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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

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 -