Performance Improvement of ZigBee Networks in Coexistence of Wi-Fi Signals

Seyedolhosseini, Atefesadat; Masoumi, Nasser; Modarressi, Mehdi

doi:10.1145/3134383.3134408

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Seyedolhosseini et al [Seyedolhosseini et al 2017] apresenta a formulação para mitigar a interferência entre os dispositivos Wifi e Zigbee baseando-se na Taxa de Ruído de Interferência de Sinal (SINR), que mede a qualidade do sinal de transmissão. O autor utiliza o SINR junto com o algoritmo Kruskal para determinar o menor caminho entre os nós Zigbee de maneira que não causem perdas de pacotes nos nós Wifi.…”

Section: Wang Et Alunclassified

“…As tecnologias mais populares para esses AIs são Wifi e Zigbee, sendo que ambas tecnologias usam a frequência de comunicação 2.4 GHz ISM (Industrial, Scientific and Medical) [Ma et al 2019]. O Wifié o principal meio de comunicação dos dispositivos móveis dos usuários, enquanto o Zigbeeé amplamente utilizado em dispositivos IoT devidoà capacidade de transmissão e limites do uso de energia [Seyedolhosseini et al 2017].…”

Section: Introductionunclassified

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Mitigação de Interferência entre Tecnologias Sem Fio em Ambientes Inteligentes

Rocha¹,

Mosca²,

Silva³

et al. 2021

Anais Do XIII Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2021)

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A Internet das Coisas (IoT) surgiu como um paradigma para evoluir as tarefas diárias e implantar novos serviços em ambientes como casas, cidades, campus e etc. Transformando-os em Ambientes Inteligentes (ou AIs). Um AI é composto por dispositivos sem fio heterogêneos, sendo que parte deles são móveis. Esses dispositivos heterogêneos apresentam um problema de interferência cruzada, uma vez que a maioria deles usa a banda ISM de 2.4 GHz para comunicação. O problema de interferência cruzada afeta diretamente a qualidade do serviço (QoS), principalmente os dispositivos de baixa potência de transmissão que fornecem a base dos serviços executados no topo da rede. Dentro desse contexto, este artigo apresenta o mecanismo Mitigação de Interferência Cruzada (MIC), o qual visa mitigar o problema de interferência cruzada em ambientes inteligentes. O mecanismo MIC considera a mobilidade dos dispositivos e sua capacidade de comunicação para realizar uma atribuição de canais sem fio adequada. Os resultados, a partir dos experimentos realizados, sugerem que o MIC minimiza a interferência dos dispositivos aplicados, enquanto reduz significativamente o tempo computacional, quando comparado às abordagens existentes.

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Section: Wang Et Alunclassified

Section: Introductionunclassified

Mitigação de Interferência entre Tecnologias Sem Fio em Ambientes Inteligentes

Rocha¹,

Mosca²,

Silva³

et al. 2021

Anais Do XIII Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2021)

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“…Therefore, the cost function in (3) and the constraints in (7) and (8) are rewritten by substituting (10), (11), and (12).…”

Section: Preprocessormentioning

confidence: 99%

“…The main components of a smart lighting system include dimmable light-emitting diodes (LEDs), photodetectors, occupancy sensors, and controller units; which have been the leading research topics over the recent years [6][7][8][9][10]. A communication protocol, which can be either wired or wireless, is employed for interconnecting the smart indoor lighting components [11][12][13]. The heart of a typical smart lighting system is a controller unit that adjusts the luminaires dimming level according to the photodetectors' output, occupancy condition, and type of visual tasks running in each zone.…”

Section: Introductionmentioning

confidence: 99%

Daylight adaptive smart indoor lighting control method using artificial neural networks

Seyedolhosseini

Masoumi

Modarressi

et al. 2020

Journal of Building Engineering

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Accurate and efficient adjustment of maintained illuminance and illuminance uniformity in indoor environments with daylight variations is a tremendous challenge, mainly due to the nonlinear and time-variant nature of lighting control systems. In this paper, we propose a smart lighting control method for indoor environments with both dimmable (controllable) and uncontrollable external light sources. Targeting an indoor environment with multiple zones, each requiring a different lighting condition and equipped with an unequal number of photodetectors and dimmable light sources, this paper presents a novel control mechanism that determines the output flux of each luminary in such a way that each zone (1) receives the required maintained illuminance, (2) illuminance uniformity conditions are met inside each zone, and (3) the power consumption is optimized. This method uses a neural network to learn the impact of each luminary on the maintained illuminance of each zone and adjust the dimming level of the luminaries to establish the required illuminance in the zones. We also rely on photodetectors to measure the daylight illuminance continuously and use it as the bias value for the neural network. The new priority value allows losing some illuminance accuracy (by allowing lager difference between the actual and required maintained illuminance values) for low-priority zones to reduce power consumption. The method has been evaluated in different test cases by chaining the widely-used DIALux tool and some MATLAB toolboxes. The evaluation results show that the method can achieve considerable accuracy by yielding an average Mean Square Error of 1.2 between the demanded and sensed illuminance values. Furthermore, when all sensors except one reference sensor are removed from each zone (to increase user comfort or reduce cost), the mean square error is less than 25.4 across all considered test cases.

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“…In a separate study, learning methods is applied in order to develop a smart lighting system and the method is applicable for static condition where daylight does not vary during daytime and objects are constant [12]. Besides, transmission is not considered in the discussed methods which is an important parameter [14].…”

Section: Introductionmentioning

confidence: 99%

Illumination Control of Smart Indoor Lighting Systems Consists of Multiple Zones

Seyedolhosseini

Masoumi

Modarressi

et al. 2018

2018 Smart Grid Conference (SGC)

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Accurate and power efficient determination of luminaires dimming level is a challenging issue in smart indoor lighting systems, since the lighting system is nonlinear and time variant. In this paper, a smart and power efficient control method is developed in order to determine luminaires dimming level in an indoor environment with multiple work zones. A positive point of the proposed control method is that photodetectors are placed at the work zones which increase the accuracy. Besides, different number of photodetectors can be placed at work zones in the proposed control method, since work zones may have different dimensions and also accuracy levels may differ. The control method takes the advantages of learning method to avoid complexity and also increase system reliability. The system can properly work with daylight variation during the daytime. Case studies are implemented in DIALux and the control method is evaluated in MATLAB. It is shown that the error for static condition is below 1% and for dynamic condition which daylight varies during daytime is increases to 5.6%.

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Performance Improvement of ZigBee Networks in Coexistence of Wi-Fi Signals

Cited by 4 publications

References 12 publications

Mitigação de Interferência entre Tecnologias Sem Fio em Ambientes Inteligentes

Mitigação de Interferência entre Tecnologias Sem Fio em Ambientes Inteligentes

Daylight adaptive smart indoor lighting control method using artificial neural networks

Illumination Control of Smart Indoor Lighting Systems Consists of Multiple Zones

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