A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks

Ru, Jingyu; Jia, Zhenhong; Yang, Yufang; Yu, Xiaoqing; Wu, Chengdong; Mei, Xu

doi:10.3390/s19081902

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…In terms of deploying wireless sensor networks on 3D surfaces [ 55 ], compared with other deployment strategies, swarm intelligence optimization algorithms have the advantages of simplicity, ease of use and no need for special modeling [ 56 ]. In this paper, a new hybrid algorithm based on MRFO is proposed and applied to the deployment of a 3D-surface WSN.…”

Section: Amrfocs For Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

Wang,

Luo,

Wei

et al. 2023

Biomimetics

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In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

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Section: Amrfocs For Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

Wang,

Luo,

Wei

et al. 2023

Biomimetics

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“…In the line segment SA, the midpoint O' is located above the curve. Therefore, based on this phenomenon, we supplement the method of judging the perceived blind spot in [27], that is, the third condition.…”

Section: Surface Area Calculationmentioning

confidence: 99%

“…In terms of deploying wireless sensor networks on 3D surfaces, swarm intelligence optimization algorithms have demonstrated the advantages of simplicity, ease of use, and no need of special modeling [27], compared with other deployment strategies. This paper proposes a method based on GWO to integrate wireless sensor networks deployed on 3D surfaces.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Wang

Xie

2020

IEEE Access

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Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution. INDEX TERMS Wireless sensor network, three-dimensional surface deployment, perceived blind zone, grid and integration, optimization, grey wolf optimizer, inner-layer encircle.

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“…Meanwhile, the repair process is limited by different kinds of intricate restraints. In the method of healing the coverage holes, the conventional healing ways redeployed by humans and airplanes which throw many nodes randomly [7,8]. WSRNs are frequently applied in the hazardous and severe scenarios.…”

Section: Introductionmentioning

confidence: 99%

The Repair Strategy for Event Coverage Holes Based on Mobile Robots in Wireless Sensor and Robot Networks

Zhuang

Wu³

et al. 2019

Sensors

Self Cite

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In the application of the wireless sensor and robot networks (WSRNs), there is an urgent need to accommodate flexible surveillance tasks in intricate surveillance scenarios. On the condition of flexible surveillance missions and demands, event coverage holes occur in the networks. The conventional network repair methods based on the geometric graph theory such as Voronoi diagram method are unable to meet the conditions of flexible surveillance tasks and severe multi-restraint scenarios. Mobile robots show obvious advantages in terms of adaptation capacity and mobility in hazardous and severe scenarios. First, we propose an event coverage hole healing model for multi-constrained scenarios. Then, we propose a joint event coverage hole repair algorithm (JECHR) on the basis of global repair and local repair to apply mobile robots to heal event coverage holes in WSRNs. Different from conventional healing methods, the proposed algorithm can heal event coverage holes efficaciously which are resulted from changing surveillance demands and scenarios. The JECHR algorithm can provide an optimal repair method, which is able to adapt different kinds of severe multi-constrained circumstances. Finally, a large number of repair simulation experiments verify the performance of the JECHR algorithm which can be adapted to a variety of intricate surveillance tasks and application scenarios.

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A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks

Cited by 9 publications

References 32 publications

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

The Repair Strategy for Event Coverage Holes Based on Mobile Robots in Wireless Sensor and Robot Networks

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