Empirical Path Loss Models for Wireless Sensor Network Deployments in Short and Tall Natural Grass Environments

2016 ◽  
pp. 1-1 ◽  
Author(s):  
Tajudeen Olasupo ◽  
Carlos E. Otero ◽  
Kehinde O. Olasupo ◽  
Ivica Kostanic
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Path Loss ◽  
Wireless Sensor ◽  
Natural Grass ◽  
Loss Models

scholarly journals PATH LOSS MEASUREMENT AND PREDICTION IN OUTDOOR FRUIT ORCHARD FOR WIRELESS SENSOR NETWORK AT 2.4 GHZ BAND

2019 ◽  
Vol 90 ◽  
pp. 237-252 ◽  
Author(s):  
Tossaporn Srisooksai ◽  
Kamol Kaemarungsi ◽  
Junichi Takada ◽  
Kentaro Saito
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Path Loss ◽  
Wireless Sensor ◽  
Loss Measurement ◽  
Fruit Orchard

scholarly journals Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772269 ◽  
Author(s):  
Alejandro Cama-Pinto ◽  
Gabriel Piñeres-Espitia ◽  
José Caicedo-Ortiz ◽  
Elkin Ramírez-Cerpa ◽  
Leonardo Betancur-Agudelo ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Packet Loss ◽  
Precision Agriculture ◽  
Signal Intensity ◽  
Path Loss ◽  
Field Tests ◽  
Wireless Sensor ◽  
Intensity Level ◽  
Log Normal

Today, through the monitoring of agronomic variables, the wireless sensor networks are playing an increasingly important role in precision agriculture. Among the emerging technologies used to develop prototypes related to wireless sensor network, we find the Arduino platform and XBee radio modules from the DIGI Company. In this article, based on field tests, we conducted a comparative analysis of received strength signal intensity levels, calculation of path loss with “log-normal shadowing” and free-space path loss models. In addition, we measure packet loss for different transmission, distances and environments with respect to an “Arduino Mega” board, and radio modules XBee PRO S1 and XBee Pro S2. The tests for the packet loss and received strength signal intensity level show the best performance for the XBee Pro S2 in the indoor, outdoor, and rural scenarios.

Path Attenuation of Prediction Wireless Sensor Network Signal in Corn Field Based on the Generalized Regression Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 547-550
Author(s):  
He Pan ◽  
Tai Hao Li ◽  
Zhi Gang Liu
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Carrier Frequency ◽  
Path Loss ◽  
Growth Period ◽  
Wireless Sensor ◽  
Generalized Regression Neural Network ◽  
Corn Field ◽  
Generalized Regression

In order to solve the problem whether the wireless sensor network (WSN) nodes are quickly and reasonably arranged in the corn field, this paper proposes the prediction of wireless signal path loss in the corn field on the basis of generalized regression neural network. In this test, this paper takes carrier frequency of 433 MHz and 2.4GHz. According to the features of radio transmission, the corn is divided into three different growth period to measure the path attenuation. Attenuation value is the output Expectation value. Six influenced factors, namely the growth period, the transmitter antenna height, receiver antenna height, antenna gain , the carrier frequency and communication distance, are the input vectors. According to this, the GRNN prediction model is established.

A WSN-Based Localization Scheme Considering the Reliability of RSSI Measurements

2013 ◽  
Vol 679 ◽  
pp. 115-120
Author(s):  
In Whee Joe ◽  
Myung Oh Park
Keyword(s):  
Wireless Sensor Network ◽  
Standard Deviation ◽  
Environmental Factors ◽  
Sensor Network ◽  
Path Loss ◽  
Signal Strength ◽  
Wireless Sensor ◽  
Reference Node ◽  
Localization Scheme ◽  
Received Signal Strength Indication

In this paper, we propose a localization scheme considering the reliability of RSSI (Received Signal Strength Indication) measurements in the WSN (Wireless Sensor Network) environment. This scheme attempts to reduce location errors due to indoor obstacles or environmental factors, when location calculations are based on RSSI. The standard deviation is used to evaluate the reliability of RSSI measurements from the reference node. Also, the directional path loss exponent is calculated through learning with respect to the reference node. The experimental results show that the proposed localization scheme improves the performance significantly in terms of location accuracy, compared to the existing RSSI-based approaches.

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