Sensor deployment for target coverage in underwater wireless sensor network

Arivudainambi, D.; Balaji, S.; Poorani, T. S.

doi:10.23919/pemwn.2017.8308032

Cited by 27 publications

(15 citation statements)

References 14 publications

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“…As one of the research fields of artificial intelligence, metaheuristic algorithms are an optimization tool for solving large-scale optimization problems based on the idea of sacrificing solution accuracy for solution efficiency, which is based on the division of labor and self-organization behavior of biological groups [12][13][14]. A novel modified GWO with Lévy flight (LF) is proposed in [15], where the whole wolf group is divided into four groups (α, β, δ and ω), and the first three groups are the best adaptive wolves guiding other wolves (ω) to search for the target, which is similar to GWO.…”

Section: Related Workmentioning

confidence: 99%

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An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Zhao

Cui

Guo

et al. 2020

Sensors

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Sensor nodes perform missions based on the effectual invariable coverage of events, and it is commonly guaranteed by the determinate deployment for sensor nodes who deviate from the optimum site frequently. To reach the optimal coverage effect with the lowest costs is a primary goal of wireless sensor networks. In this paper, by splicing the sensing area optimally with cellular grids, the best deployment location for sensors and the required minimum number of them are revealed. The optimization problem of coverage rate and energy consumption is converted into a task assignment problem, and a dynamic partition algorithm for cellular grids is also proposed to improve the coverage effect when the number of sensors is variable. Furthermore, on the basis of solving the multi-objective problem of reducing and balancing the energy cost of sensors, the vampire bat optimizer is improved by introducing virtual bats and virtual preys, and finally solves the asymmetric assignment problem once the number of cellular grids is not equal to that of sensors. Simulation results indicate that the residual energy of sensors during redeployment is balanced notably by our strategy when compared to three other popular coverage-enhancement algorithms. Additionally, the total energy cost of sensor nodes and coverage rate can be optimized, and it also has a superior robustness when the number of nodes changes.

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Section: Related Workmentioning

confidence: 99%

“…N C and R C can be calculated by Equations (5)- (7) and (14), respectively, based on the relationship between the actual number of nodes and N ub . The distance matrix D N S ×N ub can be calculated by Equation (2).…”

Section: Calculating the Radius Of Cellular Grids And The Distance Mamentioning

confidence: 99%

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Zhao

Cui

Guo

et al. 2020

Sensors

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“…To further verify the accuracy of the experiment, we compared the 3D-VPCA algorithm in this paper with the cuckoo search algorithm (CSA) [34] and the random algorithm (Random Algorithm). Initially, we deployed the nodes in a cube space of size 100 m 3 meters.…”

Section: Algorithm Analysis and Contrast Experimentmentioning

confidence: 99%

Target Detection Coverage Algorithm Based on 3D-Voronoi Partition for Three-Dimensional Wireless Sensor Networks

Dang

Shao

Hao

2019

Mobile Information Systems

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The detection of target events is an important research area in the field of wireless sensor networks (WSNs). In recent years, many researchers have discussed the problem of WSN target coverage in a two-dimensional (2D) coordinate system. However, the target detection problem in a 3D coordinate system has not been investigated extensively, and it is difficult to improve the network coverage ratio while ensuring reliable performance of WSN. In addition, sensor nodes that are initially deployed randomly cannot achieve accurate target coverage in practice. Moreover, it is necessary to consider the energy consumption factor owing to the limited energy of the sensor node itself. Hence, with the objective of addressing the target event coverage problem of WSNs in 3D space applications, this paper proposes a target detection coverage algorithm based on 3D-Voronoi partitioning for WSNs (3D-VPCA) in order to ensure reliable performance of the entire network. First, we extend Voronoi division based on the 2D plane, which allows 3D-Voronoi partitioning of sensor nodes in 3D regions. Then, it is optimized according to the 3D-Voronoi neighbouring node partitioning characteristics and combined with the improved algorithm. Next, we set the priority coverage mechanism and introduce the correlation force between the target point and the sensor node in the algorithm, so that the sensor node can move to the target position for accurate coverage. Finally, we carry out related simulation experiments to evaluate the performance and accuracy of the proposed algorithm. The results show that the proposed algorithm can effectively improve the coverage performance of the network while ensuring a high overall coverage ratio.

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“…In the algorithm, it took the cube as the basic unit to perform partition of UASNs, and arranged rotating temporary control nodes in the cluster. The results showed that CSWS improved the network coverage and energy consumption by comparing with the random deployment algorithm [66] and the redundant node sleep algorithm [67].…”

Section: Progress Of Clustering Technologiesmentioning

confidence: 99%

A Software-Defined Clustering Mechanism for Underwater Acoustic Sensor Networks

Wang

Gao

et al. 2019

IEEE Access

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Underwater acoustic sensor networks (UASNs) are important technical means to explore the ocean realm. Clustering is a crucial strategy which determines the performance and the lifetime of UASNs significantly. As a technological innovation, software defined networking (SDN) separates the data plane from the control plane to build a programmable network infrastructure. This paper presents a clustering mechanism based on SDN. Firstly, we introduce the architecture of software-defined UASN named SD-UASN. Secondly, we investigate hardware models of the underwater node and the surface controller. Thirdly, we define the communication procedure of SD-UASN, and implement the clustering mechanism. Finally, we perform simulations to verify the effectiveness of the proposed mechanism. The results reveal that a tradeoff of multiple constraints is achieved, and the performance of the clustering mechanism can be enhanced greatly. This work provides essential theoretical and technical support for software-defined UASNs.INDEX TERMS Underwater acoustic sensor networks (UASNs), clustering algorithm, software defined networking (SDN), software-defined UASN (SD-UASN), OpenFlow.

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Sensor deployment for target coverage in underwater wireless sensor network

Cited by 27 publications

References 14 publications

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Target Detection Coverage Algorithm Based on 3D-Voronoi Partition for Three-Dimensional Wireless Sensor Networks

A Software-Defined Clustering Mechanism for Underwater Acoustic Sensor Networks

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