and clarify that each model has an optimal aggregation parameter (i.e., an optimal aggregation interval or optimal aggregation number), that minimizes the mean total system time. In addition, we derive the explicit optimal aggregation parameter for a D/M/1 transmission model with each aggregation scheme and clarify that it provides accurate approximation of that of each aggregation model. The optimal aggregation interval was determined by the transmission rate alone, while the optimal aggregation number was determined by the arrival and transmission rates alone with explicitly derived proportional constants. These results can provide a theoretical basis and a guideline for designing aggregation devices, such as IoT gateways.},
keywords={},
doi={10.1587/transcom.2018EBP3010},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Queueing Delay Analysis and Optimization of Statistical Data Aggregation and Transmission Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 2186
EP - 2195
AU - Hideaki YOSHINO
AU - Kenko OTA
AU - Takefumi HIRAGURI
PY - 2018
DO - 10.1587/transcom.2018EBP3010
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2018
AB - Data aggregation, which is the process of summarizing a large amount of data, is an effective method for saving limited communication resources, such as radio frequency and sensor-node energy. Packet aggregation in wireless LAN and sensed-data aggregation in wireless sensor networks are typical examples. We propose and analyze two queueing models of fundamental statistical data aggregation schemes: constant interval and constant aggregation number. We represent each aggregation scheme by a tandem queueing network model with a gate at the aggregation process and a single server queue at a transmission process. We analytically derive the stationary distribution and Laplace-Stieltjes transform of the system time for each aggregation and transmission process and of the total system time. We then numerically evaluate the stationary mean system time characteristics and clarify that each model has an optimal aggregation parameter (i.e., an optimal aggregation interval or optimal aggregation number), that minimizes the mean total system time. In addition, we derive the explicit optimal aggregation parameter for a D/M/1 transmission model with each aggregation scheme and clarify that it provides accurate approximation of that of each aggregation model. The optimal aggregation interval was determined by the transmission rate alone, while the optimal aggregation number was determined by the arrival and transmission rates alone with explicitly derived proportional constants. These results can provide a theoretical basis and a guideline for designing aggregation devices, such as IoT gateways.
ER -