IEICE TRANSACTIONS on Communications
Queueing Delay Analysis and Optimization of Statistical Data Aggregation and Transmission Systems
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.10 pp.2186-2195
- Publication Date
- 2018/10/01
- Publicized
- 2018/04/02
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018EBP3010
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Hideaki YOSHINO
Nippon Institute of Technology
Kenko OTA
Nippon Institute of Technology
Takefumi HIRAGURI
Nippon Institute of Technology
Keyword
Latest Issue
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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},}
Copy
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 -
English
