A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling
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...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9930331/ |
| _version_ | 1798023727823192064 |
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| author | Khaoula Zaimen Mohamed-El-Amine Brahmia Laurent Moalic Abdelhafid Abouaissa Lhassane Idoumghar |
| author_facet | Khaoula Zaimen Mohamed-El-Amine Brahmia Laurent Moalic Abdelhafid Abouaissa Lhassane Idoumghar |
| author_sort | Khaoula Zaimen |
| collection | DOAJ |
| description | 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 should be explored in the future. |
| first_indexed | 2024-04-11T17:51:01Z |
| format | Article |
| id | doaj.art-7a7cc3106d5546cd9ae32ef599b3e809 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T17:51:01Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7a7cc3106d5546cd9ae32ef599b3e8092022-12-22T04:11:06ZengIEEEIEEE Access2169-35362022-01-011011329411332910.1109/ACCESS.2022.32172009930331A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives ModelingKhaoula Zaimen0https://orcid.org/0000-0002-6901-369XMohamed-El-Amine Brahmia1https://orcid.org/0000-0003-0114-210XLaurent Moalic2https://orcid.org/0000-0003-3749-3227Abdelhafid Abouaissa3Lhassane Idoumghar4https://orcid.org/0000-0001-8853-3968CESI LINEACT, Strasbourg, FranceCESI LINEACT, Strasbourg, FranceUR 7499, Institut de Recherche en Informatique, Mathématique, Automatique et Signal, Mulhouse Cedex, FranceUR 7499, Institut de Recherche en Informatique, Mathématique, Automatique et Signal, Mulhouse Cedex, FranceUR 7499, Institut de Recherche en Informatique, Mathématique, Automatique et Signal, Mulhouse Cedex, FranceRecent 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 should be explored in the future.https://ieeexplore.ieee.org/document/9930331/Artificial intelligencemachine learningmetaheuristicsobjectives modelingoptimization modelwireless sensor networks |
| spellingShingle | Khaoula Zaimen Mohamed-El-Amine Brahmia Laurent Moalic Abdelhafid Abouaissa Lhassane Idoumghar A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling IEEE Access Artificial intelligence machine learning metaheuristics objectives modeling optimization model wireless sensor networks |
| title | A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling |
| title_full | A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling |
| title_fullStr | A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling |
| title_full_unstemmed | A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling |
| title_short | A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling |
| title_sort | survey of artificial intelligence based wsns deployment techniques and related objectives modeling |
| topic | Artificial intelligence machine learning metaheuristics objectives modeling optimization model wireless sensor networks |
| url | https://ieeexplore.ieee.org/document/9930331/ |
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