Real-time link quality estimation for industrial wireless sensor networks using dedicated nodes

Gomes, Ruan Delgado; Queiroz, Diego V.; Filho, Abel Cavalcante Lima; Fonseca, Iguatemi E.; Alencar, Marcelo Sampaio de

doi:10.1016/j.adhoc.2017.02.007

Cited by 68 publications

(53 citation statements)

References 30 publications

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“…For this reason several communication oriented performance improvements have been achieved. Such improvements include reliable communication slot assignment [128], autonomous channel switching for spectrum sharing [130], synchronization for nodes with imprecise timers [138], and real-time link quality estimation [144]. Cooperative data relaying schemes also facilitate secure and interference-free data management, with recent approaches employing fountaincoding aided transmissions [132] and belief function based cooperation [134].…”

Section: B Architectures Focusing On Iiot / Icps and Wsanmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: B Architectures Focusing On Iiot / Icps and Wsanmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…[1][2][3][4][5][6][7][8] For most higher layer functions, including routing, the packet delivery ratio (PDR) is the single most important information about a link's quality. This information is needed in order to decide the best path through a network, the right level of forward error correction (FEC) coding, what transmission power to use, which transmission rate to use, which channel to use in a multichannel network, and much more.…”

Section: Introductionmentioning

confidence: 99%

“…The process of identifying a link's quality is known as link quality estimation (LQE) and has been a frequent topic of study. [1][2][3][4][5][6][7][8] For most higher layer functions, including routing, the packet delivery ratio (PDR) is the single most important information about a link's quality. It is known that the PDR is dependent on the packet length.…”

Section: Introductionmentioning

confidence: 99%

Link quality estimation for arbitrary packet sizes over wireless links using packet reception events

Jacobsson

Rohner

2019

Int J Communication

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The packet error rate of wireless links is known to increase with the length of packets. Yet, packet length is rarely taken into account in protocols and algorithms estimating the packet error rate. Still, it is an important factor that higher layer protocols need to be aware of. In this article, we systematically measure the relationship between packet length and packet error rate over a wide range of wireless links and technologies. On the basis of our measurements, we propose a simple empirical model that can capture this behavior. Using this model, multiple methods are proposed that can estimate the packet error rate for any packet length by sampling the link. We consider methods based on hello packets with controlled packet lengths as well as data packets, where the transmitted packet lengths cannot be controlled. We investigate the accuracy of the different estimation methods in various situations and show how they are able to predict the delivery ratio for different packet sizes. KEYWORDSbit error rate, hello protocol, link quality estimation, packet error ratewhere l is the packet length in bits, including all headers.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Existem duas abordagens para criar uma lista negra: global e local. Na primeira abordagem, todos os nós têm a mesma lista de canais, mas essa solução pode ser subótima, já que os canais podem apresentar qualidade diferente para diferentes enlaces da rede, mesmo se os nós que fazem parte dos diferentes enlaces estiverem próximos uns dos outros [4]. No segundo método, cada par de nós pode utilizar diferentes conjuntos de canais [5], ou seja, é definida uma lista negra específica por enlace, mas mecanismos de lista negra locais são mais complexos que as abordagens globais e podem introduzir colisões quando dois ou mais nós transmitem no mesmo timeslot com listas negras diferentes, o que pode resultar em múltiplos nós transmitindo no mesmo canal ao mesmo tempo.…”

Section: Introductionunclassified

“…Para permitir um correto gerenciamento das listas negras, e manter a qualidade de serviço (QoS) da rede, é necessário realizar a estimação de qualidade dos enlaces [6]. Em alguns trabalhos, a estimação é feita pelo nó receptor [4], e outros a fazem no nó transmissor [2]. Esse trabalho realiza a estimação no nó coordenador, que nesse caso trabalha também como nó sorvedouro (sink node) e receptor dos pacotes em uma rede com topologia em estrela.…”

Section: Introductionunclassified

Avaliação do Uso de Adaptação de Canal e Estimação de Qualidade de Enlace em Redes TSCH

Queiroz¹,

Gomes²,

Fonseca³

et al. 2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-O protocolo Time-Slotted Channel Hopping (TSCH) tem atraído a atenção dos pesquisadores por lidar com problemas de interferência e multipercurso, com o método de salto em frequência. Para um bom desempenho da rede, é necessário realizar o gerenciamento de lista negra de canais, com base em um estimador de qualidade de enlace, de modo a evitar o uso de canais com baixa qualidade na rede. Esse trabalho avalia o uso de adaptação de canais, com base na estimação de qualidade de enlace, em redes TSCH com topologia em estrela. Os resultados mostram que uma lista negra maior leva a um aumento no desempenho, quando a estimação é realizada de forma contínua. Palavras-Chave-IEEE 802.15.4e TSCH, redes de sensores sem fio industriais, adaptação de canal Resumo-The Time-Slotted Channel Hopping (TSCH) protocol has attracted the researchers' attention for being able to handle interference and multipath problems with the frequency hopping method. To perform well it is necessary to manage the channel blacklists, based on a link quality estimator, to avoid using channels with low quality. This work evaluates the use of channel adaptation, based on link quality estimation, in TSCH networks with star topology. The results have shown that a larger blacklist leads to an increase in performance, when the estimation is performed continuously.

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Real-time link quality estimation for industrial wireless sensor networks using dedicated nodes

Cited by 68 publications

References 30 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Link quality estimation for arbitrary packet sizes over wireless links using packet reception events

Avaliação do Uso de Adaptação de Canal e Estimação de Qualidade de Enlace em Redes TSCH

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