Localization Improvement in Wireless Sensor Networks Using a New Statistical Channel Model

Amir Karimi Alavijeh, Hossein Ramezani, Ali Karimi Alavijeh

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

In this paper, a statistical channel model is proposed based on the second moment of Received Signal Strength Indicator (RSSI) in an outdoor communication channel. The medium under study is a grass field where the RSSI data are collected in different distances and orientations using a set of in-house built transmitter-receiver sensors. The validity of the constructed sensors is confirmed since the first moment of RSSI data follows the well-known Friis model. The proposed model presents an additional relationship between the variance of RSSI data and distance. To demonstrate the application of this statistical relationship, we have investigated the localization problem of a hidden node using extended Kalman filter (EKF). Compared to the conventional EKF in which the covariance matrix of measurement noise is fixed, this matrix can be updated online using the proposed model. The experimental and simulation results of two different scenarios, which are fixed hidden node and mobile hidden node, show that the proposed model improves the accuracy of RSSI localization from 10 to 22 percent in different situations.
OriginalsprogEngelsk
TidsskriftSensors and Actuators A: Physical
Vol/bind271
Sider (fra-til)283-289
ISSN0924-4247
DOI
StatusUdgivet - 2018

Fingeraftryk

Wireless sensor networks
sensors
Extended Kalman filters
Kalman filters
moments
grasses
Sensors
transmitter receivers
noise measurement
Covariance matrix
Transceivers
communication
matrices
simulation

Citer dette

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title = "Localization Improvement in Wireless Sensor Networks Using a New Statistical Channel Model",
abstract = "In this paper, a statistical channel model is proposed based on the second moment of Received Signal Strength Indicator (RSSI) in an outdoor communication channel. The medium under study is a grass field where the RSSI data are collected in different distances and orientations using a set of in-house built transmitter-receiver sensors. The validity of the constructed sensors is confirmed since the first moment of RSSI data follows the well-known Friis model. The proposed model presents an additional relationship between the variance of RSSI data and distance. To demonstrate the application of this statistical relationship, we have investigated the localization problem of a hidden node using extended Kalman filter (EKF). Compared to the conventional EKF in which the covariance matrix of measurement noise is fixed, this matrix can be updated online using the proposed model. The experimental and simulation results of two different scenarios, which are fixed hidden node and mobile hidden node, show that the proposed model improves the accuracy of RSSI localization from 10 to 22 percent in different situations.",
keywords = "Localization; Channel Modeling; Measuremnet noise; Wireless sensor network; Extended Kalman Filters",
author = "{Karimi Alavijeh}, Amir and Hossein Ramezani and {Karimi Alavijeh}, Ali",
year = "2018",
doi = "10.1016/j.sna.2018.01.015",
language = "English",
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pages = "283--289",
journal = "Sensors and Actuators A: Physical",
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Localization Improvement in Wireless Sensor Networks Using a New Statistical Channel Model. / Karimi Alavijeh, Amir; Ramezani, Hossein; Karimi Alavijeh, Ali.

I: Sensors and Actuators A: Physical, Bind 271, 2018, s. 283-289.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Localization Improvement in Wireless Sensor Networks Using a New Statistical Channel Model

AU - Karimi Alavijeh, Amir

AU - Ramezani, Hossein

AU - Karimi Alavijeh, Ali

PY - 2018

Y1 - 2018

N2 - In this paper, a statistical channel model is proposed based on the second moment of Received Signal Strength Indicator (RSSI) in an outdoor communication channel. The medium under study is a grass field where the RSSI data are collected in different distances and orientations using a set of in-house built transmitter-receiver sensors. The validity of the constructed sensors is confirmed since the first moment of RSSI data follows the well-known Friis model. The proposed model presents an additional relationship between the variance of RSSI data and distance. To demonstrate the application of this statistical relationship, we have investigated the localization problem of a hidden node using extended Kalman filter (EKF). Compared to the conventional EKF in which the covariance matrix of measurement noise is fixed, this matrix can be updated online using the proposed model. The experimental and simulation results of two different scenarios, which are fixed hidden node and mobile hidden node, show that the proposed model improves the accuracy of RSSI localization from 10 to 22 percent in different situations.

AB - In this paper, a statistical channel model is proposed based on the second moment of Received Signal Strength Indicator (RSSI) in an outdoor communication channel. The medium under study is a grass field where the RSSI data are collected in different distances and orientations using a set of in-house built transmitter-receiver sensors. The validity of the constructed sensors is confirmed since the first moment of RSSI data follows the well-known Friis model. The proposed model presents an additional relationship between the variance of RSSI data and distance. To demonstrate the application of this statistical relationship, we have investigated the localization problem of a hidden node using extended Kalman filter (EKF). Compared to the conventional EKF in which the covariance matrix of measurement noise is fixed, this matrix can be updated online using the proposed model. The experimental and simulation results of two different scenarios, which are fixed hidden node and mobile hidden node, show that the proposed model improves the accuracy of RSSI localization from 10 to 22 percent in different situations.

KW - Localization; Channel Modeling; Measuremnet noise; Wireless sensor network; Extended Kalman Filters

U2 - 10.1016/j.sna.2018.01.015

DO - 10.1016/j.sna.2018.01.015

M3 - Journal article

VL - 271

SP - 283

EP - 289

JO - Sensors and Actuators A: Physical

JF - Sensors and Actuators A: Physical

SN - 0924-4247

ER -