WSN Deployment Strategy for Real 3D Terrain Coverage Based on Greedy Algorithm with DEM Probability Coverage Model

Fu, Wendi; Yang, Yan; Hong, Guoqi; Hou, Jing

doi:10.3390/electronics10162028

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…needed to model the target area is the Building Information Modeling (BIM) tool [55]. For outdoor environment, researchers use mainly the Digital Elevation Model (DEM) [46], [56], [57] and raster and vector modelings [58] which is a 3D representation of the terrain topology. This data source is still inaccurate since it does not contain all the information related to the target area, namely the terrain type.…”

Section: A Indoor Environment Vs Outdoor Environmentmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

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Recent advances in hardware and communication technologies have accelerated the deployment of billions of wireless sensors. This transformation has created a wide range of applications adapted to the evolving trends of our daily life requirements. Wireless sensor networks (WSNs) could be deployed in several target areas including buildings, forests, oceans, and smart cities. Nevertheless, finding the optimal location for each sensor node is a challenging task, typically when the environment involves heterogeneous obstacles. Many approaches and methods have been proposed to deal with the problem of WSN deployment, each addressing one or more objectives and constraints, such as network coverage, lifetime, connectivity, and energy consumption. The purpose of this survey paper is to provide the needed background to understand and study the WSNs deployment problem with a focus on its two key aspects: the optimization model and the solving methods based on artificial intelligence (AI). Additionally, it covers recent works on WSNs deployment and identifies their advantages and limitations. Furthermore, simulation experiments were carried out to compare the performance of widely used algorithms in the context of WSNs deployment problem, primarily genetic algorithm, particle swarm optimization, flower pollination, and ant colony optimization. Finally, this paper discusses and highlights several open challenges and research issues that must be explored in the future.

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Section: A Indoor Environment Vs Outdoor Environmentmentioning

confidence: 99%

“…The 3D WSN deployment has been proven to be more challenging and necessitates more sensor nodes to reach the same coverage rate as a 2D WSN deployment [63]. The 3D grid division [64]- [66] and Digital elevation model (DEM) [46], [56], [67] are the two widely used target area modelings in 3D environment.…”

Section: B 2d Vs 3d Environmentsmentioning

confidence: 99%

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

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“…The authors in [12][13] partically studies the deployment problem of WSNs in an indoor environment, and design a node deployment algorithm of wireless sensor networks that applied to indoor space, which optimizes the deployment stratgy with better coverage quality through the method of experiment and comparison. The author of [21][22] realized the optimization of coverage, deployment cost and other goals by deploying sensor nodes on a two-dimensional plane and using intelligent optimization algorithms to find the optimal location of nodes. The comparison of experimental results suggests that the use of multiple objectives evolutionary algorithms is significantly better than genetic algorithms traditionally.…”

Section: Sensor Nodes Deployment Optimization In Three-dimensional Fu...mentioning

confidence: 99%

“…Therefore, we can know that the previous studies used a multi-objective optimal approach to solve the node deployment problem from the above analysis. Although the above algorithm strategies can achieve a better coverage quality in terms of sensors deployment problem, more practical and significant issues have yet to be addressed [22]. The current research in this area is relatively ideal, and in the real environment, there will be more or less obstacles and fire areas with different fire probabilities, so there are still some shortcomings in the research of three-dimensional whole space.…”

Section: Sensor Nodes Deployment Optimization In Three-dimensional Fu...mentioning

confidence: 99%

An optimized deployment strategy of smart smoke sensors in a large space

2022

KSII TIIS

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With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

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“…One category is node deployment in 3D space, such as smart cities [16], smart factories [17], and underwater wireless networks [18]. The other category is node deployment of WSNs on 3D surfaces, such as node deployment on mountains [19]. The WSN coverage of 3D surfaces as a particular coverage scenario for the 3D environments has been little studied in this aspect.…”

Section: Introductionmentioning

confidence: 99%

Multistrategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization

Wang

Xiao

Yang

et al. 2022

Wireless Communications and Mobile Computing

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Achieving maximum network coverage with a limited number of sensor nodes is key to node deployment of wireless sensor network (WSN). This paper proposes an improved marine predator algorithm (IMPA) for 3D surface wireless sensor network deployment. A population evolution strategy based on random opposition-based learning and differential evolution operator is proposed to enrich the population diversity and improve the global search capability of the algorithm. The grouping idea of the Shuffled Frog Leaping Algorithm (SFLA) is then introduced. A local search strategy based on the SFLA is proposed to replace the FADs effect of MPA and enhance the ability of the algorithm to escape from the local optimum. A quasireflected opposition-based learning strategy is also presented to improve the optimization accuracy, accelerate the convergence speed of the algorithm, and improve the quality of the solution. Fifteen benchmark functions are selected for testing. The results are compared with seven different algorithms. The results show that the improved algorithm has excellent optimization performances. Finally, the IMPA is applied to optimize WSN coverage on 3D surfaces. The experimental results show that the proposed IMPA has good terrain adaptation and optimal deployment capabilities. It can improve the coverage of the network, reduce the deployment cost, and extend the network life cycle.

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WSN Deployment Strategy for Real 3D Terrain Coverage Based on Greedy Algorithm with DEM Probability Coverage Model

Cited by 8 publications

References 20 publications

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

An optimized deployment strategy of smart smoke sensors in a large space

Multistrategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization

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