Fingerprint-Based Localization Approach for WSN Using Machine Learning Models

Alhmiedat, Tareq

doi:10.3390/app13053037

Cited by 18 publications

(18 citation statements)

References 33 publications

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“…In general, CNN architecture requires a large dataset for training purposes in order to achieve better classification performance, which is not valid in the area of plant disease classification. In addition, when the number of model parameters exceeds the number of data samples, the small training dataset causes overfitting, and hence minimize the classification accuracy 32 .…”

Section: Discussionmentioning

confidence: 99%

An improved pear disease classification approach using cycle generative adversarial network

Alshammari,

Alshammari

et al. 2024

Sci Rep

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A large number of countries worldwide depend on the agriculture, as agriculture can assist in reducing poverty, raising the country’s income, and improving the food security. However, the plan diseases usually affect food crops and hence play a significant role in the annual yield and economic losses in the agricultural sector. In general, plant diseases have historically been identified by humans using their eyes, where this approach is often inexact, time-consuming, and exhausting. Recently, the employment of machine learning and deep learning approaches have significantly improved the classification and recognition accuracy for several applications. Despite the CNN models offer high accuracy for plant disease detection and classification, however, the limited available data for training the CNN model affects seriously the classification accuracy. Therefore, in this paper, we designed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets. In addition, we developed an efficient plant disease classification approach, where we adopt the CycleGAN architecture in order to enhance the classification accuracy. The obtained results showed an average enhancement of 7% in the classification accuracy.

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Section: Discussionmentioning

confidence: 99%

An improved pear disease classification approach using cycle generative adversarial network

Alshammari,

Alshammari

et al. 2024

Sci Rep

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“…Therefore, the results presented in [ 16 ] may be affected by the use of an inaccurate path loss model that differs from actual signal attenuation in a real environment. In comparison, [ 17 ] should not have such flaws, since it was based on measurements of signal levels from Xbee modems in a real indoor environment. The authors did not specify in which ISM band the modems operated, but four neural network structures were examined: linear regression, random forest, K-nearest neighbor and decision tree, and the article includes a comparison of their position estimation accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

Sadowski,

Stefanski

2024

Sensors

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This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

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“…They concluded that the proposed algorithm had two advantages over traditional tracking methods: The first was refining the sampling method by thus reducing the frequency of the sampling. The second reason was the dynamic updating of the fingerprints [ 5 , 12 , 49 ]. Qian et al [ 20 ] proposed using CSI to detect moving targets, and their results indicated that in comparison to the previous approaches, their proposed approach is more robust and more accurate.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Usually, the radio transmitter (TX) broadcasts a signal within a network, and the target reflects the propagating signal [ 9 , 10 , 11 ]. When the signal is received by the receiver (RX), it can be used to estimate a target’s location [ 12 , 13 , 14 , 15 ]. Different techniques can be used for target tracking, which include the angle of arrival (AOA), received signal strength indicators (RSSI), the time-of-arrival (TOA), as well as the time difference of arrival (TDOA) [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

Nkabiti

Chen

2023

Sensors

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Recent advancements in target tracking using Wi-Fi signals and channel state information (CSI) have significantly improved the accuracy and efficiency of tracking mobile targets. However, there remains a gap in developing a comprehensive approach that combines CSI, an unscented Kalman filter (UKF), and a sole self-attention mechanism to accurately estimate the position, velocity, and acceleration of targets in real-time. Furthermore, optimizing the computational efficiency of such approaches is necessary for their applicability in resource-constrained environments. To bridge this gap, this research study proposes a novel approach that addresses these challenges. The approach leverages CSI data collected from commodity Wi-Fi devices and incorporates a combination of the UKF and a sole self-attention mechanism. By fusing these elements, the proposed model provides instantaneous and precise estimates of the target’s position while considering factors such as acceleration and network information. The effectiveness of the proposed approach is demonstrated through extensive experiments conducted in a controlled test bed environment. The results exhibit a remarkable tracking accuracy level of 97%, affirming the model’s ability to successfully track mobile targets. The achieved accuracy showcases the potential of the proposed approach for applications in human-computer interactions, surveillance, and security.

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Fingerprint-Based Localization Approach for WSN Using Machine Learning Models

Cited by 18 publications

References 33 publications

An improved pear disease classification approach using cycle generative adversarial network

An improved pear disease classification approach using cycle generative adversarial network

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

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