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
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O monitoramento espacial preditivo, que prevê informações espaciais, como o tráfego rodoviário, tem atraído muita atenção no contexto das cidades inteligentes. O aprendizado de máquina permite o monitoramento espacial preditivo usando uma grande quantidade de dados agregados de sensores. Como a capacidade das redes móveis é estritamente limitada, ocorrem sérios atrasos na transmissão quando as cargas de tráfego de comunicação são pesadas. Se alguns dos dados utilizados para a monitorização espacial preditiva não chegarem a tempo, a precisão da previsão degrada-se porque a previsão tem de ser feita utilizando apenas os dados recebidos, o que implica que os dados para previsão são “sensíveis ao atraso”. Uma técnica de alocação baseada em utilidade sugeriu modelagem de características temporais de tais dados sensíveis a atrasos para transmissão priorizada. No entanto, nenhum estudo abordou o modelo temporal para transmissão priorizada no monitoramento espacial preditivo. Portanto, este artigo propõe um esquema que permite a criação de um modelo temporal para monitoramento espacial preditivo. O esquema é composto aproximadamente de duas etapas: a primeira envolve a criação de dados de treinamento a partir de dados originais de séries temporais e um modelo de aprendizado de máquina que pode usar os dados, enquanto a segunda etapa envolve a modelagem de um modelo temporal usando a seleção de recursos no modelo de aprendizagem. A seleção de recursos permite estimar a importância dos dados em termos de quanto os dados contribuem para a precisão da previsão do modelo de aprendizado de máquina. Este artigo considera a previsão do tráfego rodoviário como um cenário e mostra que os modelos temporais criados com o esquema proposto podem lidar com conjuntos de dados espaciais reais. Um estudo numérico demonstrou como nosso modelo temporal funciona efetivamente na transmissão priorizada para monitoramento espacial preditivo em termos de precisão de previsão.
Keiichiro SATO
Kyoto University
Ryoichi SHINKUMA
Kyoto University
Takehiro SATO
Kyoto University
Eiji OKI
Kyoto University
Takanori IWAI
NEC Corporation
Takeo ONISHI
NEC Corporation
Takahiro NOBUKIYO
NEC Corporation
Dai KANETOMO
NEC Corporation
Kozo SATODA
NEC Corporation
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Keiichiro SATO, Ryoichi SHINKUMA, Takehiro SATO, Eiji OKI, Takanori IWAI, Takeo ONISHI, Takahiro NOBUKIYO, Dai KANETOMO, Kozo SATODA, "Creation of Temporal Model for Prioritized Transmission in Predictive Spatial-Monitoring Using Machine Learning" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 8, pp. 951-960, August 2021, doi: 10.1587/transcom.2020EBP3175.
Abstract: Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3175/_p
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@ARTICLE{e104-b_8_951,
author={Keiichiro SATO, Ryoichi SHINKUMA, Takehiro SATO, Eiji OKI, Takanori IWAI, Takeo ONISHI, Takahiro NOBUKIYO, Dai KANETOMO, Kozo SATODA, },
journal={IEICE TRANSACTIONS on Communications},
title={Creation of Temporal Model for Prioritized Transmission in Predictive Spatial-Monitoring Using Machine Learning},
year={2021},
volume={E104-B},
number={8},
pages={951-960},
abstract={Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.},
keywords={},
doi={10.1587/transcom.2020EBP3175},
ISSN={1745-1345},
month={August},}
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TY - JOUR
TI - Creation of Temporal Model for Prioritized Transmission in Predictive Spatial-Monitoring Using Machine Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 951
EP - 960
AU - Keiichiro SATO
AU - Ryoichi SHINKUMA
AU - Takehiro SATO
AU - Eiji OKI
AU - Takanori IWAI
AU - Takeo ONISHI
AU - Takahiro NOBUKIYO
AU - Dai KANETOMO
AU - Kozo SATODA
PY - 2021
DO - 10.1587/transcom.2020EBP3175
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2021
AB - Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.
ER -