scholarly journals The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yisheng Miao ◽  
Huarui Wu ◽  
Lihong Zhang
Keyword(s):  
Large Scale ◽  
Path Loss ◽  
Distance Estimation ◽  
Location Estimation ◽  
Estimation Accuracy ◽  
Loss Model ◽  
Range Measurement ◽  
Path Loss Model ◽  
Wheat Field ◽  
Exponential Decay Model

The range measurement is the premise for location, and the precise range measurement is the assurance of accurate location. Hence, it is essential to know the accurate internode distance. It is noted that the path loss model plays an important role in improving the quality and reliability of ranging accuracy. Therefore, it is necessary to investigate the path loss model in actual propagation environment. Through the analysis of experiments performed at the wheat field, we find that the best fitted parametric exponential decay model (OFPEDM) can achieve a higher distance estimation accuracy and adaptability to environment variations in comparison to the traditional path loss models. Based on the proposed OFPEDM, we perform the RSSI-based location experiments in wheat field. Through simulating the location characteristics in MATLAB, we find that for all the unknown nodes, the location errors range from 0.0004 m to 5.1739 m. The location error in this RSSI-based location algorithm is acceptable in the wide areas such as wheat field. The findings in this research may provide reference for location estimation in large-scale farmland.

scholarly journals A Path Loss and Shadowing Model for Multilink Vehicle-to-Vehicle Channels in Urban Intersections

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4433 ◽  
Author(s):  
Mikael Nilsson ◽  
Carl Gustafson ◽  
Taimoor Abbas ◽  
Fredrik Tufvesson
Keyword(s):  
Large Scale ◽  
Cross Correlation ◽  
Path Loss ◽  
Loss Model ◽  
Correlation Distance ◽  
Path Loss Model ◽  
V2v Communication ◽  
Vehicle To Vehicle ◽  
Auto Correlation ◽  
The Cross

The non line-of-sight (NLOS) scenario in urban intersections is critical in terms of traffic safety—a scenario where Vehicle-to-Vehicle (V2V) communication really can make a difference by enabling communication and detection of vehicles around building corners. A few NLOS V2V channel models exist in the literature but they all have some form of limitation, and therefore further research is need. In this paper, we present an alternative NLOS path loss model based on analysis from measured V2V communication channels at 5.9 GHz between six vehicles in two urban intersections. We analyze the auto-correlation of the large scale fading process and the influence of the path loss model on this. In cases where a proper model for the path loss and the antenna pattern is included, the de-correlation distance for the auto-correlation is as low as 2–4 m, and the cross-correlation for the large scale fading between different links can be neglected. Otherwise, the de-correlation distance has to be much longer and the cross-correlation between the different communication links needs to be considered separately, causing the computational complexity to be unnecessarily large. With these findings, we stress that vehicular ad-hoc network (VANET) simulations should be based on the current geometry, i.e., a proper path loss model should be applied depending on whether the V2V communication is blocked or not by other vehicles or buildings.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

2018 ◽  
pp. 712-722 ◽  
Author(s):  
Huda Ali Hashim ◽  
◽  
Salim Latif Mohammed ◽  
Sadik Kamel Gharghan
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based

scholarly journals Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 134270-134288
Author(s):  
Simon K. Hinga ◽  
Aderemi A. Atayero
Keyword(s):  
Large Scale ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model
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