An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

Isokawa, Teijiro; Motomura, Shun; Ohtsuka, Junya; Kawa, Hironobu; Nakashima, Satoshi; Kamiura, Naotake; Matsui, Nobuyuki

doi:10.1155/2013/356231

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Variations and the behavior of power loss in the propagation of electromagnetic signals were observed. Two techniques presented in the literature have been tested to express, in indoor environments, the RSSI behavior as a function of distance: the quadratic approximation technique used in [ 32 , 33 ] and the log-normal shadowing technique, also called log-normal propagation loss, used in [ 17 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks

Ferreira

Souza

Carvalho

2020

Sensors

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Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.

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Section: Methodsmentioning

confidence: 99%

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks

Ferreira

Souza

Carvalho

2020

Sensors

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“…No trabalho de [2] foi proposto um esquema livre de âncoras. Esse esquema emprega o LQI e a contagem de saltos entre os nós sensores para a estimativa de localização.…”

Section: Revisão Da Literaturaunclassified

“…Conforme a Figura 5, linha 22, os valores de RSSI lidos dos quadros são enviados via interface serial para o computador equipado com software MATLAB, onde foi codificado o filtro de Kalman para uma única variável de RSSI. O filtro de Kalman foi aplicado nos trabalhos de [2] e [6] para tratar as medições de RSSI contaminadas por ruídos e outras interferências. A conexão do Arduino com o MATLAB é baseada na comunicação serial UART (Universal Asynchronous Receiver/Transmitter) utilizando interface USB (Universal Serial Bus) configurada com taxa de transferência de dados definida em 9600bits/s.…”

Section: Iv1 Configuração Do Esquema De Localizaçãounclassified

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Localization in Indoor Environments Using Virtualized Three-Dimensional Matrix

Ferreira¹,

Carvalho²

2019

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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The automotive air conditioning systems always used the concept of thermodynamic steam compression cycle. Even after a lot of research, some issues still remain unresolved, such as proper control of the inside temperature of the vehicle in warmer regions, such as in northern Brazil. Focusing on this issue, this article has proposed a new method to improve the control of internal temperature. The proposed method used the same concept of forced ventilation to improve heat exchange in the condenser, using an electro air blower and water from a reservoir. The proposed system was based on the theory of forced convection heat exchange. The results of the data presented showed that the survey was validated due to the drop of temperature of 3 degrees compared with the current system. In addition, the system pressure originally from 230 psi decreased to 150 psi.

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“…Na Figura 3, testaram-se duas técnicas teóricas da literatura a fim de verificar a relação entre distância e RSSI: perda de propagação log-normal [12][13][14] e aproximação quadrática [15][16][17][18]. Na mesma figura, apresenta-se a média aritmética de 20 leituras de RSSI dos APs 1 a 8.…”

Section: Conforme Figura 1 Configurou-se 8 Módulosunclassified

Localização em Ambientes Internos Utilizando Redes Wi-Fi

Ferreira¹,

Carvalho²,

Mota³

2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Este artigo apresenta um método de localização para ambientes internos capaz de melhorar a precisão de localização que é comprometida pela instabilidade do RSSI das redes IEEE 802.11. O método emprega o algoritmo k-Nearest Neighbors (kNN) e a análise de quartis para representar leituras de RSSI provenientes de APs (Access Points) de referência. Verificou-se que a proposta apresenta erro de localização nulo ao utilizar quatro APs e 10 leituras de RSSI de cada AP com apenas 0,69 segundo para localizar. Estes resultados são contribuições importantes, o que assegura que o método é promissor para localizar objetos em ambientes internos.

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An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

Cited by 6 publications

References 13 publications

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks

Localization in Indoor Environments Using Virtualized Three-Dimensional Matrix

Localização em Ambientes Internos Utilizando Redes Wi-Fi

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