Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm
In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to loca...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227623000194 |
| _version_ | 1811159649890598912 |
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| author | Oluwasegun Julius Aroba Nalindren Naicker Timothy T. Adeliyi |
| author_facet | Oluwasegun Julius Aroba Nalindren Naicker Timothy T. Adeliyi |
| author_sort | Oluwasegun Julius Aroba |
| collection | DOAJ |
| description | In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches. |
| first_indexed | 2024-04-10T05:44:46Z |
| format | Article |
| id | doaj.art-df23a99724ed42378fdeda5751323d5a |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-04-10T05:44:46Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-df23a99724ed42378fdeda5751323d5a2023-03-06T04:18:13ZengElsevierScientific African2468-22762023-03-0119e01560Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithmOluwasegun Julius Aroba0Nalindren Naicker1Timothy T. Adeliyi2ICT and Society Research Group; Information Systems; Durban University of Technology; 4001, Durban South AfricaICT and Society Research Group; Information Systems; Durban University of Technology; 4001, Durban South Africa; Corresponding author.ICT and Society Research Group; Information Technology; Durban University of Technology; 4001, Durban South AfricaIn the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches.http://www.sciencedirect.com/science/article/pii/S2468227623000194Clustering AlgorithmsDEEC-Gaussian Gradient distanceHeterogeneous systemLocalization Estimation ErrorWireless Sensor Networks |
| spellingShingle | Oluwasegun Julius Aroba Nalindren Naicker Timothy T. Adeliyi Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm Scientific African Clustering Algorithms DEEC-Gaussian Gradient distance Heterogeneous system Localization Estimation Error Wireless Sensor Networks |
| title | Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm |
| title_full | Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm |
| title_fullStr | Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm |
| title_full_unstemmed | Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm |
| title_short | Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm |
| title_sort | node localization in wireless sensor networks using a hyper heuristic deec gaussian gradient distance algorithm |
| topic | Clustering Algorithms DEEC-Gaussian Gradient distance Heterogeneous system Localization Estimation Error Wireless Sensor Networks |
| url | http://www.sciencedirect.com/science/article/pii/S2468227623000194 |
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