Energy consumption prediction in software-defined wirelwss sensor networks.
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | http://www.teses.usp.br/teses/disponiveis/3/3141/tde-04052018-113551/ |
Resumo: | Energy conservation is a main concern in Wireless Sensor Networks (WSN). To reduce energy consumption it is important to know how it is spent and how much is available during the node and network operation. Several previous works have proposed energy consumption models focused on the communication module, while neglecting the processing and sensing activities. Other works presented more complex and complete models, but lacked experiments to demonstrate their accuracy in real deployments. The main objective of this work is to design and to evaluate an accurate energy consumption model for WSN, which considers the sensing, processing, and communication modules usage. This model was used to implement two energy consumption prediction mechanism. One mechanism is based in Markov chains and the other one is based in time series analysis. The metrics to evaluate the model and prediction mechanisms performance were: energy consumption estimation accuracy, energy consumption prediction accuracy, and node\'s communication and processing resources usage. The energy consumption prediction mechanisms performance was compared using two implementation schemes: running the prediction algorithm in the sensor node and running the prediction algorithm in a Software-Defined Networking controller. The implementation was conducted using IT-SDN, a Software-Defined Wireless Sensor Network framework. For the evaluation, simulation and emulation used COOJA, while testbed experiments used TelosB devices. Results showed that considering the sensing, processing, and communication energy consumption into the model, it is possible to obtain an accurate energy consumption estimation for Wireless Sensor Networks. Also, the use of a Software-Defined Networking controller for processing complex prediction algorithms can improve the prediction accuracy. |
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Energy consumption prediction in software-defined wirelwss sensor networks.Previsão de consumo de energia em redes de sensores sem fio definidas por software.Consumo de energia elétricaEnergy consumptionSoftware-defined networkingWirelessWireless sensor networksEnergy conservation is a main concern in Wireless Sensor Networks (WSN). To reduce energy consumption it is important to know how it is spent and how much is available during the node and network operation. Several previous works have proposed energy consumption models focused on the communication module, while neglecting the processing and sensing activities. Other works presented more complex and complete models, but lacked experiments to demonstrate their accuracy in real deployments. The main objective of this work is to design and to evaluate an accurate energy consumption model for WSN, which considers the sensing, processing, and communication modules usage. This model was used to implement two energy consumption prediction mechanism. One mechanism is based in Markov chains and the other one is based in time series analysis. The metrics to evaluate the model and prediction mechanisms performance were: energy consumption estimation accuracy, energy consumption prediction accuracy, and node\'s communication and processing resources usage. The energy consumption prediction mechanisms performance was compared using two implementation schemes: running the prediction algorithm in the sensor node and running the prediction algorithm in a Software-Defined Networking controller. The implementation was conducted using IT-SDN, a Software-Defined Wireless Sensor Network framework. For the evaluation, simulation and emulation used COOJA, while testbed experiments used TelosB devices. Results showed that considering the sensing, processing, and communication energy consumption into the model, it is possible to obtain an accurate energy consumption estimation for Wireless Sensor Networks. Also, the use of a Software-Defined Networking controller for processing complex prediction algorithms can improve the prediction accuracy.A conservação da energia é uma das principais preocupações nas Redes de Sensores Sem Fio (WSN, do inglês Wireless Sensor Networks). Para reduzir o consumo de energia, é importante saber como a energia é gasta e quanta energia há disponível durante o funcionamento da rede. Diversos trabalhos anteriores propuseram modelos de consumo de energia focados no módulo de comunicação, ignorando o consumo por tarefas de processamento e sensoriamento. Outros trabalhos apresentam modelos mais completos e complexos, mas carecem de experimentos que demonstrem a exatidão em dispositivos reais. O objetivo principal deste trabalho é projetar e avaliar um modelo de consumo de energia para WSN que considere o consumo por sensoriamento, processamento e comunicação. Este modelo foi utilizado para implementar dois mecanismos de previsão de consumo de energia, um deles baseado em cadeias de Markov e o outro baseado em séries temporais. As métricas para avaliar o desempenho do