A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Yadav, Preeti; Sharma, Subhash C.

doi:10.1002/dac.5397

Cited by 19 publications

(6 citation statements)

References 119 publications

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“…The data grouping is achieved through deep learning, a particular form of ANN technology. ANNs employing deep learning involve a variety of representations between the input layer and the output layer that explains how data is collected during the learning process [71]. The composition consists of simple, non-linear elements that improve the representation and ensure the most advantageous result [72].…”

Section: Deep Learningmentioning

confidence: 99%

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Ghadi,

Mazhar,

Shloul

et al. 2024

IEEE Access

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Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. This article focuses on machine learning algorithms used to solve the security issues of wireless sensor networks. This article also focuses on different types of attacks on layers of the wireless sensor network. Moreover, this study addresses several unsolved issues, including adapting machine learning algorithms to accommodate the sensors' functionalities in this network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.

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Section: Deep Learningmentioning

confidence: 99%

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Ghadi,

Mazhar,

Shloul

et al. 2024

IEEE Access

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“…Node localization is one of the critical issues that should be taken into account in Wireless Sensor Networks (WSNs) related to network design and topology which causes faults, low performance, scalability, latency and coverage problems [9].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Supported Nano-Router Localization in WNSNs

Güleç

2023

Sakarya University Journal of Science

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Sensing data from the environment is a basic process for the nano-sensors on the network. This sensitive data need to be transmitted to the base station for data processing. In Wireless Nano-Sensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nano-sensors and transmitting it to the nano-gateways. When the number of nano-routers is not enough on the network, the data need to be transmitted by multi-hop routing. Therefore, there should be more nano-routers placed on the network for efficient direct data transmission to avoid multi-hop routing problems such as high energy consumption and network traffic. In this paper, a machine learning-supported nano-router localization algorithm for WNSNs is proposed. The algorithm aims to predict the number of required nano-routers depending on the network size for the maximum node coverage in order to ensure direct data transmission by estimating the best virtual coordinates of these nano-routers. According to the results, the proposed algorithm successfully places required nano-routers to the best virtual coordinates on the network which increases the node coverage by up to 98.03% on average and provides high accuracy for efficient direct data transmission.

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“…Collaborative projects become dynamic ecosystems of creativity and innovation, as machine learning algorithms match students with complementary skills and interests [9]. Virtual reality environments offer immersive experiences that transcend physical boundaries, enabling students to explore historical settings, dissect complex narratives, and engage with diverse perspectives [10].In this symbiotic relationship between humans and machines, the English classroom becomes a crucible of exploration and growth, where the boundaries of learning are continually pushed and the horizons of possibility expand with each interaction.…”

Section: Introductionmentioning

confidence: 99%

“…Ouyang et al (2023) explore the integration of artificial intelligence and learning analytics to improve student learning outcomes in online engineering courses, showcasing the potential of data-driven approaches in enhancing educational experiences Yadav and Sharma (2023). conduct a systematic review of localization in wireless sensor networks, highlighting the role of machine learning and optimization-based approaches in improving network performance Shin et al (2022).…”

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confidence: 99%

Optimization of English Classroom Interaction Models Incorporating Machine Learning

Ren

2024

jes

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The optimization of English classroom interaction models incorporating machine learning represents a paradigm shift in language education, offering innovative ways to enhance student engagement, participation, and learning outcomes. With machine learning algorithms, educators can analyze various factors such as student proficiency levels, learning styles, and behavioral patterns to dynamically adapt classroom interactions in real-time. These models can predict and optimize the timing, content, and format of instructional activities, fostering more personalized and effective learning experiences.This paper explores the implementation of optimized interaction models and machine learning predictions in the English classroom to enhance student engagement, academic performance, and personalized instruction. Through the analysis of various tables and findings, key insights emerge regarding the effectiveness of student-centered learning, active learning strategies, differentiated instruction, and technology integration within the interaction model. Additionally, machine learning predictions offer valuable opportunities for personalized instruction based on predicted proficiency levels. The study demonstrates promising outcomes, but limitations such as sample size constraints and data quality issues must be addressed to ensure the reliability and applicability of the findings. Results indicate a 15% increase in student participation rates, a 12-point rise in average test scores, and a shift from high to moderate teacher intervention frequency. Additionally, machine learning predictions achieved an accuracy rate of 80%, with 8 out of 10 correct predictions. Aspect evaluation scores revealed high effectiveness in student-centered learning (9.2), formative assessment (9.3), and teacher facilitation (9.0). These findings contribute to enhancing teaching and learning practices, supporting educators in fostering more engaging and personalized learning experiences for students.

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A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Abstract: In recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of

Cited by 19 publications

References 119 publications

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Machine Learning Supported Nano-Router Localization in WNSNs

Optimization of English Classroom Interaction Models Incorporating Machine Learning

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