Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization
The main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data col...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823002793 |
| _version_ | 1827797374882283520 |
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| author | Mohammed A. Alqarni Mohamed H. Mousa Mohamed K. Hussein Mohamed A. Mead |
| author_facet | Mohammed A. Alqarni Mohamed H. Mousa Mohamed K. Hussein Mohamed A. Mead |
| author_sort | Mohammed A. Alqarni |
| collection | DOAJ |
| description | The main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data collection is used to address the hot-spot problem. However, there are two challenges, namely, clustering and mobile sink shortest trajectory traversal, which significantly affect the network energy consumption, lifetime, and delay. To achieve the goal of this study, first, a formal model is presented to solve the optimization problem of determining the optimal number of clusters and corresponding cluster heads, which are taken as rendezvous nodes accessed by the mobile sink considering the network energy consumption, intracluster communication, and transmission delay. Second, a discrete differential evolution algorithm is proposed to solve the formulated optimization problem. Third, an ant colony optimization-based algorithm is proposed to construct the shortest path for the mobile sink to traverse the selected rendezvous nodes. The experimental evaluations show that the proposed strategy significantly improves cluster head selection, balances the cluster members, increases the network lifetime by 54%, decreases the transmission delay by 63%, and reduces energy consumption by 47% compared to several routing strategies in the literature. |
| first_indexed | 2024-03-11T19:21:17Z |
| format | Article |
| id | doaj.art-9561010f7d5842498ba6f9601d5ab4d0 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T19:21:17Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-9561010f7d5842498ba6f9601d5ab4d02023-10-07T04:34:11ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-09-01358101725Improved wireless sensor network data collection using discrete differential evolution and ant colony optimizationMohammed A. Alqarni0Mohamed H. Mousa1Mohamed K. Hussein2Mohamed A. Mead3Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Computer Science & Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia; Corresponding author.Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, EgyptDepartment of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, EgyptThe main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data collection is used to address the hot-spot problem. However, there are two challenges, namely, clustering and mobile sink shortest trajectory traversal, which significantly affect the network energy consumption, lifetime, and delay. To achieve the goal of this study, first, a formal model is presented to solve the optimization problem of determining the optimal number of clusters and corresponding cluster heads, which are taken as rendezvous nodes accessed by the mobile sink considering the network energy consumption, intracluster communication, and transmission delay. Second, a discrete differential evolution algorithm is proposed to solve the formulated optimization problem. Third, an ant colony optimization-based algorithm is proposed to construct the shortest path for the mobile sink to traverse the selected rendezvous nodes. The experimental evaluations show that the proposed strategy significantly improves cluster head selection, balances the cluster members, increases the network lifetime by 54%, decreases the transmission delay by 63%, and reduces energy consumption by 47% compared to several routing strategies in the literature.http://www.sciencedirect.com/science/article/pii/S1319157823002793Wireless sensor networkEnergy efficientDynamic clusteringDifferential evolutionAnt colony optimizationData collection |
| spellingShingle | Mohammed A. Alqarni Mohamed H. Mousa Mohamed K. Hussein Mohamed A. Mead Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization Journal of King Saud University: Computer and Information Sciences Wireless sensor network Energy efficient Dynamic clustering Differential evolution Ant colony optimization Data collection |
| title | Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization |
| title_full | Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization |
| title_fullStr | Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization |
| title_full_unstemmed | Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization |
| title_short | Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization |
| title_sort | improved wireless sensor network data collection using discrete differential evolution and ant colony optimization |
| topic | Wireless sensor network Energy efficient Dynamic clustering Differential evolution Ant colony optimization Data collection |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823002793 |
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