3D Self-Deployment Algorithm in Mobile Wireless Sensor Networks

Chen, Miao; Dai, Guoyong; Zhao, Xuehua; Tang, Zhongze; Chen, Qingzhang

doi:10.1007/978-3-662-46981-1_3

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…In order to increase the possibility of network connectivity, only a few nodes are selected (in the simulation experiment, the number of nodes selected to update the position is 2) to update their positions according to Eq. (21). The steps for WSN deployment on 3D surfaces are as follows.…”

Section: F Steps For Applying Egwo To Wsnmentioning

confidence: 99%

“…(15)(16)(17)(18)(19)(20), and the inner-layer group of the grey wolf updates the node position according to Eqs. (19) and (21). In addition, the position of the nodes must be constrained by the constraints of Eq.…”

Section: F Steps For Applying Egwo To Wsnmentioning

confidence: 99%

“…Some scholars have applied Voronoi diagrams commonly used in 2D environment deployment to 3D deployment [19], [20], and have obtained good deployment results. The recent works in [21], [22] present two self-deployed algorithms for the problem of deploying sensor nodes on surfaces. The widely used virtual force algorithm in 2D deployment has also been well applied in 3D surface deployment [22]- [25].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: F Steps For Applying Egwo To Wsnmentioning

confidence: 99%

Section: F Steps For Applying Egwo To Wsnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Xie

2020

IEEE Access

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“…Elhabyan et al [27] proposed a centralized pareto-based multi-objective approach to maintain full connectivity and coverage in a 3D WSN by finding the best state (cluster head, activity, or sleep) for each sensor in the network. Miao et al [28] proposed a 3D self-deployment (3DSD) algorithm, which uses negotiation strategy to ensure the effective connectivity of the network, and introduces density control strategy to make the nodes distribute evenly. The algorithm can realize the uniform and autonomous deployment of nodes in three-dimensional space with obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…In order to verify the feasibility and convergence speed of the algorithm, we select the classical PSO algorithm and the 3DSD algorithm in reference [28] as a comparison to carry out the following experiments. In the first group of experiments, we set the path loss threshold to-15dBm and the parameter sensing radius to 25m in the probability model.…”

Section: ) Comparison Of Algorithm Performancementioning

confidence: 99%

RSS-Based Coverage Deployment Method Under Probability Model in 3D-WSN

Hao

Dang

et al. 2019

IEEE Access

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Due to the wider application of wireless sensor networks in real life, 3D coverage closer to the actual application environment has become a research hotspot of current sensor networks. To this end, this paper proposes a three-dimensional coverage deployment method based on RSS (Received Signal Strength) under a probabilistic model. According to the path loss of the wireless signal in the propagation process, the distance between the nodes can be roughly calculated, and the maximum distance between the nodes is defined by setting a threshold of the path loss, thereby further ensuring network connectivity and network quality. The probability coverage model is used and the cube-based network coverage is constructed. Based on this, the optimal coverage deployment problem in 3D-WSN is explored. Combining and improving the traditional particle swarm optimization algorithm can converge faster and avoid falling into local optimum. The simulation results show that the proposed method can converge quickly to improve network coverage and effectively reduce network energy consumption. In addition, we built a real experimental environment to verify the network quality by observing the RSSI (Received Signal Strength Indicator) changes. The experimental results verify the effectiveness of the proposed method.INDEX TERMS Three-dimensional coverage, probability model, RSS, network coverage, network quality.

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Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

Hao

Dang

et al. 2020

Journal of Sensors

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Target sensing and information monitoring using wireless sensor networks have become an important research field. Based on two-dimensional plane research, information monitoring, and transmission for three-dimensional curved target events, due to the uneven deployment of nodes and failures in sensor networks, there are a lot of coverage loopholes in the network. In this paper, a method of detecting and repairing loopholes in monitoring the coverage of three-dimensional surface targets with hybrid nodes is proposed. In the target monitoring area where the hybrid nodes are randomly deployed, the three-dimensional surface cube is meshed, and the coverage loopholes are gradually detected according to the method of computational geometry, and then, the redundant mobile nodes around the coverage loopholes are selected. According to the calculated distance to cover the moving direction and distance of the loophole, the virtual force is used to adjust the mobile nodes to repair the coverage loopholes. Simulation results show that compared with other algorithms, this algorithm has a higher utilization rate of mobile nodes, uses fewer nodes to complete coverage, reduces network coverage costs, meets the overall network coverage requirements, and has lower mobile energy consumption and longer network life. The actual scene further verifies the good connectivity and high coverage of the whole network.

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3D Self-Deployment Algorithm in Mobile Wireless Sensor Networks

Cited by 6 publications

References 21 publications

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

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

RSS-Based Coverage Deployment Method Under Probability Model in 3D-WSN

Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

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