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
A coleta de som ambiental utilizando telefones celulares de última geração nos oferece oportunidades de capturar informações contextuais ricas no mundo real. A informação recolhida pode ser utilizada para vários fins, desde investigação académica até apoio à subsistência. Além disso, a mobilidade dos telemóveis abre uma porta para a formação fácil de uma infra-estrutura de detecção dinâmica, a fim de recolher dados refinados, mas ainda em grande escala, tanto a partir de perspectivas espaciais como temporais. No entanto, a recolha, análise, armazenamento e partilha de dados sonoros normalmente envolve um maior consumo de energia do que os dados escalares e, como qualquer dispositivo alimentado por bateria, os telemóveis também enfrentam a realidade de restrições energéticas. Dado que a primeira prioridade das pessoas é, naturalmente, utilizar os telemóveis para os seus próprios fins, há ocasiões em que as pessoas não estarão dispostas a permitir que os seus telemóveis sejam utilizados como dispositivos sensores, temendo que fiquem sem bateria. Portanto, a nossa investigação centra-se na detecção com eficiência energética, para reduzir o consumo médio de energia e prolongar a vida útil geral do sistema. Neste artigo, propomos um esquema de escalonamento de nós para nós móveis. Ao aplicar este esquema, cronogramas de detecção otimizados (ciclos de trabalho ATIVO/SLEEP) serão gerados periodicamente em cada nó. Seguindo o cronograma fornecido durante a detecção, a eficiência energética pode ser alcançada enquanto a Qualidade de Serviço original (ou seja, taxa de cobertura) é mantida. Ao contrário da maioria dos trabalhos anteriores que se basearam no modelo de cobertura de disco binário ideal, nossa proposta é desenhada sob um modelo probabilístico de cobertura de disco que leva em consideração as características de propagação do som. Além disso, este é o primeiro esquema adaptável a redes de sensores móveis de grande escala, onde a topologia muda dinamicamente. Um modelo preciso de consumo de energia é adotado para avaliar o esquema proposto. Os resultados da simulação mostram que o nosso esquema pode reduzir até 48% do consumo de energia num ambiente ideal e até 31% do consumo de energia num ambiente realista. A robustez do nosso esquema também é verificada em diferentes tipos de terrenos de detecção e ambientes de comunicação.
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Tian HAO, Masayuki IWAI, Yoshito TOBE, Kaoru SEZAKI, "ESMO: An Energy-Efficient Mobile Node Scheduling Scheme for Sound Sensing" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 11, pp. 2912-2924, November 2010, doi: 10.1587/transcom.E93.B.2912.
Abstract: Collecting environmental sound by utilizing high-end mobile phones provides us opportunities to capture rich contextual information in real world. The gathered information can be used for various purposes, ranging from academic research to livelihood support. Furthermore, mobility of mobile phones opens a door for easily forming a dynamic sensing infrastructure, in order to gather fine-grained, but still large-scale data from both spatial and temporal perspectives. However, collecting, analyzing, storing, and sharing of sound data usually involve large energy consumption than scalar data, and like any battery-operated device, mobile phones also face the reality of energy constraints. Because people's first priorities are naturally to use mobile phones for their own purposes, there are occasions when people will not be inclined to allow their mobile phones to be used as sensing devices fearing that they will run out of batteries. Therefore, our research focuses on energy-efficient sensing, to reduce average energy consumption and to extend overall system lifetime. In this paper, we propose a node scheduling scheme for mobile nodes. By applying this scheme, optimized sensing schedules (ACTIVE/SLEEP duty cycles) will be periodically generated at each node. Following the provided schedule during sensing, energy-efficiency can be realized while original Quality of Service (i.e. coverage rate) is retained. Unlike most previous works which were based on ideal binary disk coverage model, our proposal is designed under a probabilistic disk coverage model which takes the characteristic of sound propagation into consideration. Furthermore, this is the first scheme that is adaptable to large-scale mobile sensor networks where topology dynamically changes. An accurate energy consumption model is adopted for evaluating the proposed scheme. Simulation results show that our scheme can reduce up to 48% energy consumption in an ideal environment and up to 31% energy consumption in a realistic environment. The robustness of our scheme is also verified against different type of sensing terrains and communication environments.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.2912/_p
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@ARTICLE{e93-b_11_2912,