modelo e dos mecanismos de previsão de consumo de energia foram: exatidão da estimativa de consumo de energia, exatidão da previsão de consumo de energia e uso dos recursos de comunicação e processamento do nó. O desempenho dos mecanismos de previsão de consumo de energia foram comparados utilizando dois esquemas de implementação: rodando o algoritmo de previsão no nó sensor e rodando o algoritmo de previsão em um controlador de rede definida por software. A implementação foi conduzida utilizando IT-SDN, um arcabouço de desenvolvimento de redes de sensores sem fio definidas por software. A avaliação foi feita com simulações e emulações utilizando o simulador COOJA e ensaios com dispositivos reais utilizando o TelosB. Os resultados mostraram que considerando o consumo de energia por sensoriamento, processamento e communicação, é possivel fazer uma estimativa de consumo de energia em redes de sensores sem fio com uma boa exatidão. Ainda, o uso de um controlador de rede definida por software para processamento de algoritmos de previsão complexos pode aumentar a exatidão da previsão.Biblioteca Digitais de Teses e Dissertações da USPMargi, Cíntia BorgesNuñez Segura, Gustavo Alonso 2018-02-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://www.teses.usp.br/teses/disponiveis/3/3141/tde-04052018-113551/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2018-09-20T19:49:24Zoai:teses.usp.br:tde-04052018-113551Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212018-09-20T19:49:24Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Energy consumption prediction in software-defined wirelwss sensor networks. Previsão de consumo de energia em redes de sensores sem fio definidas por software. |
title |
Energy consumption prediction in software-defined wirelwss sensor networks. |
spellingShingle |
Energy consumption prediction in software-defined wirelwss sensor networks. Nuñez Segura, Gustavo Alonso Consumo de energia elétrica Energy consumption Software-defined networking Wireless Wireless sensor networks |
title_short |
Energy consumption prediction in software-defined wirelwss sensor networks. |
title_full |
Energy consumption prediction in software-defined wirelwss sensor networks. |
title_fullStr |
Energy consumption prediction in software-defined wirelwss sensor networks. |
title_full_unstemmed |
Energy consumption prediction in software-defined wirelwss sensor networks. |
title_sort |
Energy consumption prediction in software-defined wirelwss sensor networks. |
author |
Nuñez Segura, Gustavo Alonso |
author_facet |
Nuñez Segura, Gustavo Alonso |
author_role |
author |
dc.contributor.none.fl_str_mv |
Margi, Cíntia Borges |
dc.contributor.author.fl_str_mv |
Nuñez Segura, Gustavo Alonso |
dc.subject.por.fl_str_mv |
Consumo de energia elétrica Energy consumption Software-defined networking Wireless Wireless sensor networks |
topic |
Consumo de energia elétrica Energy consumption Software-defined networking Wireless Wireless sensor networks |
description |
Energy conservation is a main concern in Wireless Sensor Networks (WSN). To reduce energy consumption it is important to know how it is spent and how much is available during the node and network operation. Several previous works have proposed energy consumption models focused on the communication module, while neglecting the processing and sensing activities. Other works presented more complex and complete models, but lacked experiments to demonstrate their accuracy in real deployments. The main objective of this work is to design and to evaluate an accurate energy consumption model for WSN, which considers the sensing, processing, and communication modules usage. This model was used to implement two energy consumption prediction mechanism. One mechanism is based in Markov chains and the other one is based in time series analysis. The metrics to evaluate the model and prediction mechanisms performance were: energy consumption estimation accuracy, energy consumption prediction accuracy, and node\'s communication and processing resources usage. The energy consumption prediction mechanisms performance was compared using two implementation schemes: running the prediction algorithm in the sensor node and running the prediction algorithm in a Software-Defined Networking controller. The implementation was conducted using IT-SDN, a Software-Defined Wireless Sensor Network framework. For the evaluation, simulation and emulation used COOJA, while testbed experiments used TelosB devices. Results showed that considering the sensing, processing, and communication energy consumption into the model, it is possible to obtain an accurate energy consumption estimation for Wireless Sensor Networks. Also, the use of a Software-Defined Networking controller for processing complex prediction algorithms can improve the prediction accuracy. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-02-20 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.teses.usp.br/teses/disponiveis/3/3141/tde-04052018-113551/ |
url |
http://www.teses.usp.br/teses/disponiveis/3/3141/tde-04052018-113551/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
_version_ |
1809090369558675456 |