Evaluation of different signal propagation models for a mixed indoor-outdoor scenario using empirical data

Artemenko, Oleksandr; Rubina, Alina; Nayak, Adarsh Harishchandra; Menezes, Sanjeeth Baptist; Mitschele‐Thiel, Andreas

doi:10.4108/eai.20-6-2016.151519

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…RMSPE improvements due to recalibration emerged as 0.8% at 900MHz and about 7% at 2GHz; as against 16% at 2.5GHz and close to 60% at 3.5GHz. These metrics are reflected by the predicted pathloss profiles of Figure 7, as well as Figures (8) and ( 9) for the histogram and normal probability plots, respectively. 7.…”

Section:   mentioning

confidence: 81%

“…[7], based on the outcomes of a dedicated measurement campaign, suggested that the performances of the various multi-wall models should significantly improve if reflection effects are accurately taken into consideration. As implicitly suggested by [7], the multi-wall model is also applicable for modelling of indoor-to-outdoor pathloss; and the outcome of investigations by [8] clearly supp.ort that suggestion.…”

Section: Introductionmentioning

confidence: 89%

“…Thereafter, a 'conditioned pathloss vector' is defined as ( [11]) (8) and the calibration coefficients consequently determined as (9) provided that the inner product quantities appearing in Eqns. ( 7 For the purposes of the comparative evaluation of SVD and QMM as pathloss model calibration tools, four basic models are selected as candidates for calibration, using the indoor-to-outdoor measurement data available, through the use of the commercial software 'GETDATA', from Figures 2(a), 2(b), 3(a) and 3(b) of [10].…”

Section: 2mentioning

confidence: 99%

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Singular Value Decomposition and Quasi-Moment-Method as pathloss model calibration alternatives

Muhammed¹,

Ayorinde²,

Okewole³

et al. 2022

Nig. J. Tech.

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Using indoor-to-outdoor pathloss measurements for a femtocell network, this paper presents a comparative evaluation of the performances of Singular Value Decomposition (SVD) and Quasi-Moment-Method (QMM) as pathloss model calibration tools. First, the performances of two published SVD models are compared with those of corresponding QMM models, developed through the calibration of basic ECC33 and WINNER II models. Then, and after noting that the ‘base models’ from which the poorer performing, published SVD calibrations reportedly derive, are either incompletely described or characterized by misprints, alternative ‘base models’ are prescribed by this paper. It is then shown through analysis that QMM and SVD represent alternative implementations of the same basic model calibration algorithm. Computational results due to the alternative models suggest that better performance metrics (Mean Prediction Error (MPE) and Root Mean Square Prediction Error (RMSPE)) are recorded, when existing basic models are modified to mimic the SVD ‘base model’, prior to SVD/QMM calibration. Indeed, because the MPE due to the calibration of the alternative models are all close to zero (actually equal to zero in a few cases), the associated residual profiles closely follow the Gaussian distribution typically assumed in the literature, for shadow fading modelling.

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Section:   mentioning

confidence: 81%

Section: Introductionmentioning

confidence: 89%

Section: 2mentioning

confidence: 99%

See 1 more Smart Citation

Singular Value Decomposition and Quasi-Moment-Method as pathloss model calibration alternatives

Muhammed¹,

Ayorinde²,

Okewole³

et al. 2022

Nig. J. Tech.

View full text Add to dashboard Cite

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“…Therefore, FSPL losses are usually summed to calculate path losses [ 15 ]. Equation (7) describes the losses in free space, where the frequency ( f ) is expressed in MHz and the distance from the receiving antenna to the transmitting antenna d is given in kilometers [ 59 ]:

…”

Section: Technical Analysismentioning

confidence: 99%

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review

Barrios-Ulloa

Ariza-Colpas

Sánchez-Moreno

et al. 2022

Sensors

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The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

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“…The path loss exponent h is generally set within ½1:6, 1:8 under LOS or ½4, 6 under NLOS; d ij is the Euclidean distance between the ith grid and information source (lost person) at the jth cycle, d 0 = 1 m is a reference distance for Bluetooth, and s a is the Gaussian random noise. 36 In order to better depict the attenuation of RSSI value during radio signal propagation, the NM is introduced. Thus, the correlation between EM and SSM becomes…”

Section: Definitionmentioning

confidence: 99%

Sparse sensing data–based participant selection for people finding

Tian

Tang

2019

International Journal of Distributed Sensor Networks

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With the emerging of participatory sensing, crowdsensing-based lost people finding is arising. As a special locationcentric task, participant selection is a key factor to determine success or failure of lost people finding result. Besides traditional influence, like Quality of Information contribution, candidate's spatial proximity to the lost people is crucial in participant selection procedure. In order to evaluate how possible a candidate can approach lost people, the probability distribution of their tracing points should be predicted. However, the sparse sensing data problem has been a bottleneck of estimating people's probable position. To overcome these issues, we study the problem of selecting optimal participants according to each candidate's spatial proximity and Quality of Information contribution. In the first procedure, we proposed a Received Signal Strength Indicator Positioning-Determined Compressive Sensing algorithm to interpolate missing Received Signal Strength Indicator data. Then, a location-important marking method is put forward to select a set of high-quality data for estimating missing people's location. In the third procedure, a double-condition greedy participant selection approach, which guarantees candidates' spatial proximity and Quality of Information contribution, is executed to select optimal participants. Simulation results demonstrate that the proposed mechanism outperforms the other algorithms both in accuracy of positioning and quality of uploaded sensing data.

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Evaluation of different signal propagation models for a mixed indoor-outdoor scenario using empirical data

Cited by 3 publications

References 10 publications

Singular Value Decomposition and Quasi-Moment-Method as pathloss model calibration alternatives

Singular Value Decomposition and Quasi-Moment-Method as pathloss model calibration alternatives

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review

Sparse sensing data–based participant selection for people finding

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