scholarly journals Estimation of the Path-Loss Exponent by Bayesian Filtering Method

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1934
Author(s):  
Piotr Wojcicki ◽  
Tomasz Zientarski ◽  
Malgorzata Charytanowicz ◽  
Edyta Lukasik
Keyword(s):  
Fundamental Problem ◽  
Path Loss ◽  
Signal Strength ◽  
Propagation Model ◽  
Bayesian Filtering ◽  
Network Parameter ◽  
Path Loss Exponent ◽  
Dynamic Estimation ◽  
The Stability ◽  
Log Normal

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.

scholarly journals Characterization of the Log-Normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks

2020 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
Author(s):  
José Vallet García
Keyword(s):  
Empirical Studies ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Model Parameters ◽  
Normal Model ◽  
Path Loss Exponent ◽  
Localization Algorithms ◽  
Log Normal

Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model.

scholarly journals MEASUREMENTSBASEDEVALUATION OF PATHLOSSEXPONENTS IN URBAN OUTDOORENVIRONMENTS

2021 ◽  
Vol 9 (03) ◽  
pp. 72-79
Author(s):  
Akohoule Alex ◽  
◽  
Bamba Aliou ◽  
Kamagate Aladji ◽  
Konate Adama ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Average Power ◽  
Path Loss ◽  
Wireless Channel ◽  
Propagation Model ◽  
Strong Impact ◽  
Efficient Design ◽  
Path Loss Exponent ◽  
Outdoor Environments ◽  
The Impact

In wireless networks, propagation models are used to assess the received power signal and estimate the propagation channel. These models depend on the pathloss exponent (PLE) which is one of the main parameters to characterize the propagation environment. Indeed, in the wireless channel, the path loss exponent has a strong impact on the quality of the links and must therefore be estimated with precision for an efficient design and operation of the wireless network. This paper addresses the issue of path loss exponents estimation for mobile networks in four outdoor environments. This study is based on measurements carried out in four outdoor environments at the frequency of 2600 MHz within a bandwidth of 70 MHz. It evaluates the path loss exponent, and the impact of obstacles present in the environments. The parameters of the propagation model determined from the measurements show that the average power of the received signal decreases logarithmically with the distance. We obtained path loss exponents values of 4.8, 3.53, 3.6 and 3.99 for the site 1, site 2, site 3 and site 4, respectively. Clearly the density of the obstacles has an impact on the path loss exponents and our study shows that the received signal decrease faster as the transmitter and receiver separation in the dense environments.

scholarly journals Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6582
Author(s):  
SeYoung Kang ◽  
TaeHyun Kim ◽  
WonZoo Chung
Keyword(s):  
Kalman Filter ◽  
Path Loss ◽  
Target Position ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Target Localization ◽  
Wireless Sensor ◽  
Transmit Power ◽  
Path Loss Exponent ◽  
Localization Algorithms

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

scholarly journals Factorial Design Analysis for Localization Algorithms

2018 ◽  
Vol 8 (12) ◽  
pp. 2654
Author(s):  
Joaquin Mass-Sanchez ◽  
Erica Ruiz-Ibarra ◽  
Ana Gonzalez-Sanchez ◽  
Adolfo Espinoza-Ruiz ◽  
Joaquin Cortez-Gonzalez
Keyword(s):  
Statistical Analysis ◽  
Noise Level ◽  
Performance Metrics ◽  
Extreme Values ◽  
Path Loss ◽  
Propagation Model ◽  
Path Loss Exponent ◽  
Localization Algorithms ◽  
Accuracy And Precision ◽  
The Impact

Localization is a fundamental problem in Wireless Sensor Networks, as it provides useful information regarding the detection of an event. There are different localization algorithms applied in single-hop or multi-hop networks; in both cases their performance depends on several factors involved in the evaluation scenario such as node density, the number of reference nodes and the log-normal shadowing propagation model, determined by the path-loss exponent (η) and the noise level (σdB) which impact on the accuracy and precision performance metrics of localization techniques. In this paper, we present a statistical analysis based on the 2k factorial methodology to determine the key factors affecting the performance metrics of localization techniques in a single-hop network to concentrate on such parameters, thus reducing the amount of simulation time required. For this proposal, MATLAB simulations are carried out in different scenarios, i.e., extreme values are used for each of the factors of interest and the impact of the interaction among them in the performance metrics is observed. The simulation results show that the path-loss exponent (η) and noise level (σdB) factors have the greatest impact on the accuracy and precision metrics evaluated in this study. Based on this statistical analysis, we recommend estimating the propagation model as close to reality as possible to consider it in the design of new localization techniques and thus improve their accuracy and precision metrics.

scholarly journals Determination of an Outdoor Path Loss Model and Signal Penetration Level in Some Selected Modern Residential and Office Apartments in Ogbomosho, Oyo State, Nigeria

2018 ◽  
pp. 1-25
Author(s):  
V. O. A. Akpaida ◽  
F. I. Anyasi ◽  
S. I. Uzairue ◽  
A. I. Idim
Keyword(s):  
Building Materials ◽  
Path Loss ◽  
Signal Strength ◽  
Base Stations ◽  
Propagation Path ◽  
Loss Model ◽  
Path Loss Model ◽  
Path Loss Exponent ◽  
Received Power

This article involves the site specific determination of an outdoor path loss model and Signal penetration level in some selected modern residential and office apartments in Ogbomosho, Oyo State. Measurements of signal strength and its associated location parameters referenced globally were carried out. Propagation path loss characteristics of Ogbomosho were investigated using three different locations with distinctively different yet modern building materials. Consequently, received signal strength (RSS) was measured at a distance d in meters, from appropriate base stations for various environments investigated. The data were analyzed to determine the propagation path loss exponent, signal penetration level and path loss characteristics. From calculations, the average building penetration losses were, 5.93dBm, 6.40dBm and 6.1dBm outside the hollow blocks B1, solid blocks B2 and hollow blocks mixed with pre cast asbestos B3, buildings respectively with a corresponding path loss exponent values of, 3.77, 3.80 and 3.63. Models were developed and validated, and used to predict the received power inside specific buildings. Moreover, the propagation models developed for the different building types can be used to predict the respective signal level within the building types, once the transmitter – receiver distance is known. The readings obtained from the developed models were compared with both the measured values and values computed using some existing models with satisfactory results obtained.

scholarly journals Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3024 ◽  
Author(s):  
Álvarez ◽  
Díez-González ◽  
Alonso ◽  
Fernández-Robles ◽  
Castejón-Limas ◽  
...  
Keyword(s):  
Path Loss ◽  
Position Estimation ◽  
Propagation Model ◽  
Time Difference ◽  
Time Difference Of Arrival ◽  
Wide Range ◽  
Multiple Sensor ◽  
Time Determination ◽  
Log Normal ◽  
First Time

The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain.

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