Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes
In many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current lo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/11/3697 |
| _version_ | 1797532603302019072 |
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| author | Dogan Yildiz Serap Karagol |
| author_facet | Dogan Yildiz Serap Karagol |
| author_sort | Dogan Yildiz |
| collection | DOAJ |
| description | In many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current location. The main challenge at this stage is to design an optimum path to estimate the locations of UNNs as accurately as possible, reach all nodes in the network, and complete the localization process as quickly as possible. This article proposes a new path planning approach for MA-based localization called Nested Hexagon Curves (NHexCurves). The proposed model’s performance is compared with the performance of five existing static path planning models using Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) localization techniques in the obstacle-presence scenario. With the obstacle-handling trajectories used for the models, the negative impact of the obstacle on the localization is reduced. The proposed model provides full coverage and high localization accuracy in the obstacle-presence scenario. The simulation results show the advantages of the proposed path planning model with the H-curve model over existing schemes. |
| first_indexed | 2024-03-10T11:01:37Z |
| format | Article |
| id | doaj.art-9069663742b745678246f0b5e804951c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:01:37Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9069663742b745678246f0b5e804951c2023-11-21T21:26:44ZengMDPI AGSensors1424-82202021-05-012111369710.3390/s21113697Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence SchemesDogan Yildiz0Serap Karagol1Electrical & Electronics Engineering Department, Ondokuz Mayis University, 55270 Samsun, TurkeyElectrical & Electronics Engineering Department, Ondokuz Mayis University, 55270 Samsun, TurkeyIn many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current location. The main challenge at this stage is to design an optimum path to estimate the locations of UNNs as accurately as possible, reach all nodes in the network, and complete the localization process as quickly as possible. This article proposes a new path planning approach for MA-based localization called Nested Hexagon Curves (NHexCurves). The proposed model’s performance is compared with the performance of five existing static path planning models using Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) localization techniques in the obstacle-presence scenario. With the obstacle-handling trajectories used for the models, the negative impact of the obstacle on the localization is reduced. The proposed model provides full coverage and high localization accuracy in the obstacle-presence scenario. The simulation results show the advantages of the proposed path planning model with the H-curve model over existing schemes.https://www.mdpi.com/1424-8220/21/11/3697localizationmobile anchor node assisted localizationobstacle-handlingpath planningstatic path planning |
| spellingShingle | Dogan Yildiz Serap Karagol Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes Sensors localization mobile anchor node assisted localization obstacle-handling path planning static path planning |
| title | Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes |
| title_full | Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes |
| title_fullStr | Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes |
| title_full_unstemmed | Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes |
| title_short | Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes |
| title_sort | path planning for mobile anchor based wireless sensor networks localization obstacle presence schemes |
| topic | localization mobile anchor node assisted localization obstacle-handling path planning static path planning |
| url | https://www.mdpi.com/1424-8220/21/11/3697 |
| work_keys_str_mv | AT doganyildiz pathplanningformobileanchorbasedwirelesssensornetworkslocalizationobstaclepresenceschemes AT serapkaragol pathplanningformobileanchorbasedwirelesssensornetworkslocalizationobstaclepresenceschemes |