An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm

Dao, Thi-Kien; Chu, Shu-Chuan; Nguyen, Trong-The; Nguyen, Trinh-Dong; Nguyen, Vinh-Tiep

doi:10.3390/e24081018

Cited by 11 publications

(8 citation statements)

References 43 publications

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“…In this study, to enhance the global exploration capability of the improved algorithm, we replaced Formula (8) in the original algorithm with Formula (14). In other words, when the conditions p < 0.5 and |A| ≥ 1 are satisfied, the algorithm employs Equation ( 14) to update the position of the whales.…”

Section: The Lévy Flight Methodsmentioning

confidence: 99%

“…To a certain extent, the introduction of the Lévy flight can also accelerate the cover algorithm's convergence rate. The iterative formula improved by Lévy flight can be expressed as Equation (14).…”

Section: Enhanced Exploitation Phasementioning

confidence: 99%

“…Nematzadeh S et al [ 13 ] presented a mutant GWO (MuGWO) to enhance resource utilization by maximizing coverage and maintaining connectivity. Dao, TK et al [ 14 ] proposed an improved Archimedes optimization algorithm (EAOA) to address optimal node coverage in unbalanced WSN distribution during random deployment. ZainEldin, H et al [ 15 ] developed an improved dynamic deployment technique based on a genetic algorithm (IDDT-GA) to maximize coverage with minimal nodes and reduce overlapping areas between adjacent nodes.…”

Section: Introductionmentioning

confidence: 99%

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Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

Zhang

2023

Biomimetics

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The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed.

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Section: The Lévy Flight Methodsmentioning

confidence: 99%

Section: Enhanced Exploitation Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

Zhang

2023

Biomimetics

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“…Each node will expand with a limited Radius, and can find other nodes within the node's connection Radius. Node link Radius, Antennas, and Network node locations are all factors in whether there is connectivity between two nodes and are characterised by the connectivity probability [11]. It is important to note that through the probability of connection, it is possible to obtain the physical possibility of data delivery.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Mardenov,

Adamova,

Zhukabayeva

et al. 2023

Journal of Robotics and Control

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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.

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“…The placement of the nodes is frequently highly uncertain-for some design reasons, it could even be classified as harsh-which could cause some errors in the location signal [7]. It is often utilized across the Internet or cloud environment [8,9] because it has the beneficial properties of WSN, such as self-organization, speed, practicality, and ease of deployment [10]. In order to monitor environmental and physical conditions, the WSN network [11] is equipped with the minute parts of heterogeneous or homogeneous sensor nodes [12].…”

Section: Introductionmentioning

confidence: 99%

A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer

Nguyen,

Dao,

Nguyen

2023

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks (WSNs) are widely used for various practical applications due to their simplicity and versatility. The quality of service in WSNs is greatly influenced by the coverage, which directly affects the monitoring capacity of the target region. However, low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges. This study proposes an optimal node planning strategy for network coverage based on an adjusted single candidate optimizer (ASCO) to address these issues. The single candidate optimizer (SCO) is a metaheuristic algorithm with stable implementation procedures. However, it has limitations in avoiding local optimum traps in complex node coverage optimization scenarios. The ASCO overcomes these limitations by incorporating reverse learning and multi-direction strategies, resulting in updated equations. The performance of the ASCO algorithm is compared with other algorithms in the literature for optimal WSN node coverage. The results demonstrate that the ASCO algorithm offers efficient performance, rapid convergence, and expanded coverage capabilities. Notably, the ASCO achieves an archival coverage rate of 88%, while other approaches achieve coverage rates below or equal to 85% under the same conditions.

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An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm

Cited by 11 publications

References 43 publications

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer

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