Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing
Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/960 |
| _version_ | 1797319054541717504 |
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| author | Alwin M. Kurian Munachimso J. Onuorah Habib M. Ammari |
| author_facet | Alwin M. Kurian Munachimso J. Onuorah Habib M. Ammari |
| author_sort | Alwin M. Kurian |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage. |
| first_indexed | 2024-03-08T04:01:14Z |
| format | Article |
| id | doaj.art-19d9f5521f5f451abbfccd7a0b446231 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:01:14Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-19d9f5521f5f451abbfccd7a0b4462312024-02-09T15:07:08ZengMDPI AGApplied Sciences2076-34172024-01-0114396010.3390/app14030960Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill ClimbingAlwin M. Kurian0Munachimso J. Onuorah1Habib M. Ammari2Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USADepartment of Engineering Technology, Engineering School, San Jacinto Community College, Pasadena, TX 77505, USADepartment of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University-Kingsville, Kingsville, TX 78363, USAWireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage.https://www.mdpi.com/2076-3417/14/3/960wireless sensor networkstraveling salesman problemant colony optimizationbinary ant colony algorithmhill climbingsimulated annealing |
| spellingShingle | Alwin M. Kurian Munachimso J. Onuorah Habib M. Ammari Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing Applied Sciences wireless sensor networks traveling salesman problem ant colony optimization binary ant colony algorithm hill climbing simulated annealing |
| title | Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing |
| title_full | Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing |
| title_fullStr | Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing |
| title_full_unstemmed | Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing |
| title_short | Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing |
| title_sort | optimizing coverage in wireless sensor networks a binary ant colony algorithm with hill climbing |
| topic | wireless sensor networks traveling salesman problem ant colony optimization binary ant colony algorithm hill climbing simulated annealing |
| url | https://www.mdpi.com/2076-3417/14/3/960 |
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