Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wire...
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| Format: | Article |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/2/343 |
| _version_ | 1818037578371170304 |
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| author | Dezhi Han Yunping Yu Kuan-Ching Li Rodrigo Fernandes de Mello |
| author_facet | Dezhi Han Yunping Yu Kuan-Ching Li Rodrigo Fernandes de Mello |
| author_sort | Dezhi Han |
| collection | DOAJ |
| description | The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements. |
| first_indexed | 2024-12-10T07:29:04Z |
| format | Article |
| id | doaj.art-f4ed690d10874f809534f1eafc3505ee |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:29:04Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f4ed690d10874f809534f1eafc3505ee2022-12-22T01:57:38ZengMDPI AGSensors1424-82202020-01-0120234310.3390/s20020343s20020343Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor NetworksDezhi Han0Yunping Yu1Kuan-Ching Li2Rodrigo Fernandes de Mello3College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Computer Science and Information Engineering, Providence University, Taichung 43301, TaiwanDepartment of Computer Science, University of Sao Paulo, Sao Carlos, SP 13566-590, BrazilThe Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.https://www.mdpi.com/1424-8220/20/2/343wireless sensor networksdv-hopdifferential evolutionnode localization |
| spellingShingle | Dezhi Han Yunping Yu Kuan-Ching Li Rodrigo Fernandes de Mello Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks Sensors wireless sensor networks dv-hop differential evolution node localization |
| title | Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks |
| title_full | Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks |
| title_fullStr | Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks |
| title_full_unstemmed | Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks |
| title_short | Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks |
| title_sort | enhancing the sensor node localization algorithm based on improved dv hop and de algorithms in wireless sensor networks |
| topic | wireless sensor networks dv-hop differential evolution node localization |
| url | https://www.mdpi.com/1424-8220/20/2/343 |
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