Coverage Maximization in WSN Deployment Using Particle Swarm Optimization with Voronoi Diagram

Zaimen, Khaoula; Brahmia, Mohamed-el-Amine; Dollinger, Jean-François; Moalic, Laurent; Abouaïssa, Abdelhafid; Idoumghar, Lhassane

doi:10.1007/978-3-030-87657-9_7

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…( Chawla and Duhan, 2018 ; Sutantyo et al, 2011 ). There are other forms of hybrid methods that use geometric processes, e.g., Voronoi tessellation in pure form or augmented with order processes, e.g., buffering ( Arul and Manocha, 2021 ), k-means algorithm ( Chowdhury and De, 2021 ), gradient descent algorithm ( Inoue et al, 2021 ), area prioritization ( Zarei and Mozafar, 2021 ), and particle swarm optimization ( Zaimen et al, 2021 ). In these methods, the plan generation is controlled by theorems, propositions, lemmas, and protocols of the geometric process.…”

Section: Related Workmentioning

confidence: 99%

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Yusuf

Baber²

2022

Front. Robot. AI

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This article describes an approach for multiagent search planning for a team of agents. A team of UAVs tasked to conduct a forest fire search was selected as the use case, although solutions are applicable to other domains. Fixed-path (e.g., parallel track) methods for multiagent search can produce predictable and structured paths, with the main limitation being poor management of agents’ resources and limited adaptability (i.e., based on predefined geometric paths, e.g., parallel track, expanding square, etc.). On the other hand, pseudorandom methods allow agents to generate well-separated paths; but methods can be computationally expensive and can result in a lack of coordination of agents’ activities. We present a hybrid solution that exploits the complementary strengths of fixed-pattern and pseudorandom methods, i.e., an approach that is resource-efficient, predictable, adaptable, and scalable. Our approach evolved from the Delaunay triangulation of systematically selected waypoints to allocate agents to explore a specific region while optimizing a given set of mission constraints. We implement our approach in a simulation environment, comparing the performance of the proposed algorithm with fixed-path and pseudorandom baselines. Results proved agents’ resource utilization, predictability, scalability, and adaptability of the developed path. We also demonstrate the proposed algorithm’s application on real UAVs.

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Section: Related Workmentioning

confidence: 99%

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Yusuf

Baber²

2022

Front. Robot. AI

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“…The area dimension is another important criterion to be considered in the process of target area modeling. Indeed, most of the existing research works assume a 2D flat area divided according to a regular pattern as the grid representation [52], [54] or using a computational geometry approach such as Voronoi diagram and Delaunay triangulation [59]- [61]. Other approaches do not adopt any area division technique but define a set of distributed deployment points where sensors could be placed.…”

Section: B 2d Vs 3d Environmentsmentioning

confidence: 99%

“…P pbesti denotes the best position reached by the particle, P i (t) refers to the current position of the particle and P gbest is the global best position of the swarm. Several works have applied PSO algorithm and its variants to find the optimal placements of WSNs [47], [59], [106]- [115] Du [106], considered the WSN deployment problem in 3D terrain. To solve this problem, the author proposed a combination solution of the distributed particle swarm optimization and the 3D virtual force algorithm to maximize the coverage.…”

Section: Swarm Intelligence Optimization Algorithmsmentioning

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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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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Coverage Optimization of Field Observation Instrument Networking Based on an Improved ABC Algorithm

Deng

Huo

2022

Communications in Computer and Information Science

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Coverage Maximization in WSN Deployment Using Particle Swarm Optimization with Voronoi Diagram

Cited by 5 publications

References 16 publications

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

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

Coverage Optimization of Field Observation Instrument Networking Based on an Improved ABC Algorithm

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