author={Tian HAO, Masayuki IWAI, Yoshito TOBE, Kaoru SEZAKI, },
journal={IEICE TRANSACTIONS on Communications},
title={ESMO: An Energy-Efficient Mobile Node Scheduling Scheme for Sound Sensing},
year={2010},
volume={E93-B},
number={11},
pages={2912-2924},
abstract={Collecting environmental sound by utilizing high-end mobile phones provides us opportunities to capture rich contextual information in real world. The gathered information can be used for various purposes, ranging from academic research to livelihood support. Furthermore, mobility of mobile phones opens a door for easily forming a dynamic sensing infrastructure, in order to gather fine-grained, but still large-scale data from both spatial and temporal perspectives. However, collecting, analyzing, storing, and sharing of sound data usually involve large energy consumption than scalar data, and like any battery-operated device, mobile phones also face the reality of energy constraints. Because people's first priorities are naturally to use mobile phones for their own purposes, there are occasions when people will not be inclined to allow their mobile phones to be used as sensing devices fearing that they will run out of batteries. Therefore, our research focuses on energy-efficient sensing, to reduce average energy consumption and to extend overall system lifetime. In this paper, we propose a node scheduling scheme for mobile nodes. By applying this scheme, optimized sensing schedules (ACTIVE/SLEEP duty cycles) will be periodically generated at each node. Following the provided schedule during sensing, energy-efficiency can be realized while original Quality of Service (i.e. coverage rate) is retained. Unlike most previous works which were based on ideal binary disk coverage model, our proposal is designed under a probabilistic disk coverage model which takes the characteristic of sound propagation into consideration. Furthermore, this is the first scheme that is adaptable to large-scale mobile sensor networks where topology dynamically changes. An accurate energy consumption model is adopted for evaluating the proposed scheme. Simulation results show that our scheme can reduce up to 48% energy consumption in an ideal environment and up to 31% energy consumption in a realistic environment. The robustness of our scheme is also verified against different type of sensing terrains and communication environments.},
keywords={},
doi={10.1587/transcom.E93.B.2912},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - ESMO: An Energy-Efficient Mobile Node Scheduling Scheme for Sound Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 2912
EP - 2924
AU - Tian HAO
AU - Masayuki IWAI
AU - Yoshito TOBE
AU - Kaoru SEZAKI
PY - 2010
DO - 10.1587/transcom.E93.B.2912
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2010
AB - Collecting environmental sound by utilizing high-end mobile phones provides us opportunities to capture rich contextual information in real world. The gathered information can be used for various purposes, ranging from academic research to livelihood support. Furthermore, mobility of mobile phones opens a door for easily forming a dynamic sensing infrastructure, in order to gather fine-grained, but still large-scale data from both spatial and temporal perspectives. However, collecting, analyzing, storing, and sharing of sound data usually involve large energy consumption than scalar data, and like any battery-operated device, mobile phones also face the reality of energy constraints. Because people's first priorities are naturally to use mobile phones for their own purposes, there are occasions when people will not be inclined to allow their mobile phones to be used as sensing devices fearing that they will run out of batteries. Therefore, our research focuses on energy-efficient sensing, to reduce average energy consumption and to extend overall system lifetime. In this paper, we propose a node scheduling scheme for mobile nodes. By applying this scheme, optimized sensing schedules (ACTIVE/SLEEP duty cycles) will be periodically generated at each node. Following the provided schedule during sensing, energy-efficiency can be realized while original Quality of Service (i.e. coverage rate) is retained. Unlike most previous works which were based on ideal binary disk coverage model, our proposal is designed under a probabilistic disk coverage model which takes the characteristic of sound propagation into consideration. Furthermore, this is the first scheme that is adaptable to large-scale mobile sensor networks where topology dynamically changes. An accurate energy consumption model is adopted for evaluating the proposed scheme. Simulation results show that our scheme can reduce up to 48% energy consumption in an ideal environment and up to 31% energy consumption in a realistic environment. The robustness of our scheme is also verified against different type of sensing terrains and communication environments.
ER -