Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than ot...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/3/487 |
| _version_ | 1828155178076864512 |
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| author | Huanqing Cui Minglei Shu Min Song Yinglong Wang |
| author_facet | Huanqing Cui Minglei Shu Min Song Yinglong Wang |
| author_sort | Huanqing Cui |
| collection | DOAJ |
| description | Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. |
| first_indexed | 2024-04-11T22:50:26Z |
| format | Article |
| id | doaj.art-09f3abfb3780457abaa9c757e41cdc21 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:50:26Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-09f3abfb3780457abaa9c757e41cdc212022-12-22T03:58:36ZengMDPI AGSensors1424-82202017-03-0117348710.3390/s17030487s17030487Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks LocalizationHuanqing Cui0Minglei Shu1Min Song2Yinglong Wang3Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan 250101, ChinaComputer Science Department, Michigan Technological University, Houghton, MI 49931, USAShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan 250101, ChinaLocalization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.http://www.mdpi.com/1424-8220/17/3/487wireless sensor networksparticle swarm optimizationlocalizationparameter selectionperformance comparison |
| spellingShingle | Huanqing Cui Minglei Shu Min Song Yinglong Wang Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization Sensors wireless sensor networks particle swarm optimization localization parameter selection performance comparison |
| title | Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization |
| title_full | Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization |
| title_fullStr | Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization |
| title_full_unstemmed | Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization |
| title_short | Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization |
| title_sort | parameter selection and performance comparison of particle swarm optimization in sensor networks localization |
| topic | wireless sensor networks particle swarm optimization localization parameter selection performance comparison |
| url | http://www.mdpi.com/1424-8220/17/3/487 |
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