An analytical framework with border effects to estimate the connectivity performance of finite multihop networks in shadowing environments

Nagar, Jaiprakash; Chaturvedi, Sanjay K.; Soh, Sieteng

doi:10.1007/s10586-021-03374-5

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…WSNs is a widely accepted and renowned technology because it is cheap, readily available, and can be installed on the fly in almost no time at any place [ 5 , 6 ]. In addition, WSNs consist of small and homogeneous sensors that work in a de-centralised fashion requiring no pre-installed foundation and communicating over wireless channels [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network

Singh

Amutha

Nagar

et al. 2022

Sensors

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The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms.

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Section: Introductionmentioning

confidence: 99%