Machine learning for coverage optimization in wireless sensor networks: a comprehensive review

Egwuche, Ojonukpe S.; Singh, Abhilash; Ezugwu, Absalom E.; Greeff, Japie; Olusanya, Micheal O.; Abualigah, Laith

doi:10.1007/s10479-023-05657-z

Cited by 10 publications

(4 citation statements)

References 161 publications

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“…These topics include critical factors determining the application requirements, energy efficiency, cost, and overall performance of WSNs. In their study, Egwuche et al reflected the increase in efforts to improve WSN performance and efficiency by performing bibliometric analysis [4].…”

Section: Related Workmentioning

confidence: 99%

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Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Birtane,

Sahingoz,

Korkmaz

2024

IEEE Access

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Over the past few years, there has been a significant emphasis on improving the capabilities of Wireless Sensor Networks (WSNs) by making advancements in communication protocols, energy efficiency, optimal deployment, data analytics, and integration with emerging technologies such as the Internet of Things and artificial intelligence. The deployment of WSN nodes can greatly enhance the effectiveness, scalability, and capability of different systems, resulting in cost reductions, enhanced performance, and improved safety in which the deployment of WSN involves determining the best positioning of sensor nodes to attain maximum coverage and connectivity while minimizing the number of nodes needed. WSNs often face challenges in deploying nodes effectively and Genetic Algorithms (GAs) offer a valuable approach for tackling this problem due to their ability to efficiently search large and complex solution spaces, such as those of complex network design, taking into account various constraints and objectives, which are common characteristics of real-world WSN deployment scenarios. The objective of this study is to use a new method, called the vibrational genetic algorithm, which can be used to optimize the placement of sensor nodes more efficiently. Apart from the other research, it is preferred to use heterogeneous sensor nodes to increase the coverage rate in an irregularly shaped area. The results of the experiments demonstrate that the proposed model offers an effective solution for achieving maximum coverage in application theaters that are more realistic and complex.

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Section: Related Workmentioning

confidence: 99%

“…Egwuche et al conducted a comprehensive review, emphasizing WSN coverage optimization using machine learning and nature-inspired algorithms. Deep learning techniques have been identified as promising for enhancing coverage and network performance [4]. Elhoseny et al tackled the Kcoverage problem, proposing a K-scope model based on GA.…”

Section: Related Workmentioning

confidence: 99%

Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Birtane,

Sahingoz,

Korkmaz

2024

IEEE Access

View full text Add to dashboard Cite

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“…Several time-series models are used in ensemble-based time collection evaluation to produce destiny value projections based on historical data. Anomalies can be found and network performance can be improved with the use of those predictions [6]. It has been demonstrated that ensemble-based time collection analysis is an efficient way to examine and identify irregularities in the information gathered from Wi-Fi sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effectiveness of Ensemble Based Analysis in Wireless Sensor Networks

Hong

2024

JMC

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The usefulness of ensemble-based total time series analysis in Wi-Fi sensor networks is examined in this paper. A device to uses an ensemble approach combines multiple strategies to enhance overall predictive performance. This research assesses various tactics using unique metrics, such as robustness and accuracy. It contrasts the effectiveness of traditional time series methods with ensemble-based total fashions. An experimental approach focusing mostly on exceptional Wi-Fi sensor network scenarios is employed to evaluate the overall effectiveness of the suggested methods. Additionally, this study looks into how changes to community features like energy delivery, conversation range, and node density affect how effective the suggested methods are. The study's findings maintain the capacity to create effective Wi-Fi sensor networks with improved predicted overall performance. The usefulness of ensemble-based time collecting and analysis techniques for wireless sensor networks is investigated in this research. This study primarily looks upon function extraction and seasonality discounting of time series records in WSNs. In this analysis, seasonality is discounted using an ensemble median filter, and feature extraction is accomplished by primary component assessment. To assess the performance of the suggested ensemble technique on every simulated and real-world international WSN fact, multiple experiments are carried out. The findings suggest that the ensemble approach can improve the exceptional time-gathering records within WSNs and reduce seasonality. Furthermore, when compared to single-sensor strategies, the ensemble technique further improves the accuracy of the function extraction system. This work demonstrates the applicability of the ensemble approach for the investigation of time collection data in WSNs

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“…1 reveals that this research report is useful in modeling the impact optimization methods on WSNs. The foremost objective of this research is to investigate the roles and factors that determine the differences in the optimization of WSNs for various tasks [17]. This study utilizes metaheuristic algorithms, which borrow from nature, and explores their advantages.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

Alkanhel,

Khafaga,

Zaki

et al. 2024

CMC

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The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor's specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

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Machine learning for coverage optimization in wireless sensor networks: a comprehensive review

Cited by 10 publications

References 161 publications

Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Analyzing the Effectiveness of Ensemble Based Analysis in Wireless Sensor Networks

Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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