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
Neste artigo, defendemos a aplicação do conceito de despertar baseado em conteúdo para estimativa distribuída em redes de sensores sem fio que empregam receptores de despertar. Com a estimativa distribuída, onde dados de detecção de múltiplos nós são usados para estimar uma observação alvo, o consumo de energia pode ser reduzido garantindo que apenas um subconjunto de nós na rede transmita seus dados, de modo que os dados coletados possam garantir a precisão de estimativa necessária. . Nesse caso, um coletor precisa ativar seletivamente os nós sensores cujos dados podem contribuir para a melhoria da precisão da estimativa. Neste artigo, propomos sinalização de ativação chamada amostragem estimada (ES), que pode ativar seletivamente os nós desejados usando controle de ativação baseado em conteúdo. O método ES inclui um mecanismo que procura dinamicamente os nós desejados em uma distribuição de dados de detecção. Com resultados numéricos obtidos por simulações computacionais, mostramos que a estimativa distribuída com o método ES atinge menor consumo de energia do que o despertar convencional baseado em identidade, ao mesmo tempo que satisfaz a precisão exigida. Mostramos também que o mecanismo dinâmico proposto controla com precisão o equilíbrio entre atraso e consumo de energia para completar a estimativa distribuída.
Hitoshi KAWAKITA
Kansai University
Hiroyuki YOMO
Kansai University
Petar POPOVSKI
Aalborg University
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Hitoshi KAWAKITA, Hiroyuki YOMO, Petar POPOVSKI, "Energy-Efficient Distributed Estimation Using Content-Based Wake-Up in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 4, pp. 391-400, April 2021, doi: 10.1587/transcom.2020EBT0003.
Abstract: In this paper, we advocate applying the concept of content-based wake-up to distributed estimation in wireless sensor networks employing wake-up receivers. With distributed estimation, where sensing data of multiple nodes are used for estimating a target observation, the energy consumption can be reduced by ensuring that only a subset of nodes in the network transmit their data, such that the collected data can guarantee the required estimation accuracy. In this case, a sink needs to selectively wake up those sensor nodes whose data can contribute to the improvement of estimation accuracy. In this paper, we propose wake-up signaling called estimative sampling (ES) that can selectively activate the desired nodes by using content-based wake-up control. The ES method includes a mechanism that dynamically searches for the desired nodes over a distribution of sensing data. With numerical results obtained by computer simulations, we show that the distributed estimation with ES method achieves lower energy consumption than conventional identity-based wake-up while satisfying the required accuracy. We also show that the proposed dynamic mechanism finely controls the trade-off between delay and energy consumption to complete the distributed estimation.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBT0003/_p
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@ARTICLE{e104-b_4_391,
author={Hitoshi KAWAKITA, Hiroyuki YOMO, Petar POPOVSKI, },
journal={IEICE TRANSACTIONS on Communications},
title={Energy-Efficient Distributed Estimation Using Content-Based Wake-Up in Wireless Sensor Networks},
year={2021},
volume={E104-B},
number={4},
pages={391-400},
abstract={In this paper, we advocate applying the concept of content-based wake-up to distributed estimation in wireless sensor networks employing wake-up receivers. With distributed estimation, where sensing data of multiple nodes are used for estimating a target observation, the energy consumption can be reduced by ensuring that only a subset of nodes in the network transmit their data, such that the collected data can guarantee the required estimation accuracy. In this case, a sink needs to selectively wake up those sensor nodes whose data can contribute to the improvement of estimation accuracy. In this paper, we propose wake-up signaling called estimative sampling (ES) that can selectively activate the desired nodes by using content-based wake-up control. The ES method includes a mechanism that dynamically searches for the desired nodes over a distribution of sensing data. With numerical results obtained by computer simulations, we show that the distributed estimation with ES method achieves lower energy consumption than conventional identity-based wake-up while satisfying the required accuracy. We also show that the proposed dynamic mechanism finely controls the trade-off between delay and energy consumption to complete the distributed estimation.},
keywords={},
doi={10.1587/transcom.2020EBT0003},
ISSN={1745-1345},
month={April},}
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TY - JOUR
TI - Energy-Efficient Distributed Estimation Using Content-Based Wake-Up in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 391
EP - 400
AU - Hitoshi KAWAKITA
AU - Hiroyuki YOMO
AU - Petar POPOVSKI
PY - 2021
DO - 10.1587/transcom.2020EBT0003
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2021
AB - In this paper, we advocate applying the concept of content-based wake-up to distributed estimation in wireless sensor networks employing wake-up receivers. With distributed estimation, where sensing data of multiple nodes are used for estimating a target observation, the energy consumption can be reduced by ensuring that only a subset of nodes in the network transmit their data, such that the collected data can guarantee the required estimation accuracy. In this case, a sink needs to selectively wake up those sensor nodes whose data can contribute to the improvement of estimation accuracy. In this paper, we propose wake-up signaling called estimative sampling (ES) that can selectively activate the desired nodes by using content-based wake-up control. The ES method includes a mechanism that dynamically searches for the desired nodes over a distribution of sensing data. With numerical results obtained by computer simulations, we show that the distributed estimation with ES method achieves lower energy consumption than conventional identity-based wake-up while satisfying the required accuracy. We also show that the proposed dynamic mechanism finely controls the trade-off between delay and energy consumption to complete the distributed estimation.
ER -