Multiplicative based path loss model

Bilgehan, Bülent; Ojo, Stephen

doi:10.1002/dac.3794

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…It is important to note that the views of [99][100][101][102][103] about various connectivity issues can affect the density and the overall evaluation of a wardriving result based on the variability of several issues highlighted in this paper. e wardriving approach is used in this work to crawl wider regions for examination [104][105][106].…”

Section: Survey Resultsmentioning

confidence: 99%

“…Because of variable connectivity issues, mobile AP devices (MiFi) may be ON and OFF a wardriving radar, resulting in inconsistent results and datasets at each run. e views expressed in the works [99][100][101][102][103] on various connectivity issues can affect the density and the overall evaluation of a wardriving result based on the variability of the above problems. However, current research endeavour using Artificial Intelligence (AI) and Machine Learning is addressing these issues [110][111][112][113].…”

Section: Limitations Of the Study Some Identified Limitations Of This...mentioning

confidence: 99%

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Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

Etta

Imoize

Shukla

et al. 2022

Mobile Information Systems

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This study aims to investigate Wireless Local Area Network (WLAN) within the context of its applicability as a 21st-century business tool and its survivability in a security threat-infested cyber landscape. WLAN security leverages the Wardriving technique deployed within geolocation to scan for WLAN density and explore the associated security mechanisms. Specifically, the study adopts two approaches; the first part reviews relevant research articles in electronic libraries and databases on WLAN security based on wardriving techniques. The other part comprises a measurement campaign conducted in a mid-sized city in North Cyprus. The field measurement aims to underscore the claims from the literature to find out how the security encryption technologies are used. In particular, the goal is to determine the availability of WLAN infrastructure and monitor how the security measures are implemented in Northern Cyprus. The main objective is to determine the security state of WLAN in Cyprus and examine how it can be generalized for related environments. In order to completely grasp the research issue posed in this study, data analyses from several perspectives are analyzed and examined critically. The wardriving approach has been used in this work to crawl wider regions for examination. This study was conducted with security findings drawn only from publicly accessible information emitted by each investigated wireless access point. The channel usage, Service Set IDentifier (SSID) security, the Encryption type (Open, WEP, WPA, WPA2, WPA3, and Mixed mode), WPS usage statistics, geographical locations, detailed security statistics described in Wigle CSV format, and vendor statistics are highlighted. Generally, results indicate that 21,345 WLANs were detected. From the detected WLANs, 23 (0.1 percent) used WEP encryption, 18 (0.08 percent) used WPA-TKIP encryption, 5,359 (25.1 percent) were unencrypted, and a clear majority of 9,139 (42.82 percent) used the more secure WPA2 encryption, while 13 networks (0.06 percent) used the latest WPA3 encryption technique. The results imply that WLAN security in Cyprus can be said to be moderate. Thus, this study adds to the expanding corpus of research on WLAN security and Wardriving to all parties in the wireless security ecosystem. The current study examines WLAN operations in North Cyprus while pointing to future research directions on Wireless LAN security mechanisms. Overall, the dataset from the wardriving experiment is novel and would serve future research exploration in the wireless security systems domain.

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Section: Survey Resultsmentioning

confidence: 99%

Section: Limitations Of the Study Some Identified Limitations Of This...mentioning

confidence: 99%

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

Etta

Imoize

Shukla

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

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“…To provide solutions to the challenges associated with analytical models, machine learning algorithms were introduced in other studies. [23][24][25] Machine learning in path loss predictions involves using large, robust datasets and flexible network architecture in solving a supervised regression problem. 26 Supervised learning is used with labeled data where a particular input is correspondingly mapped to its target output.…”

Section: Introductionmentioning

confidence: 99%

“…These models produce a high accuracy but require a significant amount of time and considerable computational complexity. To provide solutions to the challenges associated with analytical models, machine learning algorithms were introduced in other studies 23–25 . Machine learning in path loss predictions involves using large, robust datasets and flexible network architecture in solving a supervised regression problem 26 .…”

Section: Introductionmentioning

confidence: 99%

An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Ojo

Akkaya

Sopuru

2022

Int J Communication

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Summary Accurate path loss prediction models are indispensable in modern wireless communication systems. In recent times, several path loss prediction models have been proposed to improve network performance. However, most of these models have not addressed the fundamental issues. The problem of deploying a single path loss prediction model that fits well in all wireless propagation environments remains. To address this problem, we present machine learning‐based ensemble methods to path loss predictions. Specifically, ensemble methods have been introduced to improve signal prediction accuracy and performance. Additionally, the radial basis function (RBF) and multilayer perceptron (MLP) neural network models were deployed and improved by adding more network parameters to their respective input layers. Results revealed that the RBF model with an increase in the number of centroids reduces the mean square error (MSE). Also, the Gaussian kernel function gives lower MSE compared to the multiquadric and inverse multiquadric functions. The bagging and blending ensemble path loss prediction models were introduced for optimal performance. An RBF neural network of different clusters was incorporated into the bagging algorithm as base learners. The RBF, MLP, blending, and bagging were examined using standard metrics for the training, testing, and validation dataset for model validation. The developed bagging ensemble path loss prediction model gave the lowest errors (MSE = 0.0011 dB, SSE = 0.6069 dB, MAE = 0.0245 dB, & R = 0.7484 dB) for the datasets. The bagging ensemble method acts as a variance and error reduction mechanism because it predicted path loss closest to measured data and is suitable for near precise path loss predictions.

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“…Multiplicative‐based path loss model was developed in Bilgehan and Ojo 17 through extensive measurements across six base stations in north of Cyprus at 2100 MHz. The model was formulated from geometric calculus, and the parameters were derived from field measurements.…”

Section: Introductionmentioning

confidence: 99%

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Ojo

Imoize

Alienyi³

2020

Int J Communication

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Summary Path loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, this paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

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Multiplicative based path loss model

Cited by 13 publications

References 20 publications

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

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