Path Loss Model for a Wireless Sensor Network in Different Weather Conditions

Czerwiński, Dariusz; Przyłucki, Sławomir; Wójcicki, Piotr; Sitkiewicz, Jaroslaw

doi:10.1007/978-3-319-59767-6_9

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The LNSM model is the most universal of the three and its usefulness has been confirmed for both indoor and outdoor environments [8,10]. In addition, it gives promising results in changing weather or environmental conditions [12][13][14].…”

Section: Introductionmentioning

confidence: 93%

“…An equally important factor, apart from those mentioned above, is the influence of weather conditions. Especially, in outdoor environment, the path loss exponent values tend to change over a long period of time because of seasonal reasons [7,13]. It is also worth emphasising that it is not guaranteed that all anchor nodes radiate in the same manner.…”

Section: Path-loss Exponent Estimation Using Bayesian Filteringmentioning

confidence: 99%

“…Extensive experimental research on the impact of changes in the environment as well as factors influencing the RSSI signal in the time domain and frequency domain are presented in detail in [16]. In turn, the importance of weather conditions, including evaluation of RSSI signal sensitivity to changes in temperature and humidity in the outdoor environment, is the subject of the experimental studies presented in [13]. Analogous research for the outdoor scenario but for the estimation of relatively large distances based on RSSI signal measurements is presented in [8].…”

Section: Introductionmentioning

confidence: 99%

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Application of Particle Filter in Path-loss Modelling

Wójcicki,

Zientarski,

Przyłucki

2023

Adv. Sci. Technol. Res. J.

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The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

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Section: Introductionmentioning

confidence: 93%

Section: Path-loss Exponent Estimation Using Bayesian Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Particle Filter in Path-loss Modelling

Wójcicki,

Zientarski,

Przyłucki

2023

Adv. Sci. Technol. Res. J.

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“…One of the primary uses are attempts to locate an object (device) on the basis of received signal strength indicator (RSSI). Many papers describe the use of RSSI for indoor location [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] and outdoor location [ 8 , 9 , 10 , 11 , 12 , 13 ]. The main reason for these applications is the wide availability and affordability of devices using the received signal strength indicator.…”

Section: Introductionmentioning

confidence: 99%

“…Optimising RSSI measurements is necessary due to the high noise caused by obstacles and sudden signal fading. Additionally, a change in the environmental parameters (temperature, humidity) can affect the indicator without altering the position of the transmitters [ 11 , 36 , 37 ]. In most works, the impact of precipitation is usually neglected.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Path-Loss Exponent by Bayesian Filtering Method

Wójcicki

Zientarski

Charytanowicz

et al. 2021

Sensors

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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.

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