A Node Deployment Optimization Algorithm of WSNs Based on Improved Moth Flame Search

Yao, Yindi; Hu, Shanshan; Li, Ying; Wen, Qin

doi:10.1109/jsen.2022.3166804

Cited by 28 publications

(5 citation statements)

References 34 publications

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“…Under the combined action of three forces, underwater nodes distributed in dense or sparse areas are driven to move in the direction of a higher coverage rate. Based on Coulomb law, the virtual force → F ij between the i th and j th nodes is expressed as (15) where ε r is the repulsive force coefficient; ε a is the attractive force coefficient; d ij is the geometric distance between nodes; d th is the distance threshold; R c is the communication radius of the underwater node; and α ij is the azimuth angle between nodes. The distribution density of underwater nodes causes the distance between nodes to be too far or too close, resulting in corresponding repulsive or attractive forces between nodes.…”

Section: Enhanced Coverage and Protocol Modelingmentioning

confidence: 99%

“…Intelligence optimization algorithms have prompted researchers to propose innovative techniques capable of improving coverage performance [15]. After transforming node coverage and energy problems into octahedral task allocation problems, Zhao et al proposed a vampire bat optimizer method, which enhanced coverage efficiency and reduced energy consumption [16].…”

Section: Introductionmentioning

confidence: 99%

“…The results are shown in Table 1. [15] VF-IMFO Coverage holes Analyze virtual force for node path optimization. Zhao, X.Q., 2019 [16] VBO Energy consumption Multi-energy optimization during redeployment.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

Zhang,

Luo,

et al. 2023

JMSE

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The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.

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Section: Enhanced Coverage and Protocol Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

Zhang,

Luo,

et al. 2023

JMSE

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“…Sensor networks play a vital role in the study of intelligent environmental monitoring systems [ 1 , 2 ]. Node localization optimization (NLO) and node coverage optimization (NCO) are important problems in WSNs [ 3 , 4 , 5 , 6 ], which are the core component of the Internet of Things (IoT) for intelligent management. The emergence of SI optimization algorithms provides novel approaches for many practical optimization problems that are difficult to solve.…”

Section: Introductionmentioning

confidence: 99%

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Xia,

Yang,

Zhang

et al. 2023

Biomimetics

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Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals’ smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.

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“…Te simulation results show that the algorithm has better repair efect, faster convergence speed, higher accuracy, efciency, and robustness, prolonging the network life by increasing the coverage of WSN. Yao et al proposed the VF-IMFO algorithm to repair coverage holes and reduce energy consumption during sensor node deployment [23]. Te path is optimized by adaptive inertia weight and variable spiral position updating strategy, and by analyzing the attraction of uncovered grid points, the virtual force between adjacent sensor nodes and the repulsive force of the boundary, and using the joint force as the disturbance factor of moth position updating.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deployment for Hybrid Sensor Networks Based on Efficient Node Configuration

Sun,

Peng,

Wang

et al. 2023

International Journal of Intelligent Systems

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Hybrid sensor networks, which contain mobile nodes and stationary nodes, are being used more and more widely. The second deployment of mobile nodes is a key problem to be solved, and the deployment performance of the network directly affects the monitoring effect of the network. Optimizing the configuration ratio of the two nodes can effectively reduce the network cost. In this paper, under the premise of knowing the coverage of the required monitoring area, the impact of sensor devices on node configuration is studied through parameter analysis, and the number and types of sensors that should be deployed in the hybrid sensor network are deduced, which can be conveniently and accurately used to design the actual hybrid sensor network. At the same time, for the secondary deployment of mobile nodes, this paper proposes a new mobile coverage method BS-CCP (box search and concentric circle positioning) to improve the coverage of the hybrid sensor network and maximize the coverage of the target area with the specified sensor types and numbers. Compared with existing work, the method in this paper reduces the number of iterations and reduces the number of required nodes. Comparing BS-CCP with the existing network mobile coverage algorithm, the experimental results show that the coverage obtained by this method is larger and more efficient.

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A Node Deployment Optimization Algorithm of WSNs Based on Improved Moth Flame Search

Cited by 28 publications

References 34 publications

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Optimal Deployment for Hybrid Sensor Networks Based on Efficient Node Configuration

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