Reconstruction of coverage hole model and cooperative repair optimization algorithm in heterogeneous wireless sensor networks

Gou, Pingzhang; Mao, Gang; Jia, Xiangdong

doi:10.1016/j.comcom.2020.01.053

Cited by 16 publications

(6 citation statements)

References 21 publications

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“…For instance, Hao et al [ 22 ] used computational geometry in a 3D surface environment to detect holes step by step and used virtual forces to adjust the node positions according to the movement direction and distance, thus repairing the coverage holes and further ensuring network coverage and effective connectivity. The literature [ 23 ] came up with a Multi-Factor Collaborative Hole Repair Optimization Algorithm (FCH-ROA) for HWSNs, which constructs Pearson-like fuzzy matching relationships between mobile nodes and candidate nodes to repair the covered hole area and generates a decision table considering key nodes, remaining energy, and relative distance. Meanwhile, the mobile node moves a limited distance according to the decision table to complete the repair of the coverage holes.…”

Section: Related Workmentioning

confidence: 99%

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VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

Gou

Guo

et al. 2023

Sensors

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During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes’ energy consumption and lengthen the network’s lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network’s lifetime.

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Section: Related Workmentioning

confidence: 99%

“…It is recommended to presume that all mobile nodes in the network are dormant before attempting to improve coverage. The network can be built as an HWSN [ 23 ] by randomly deploying N nodes with various sensing radii in the target area R. The network model in this research is built on the following assumptions.…”

Section: System Model and Term Definitionmentioning

confidence: 99%

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

Gou

Guo

et al. 2023

Sensors

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“…In Equation (10), N is the number of packets, L is the packet length, S is the transmission rate, and the sensor acquisition rate can be calculated by Equation (11).…”

Section: Energy and Performance Optimization Of Vulnerability Repair ...mentioning

confidence: 99%

Distributed WSN Vulnerability Remediation System Based on Mobile-N Policy

Zhou

2023

Preprint

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Wireless sensor networks are widely used in military reconnaissance, environmental monitoring, and other fields. Due to its characteristics such as high node deployment difficulty, limited energy, etc. makes the network coverage area appear vulnerability, etc. leads to serious security problems, the study proposes a distributed wireless sensor network vulnerability repair system based on the Mobile-N strategy, through the vulnerability detection to determine the location of the vulnerability edge node, and at the same time proposes an energy and performance optimization strategy, through the mobile distance, redundancy and the remaining energy of the comprehensive comparison to select the optimal repair node. Experimental results show that the Mobile-N strategy performs well in repairing network vulnerabilities and can effectively repair vulnerabilities while minimizing energy consumption and maximizing network stability. Specifically, the effective repair area ratio and node repair rate of the Mobile-N strategy in each experiment are higher than those of other algorithms, with an average effective repair area ratio of 94.9% and an average failed node repair rate of 94.9%, which indicates that the strategy has obvious advantages in terms of repair energy efficiency and repair node rate. Therefore, the distributed WSN vulnerability repair system based on Mobile-N strategy is of great research significance for improving the security and efficiency of wireless sensor networks and ensuring their stable operation.

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“…Gou Pingzhang et al [23] established the coverage blind area model between heterogeneous wireless sensor network nodes and proposed a multifactor collaborative hole repair optimization algorithm (FCH-ROA). Coverage blind area restoration not only determines whether mixed wireless sensor networks can work normally and effectively, but also determines the coverage, performance, and lifetime of the network.…”

Section: Related Workmentioning

confidence: 99%

Monitoring Area Coverage Based on Control Multimedia Nodes Position in Mixed Underwater Mobile Wireless Sensor Networks

Liu

2022

Advances in Multimedia

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Due to the high cost of mobile nodes and the low cost of fixed nodes, in some applications of wireless sensor networks, in order to reduce the use cost, the mixed mode is used for node deployment. The deployed nodes include both fixed and mobile nodes. After the nodes are deployed, the fixed nodes do not move, and the mobile nodes move reasonably according to the network coverage to improve the network coverage effect. Due to the differences in equipment structure and use cost between fixed nodes and mobile nodes, in order to complete the monitoring task under the condition of reducing the use cost of wireless sensor networks, the number and position of mobile nodes can be reasonably adjusted as needed to improve the effective coverage area of wireless sensor networks. In the process of adjusting the position of mobile nodes, the moving distance of mobile nodes is fully considered. Simulation results show that this algorithm can improve the coverage effect of mixed wireless sensor networks and reduce the moving distance of mobile nodes.

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Reconstruction of coverage hole model and cooperative repair optimization algorithm in heterogeneous wireless sensor networks

Cited by 16 publications

References 21 publications

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

Distributed WSN Vulnerability Remediation System Based on Mobile-N Policy

Monitoring Area Coverage Based on Control Multimedia Nodes Position in Mixed Underwater Mobile Wireless Sensor Networks

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