Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms
Localization has emerged as a critical problem over the past decades, with diverse techniques developed to address robot and mobile localization challenges across varied domains. However, existing localization methods still fall short of achieving the precision needed for certain high-demand applica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4804 |
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| author | Tamer Ramadan Badawy Nesreen I. Ziedan |
| author_facet | Tamer Ramadan Badawy Nesreen I. Ziedan |
| author_sort | Tamer Ramadan Badawy |
| collection | DOAJ |
| description | Localization has emerged as a critical problem over the past decades, with diverse techniques developed to address robot and mobile localization challenges across varied domains. However, existing localization methods still fall short of achieving the precision needed for certain high-demand applications. The proposed algorithm is designed to enhance localization accuracy by integrating mathematical modeling with the Crow Search Algorithm (CSA). The objective is to identify the most probable position within a designated search space. Anchored by a network of fixed points, the search area is initially defined. A mathematical approach is then applied to reduce this area by calculating the intersections between circles centered at each anchor point. Within this reduced area, an array of candidate points are selected, and their centroid is computed to serve as an initial estimate. The modified CSA iteratively improves upon this estimate by emulating the natural behavior of crows, updating its variables to converge on the optimal position. Experimental evaluations, conducted on both real and simulated datasets, demonstrate that the proposed algorithm leads to a better localization accuracy than existing methods. The proposed methodology achieves a significant accuracy improvement with an accuracy of 98%. These results confirm the effectiveness of our approach for applications that require high precision with minimal infrastructure and low computational complexity. |
| format | Article |
| id | doaj-art-1fda31fa83ec45348a1df6159aab2b67 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1fda31fa83ec45348a1df6159aab2b672025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-08-012515480410.3390/s25154804Optimized Intelligent Localization Through Mathematical Modeling and Crow Search AlgorithmsTamer Ramadan Badawy0Nesreen I. Ziedan1Communications and Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura 7651012, EgyptComputer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 7120001, EgyptLocalization has emerged as a critical problem over the past decades, with diverse techniques developed to address robot and mobile localization challenges across varied domains. However, existing localization methods still fall short of achieving the precision needed for certain high-demand applications. The proposed algorithm is designed to enhance localization accuracy by integrating mathematical modeling with the Crow Search Algorithm (CSA). The objective is to identify the most probable position within a designated search space. Anchored by a network of fixed points, the search area is initially defined. A mathematical approach is then applied to reduce this area by calculating the intersections between circles centered at each anchor point. Within this reduced area, an array of candidate points are selected, and their centroid is computed to serve as an initial estimate. The modified CSA iteratively improves upon this estimate by emulating the natural behavior of crows, updating its variables to converge on the optimal position. Experimental evaluations, conducted on both real and simulated datasets, demonstrate that the proposed algorithm leads to a better localization accuracy than existing methods. The proposed methodology achieves a significant accuracy improvement with an accuracy of 98%. These results confirm the effectiveness of our approach for applications that require high precision with minimal infrastructure and low computational complexity.https://www.mdpi.com/1424-8220/25/15/4804indoor localization2D position estimationswarm intelligenceCrow Search Algorithmmobile localizationindoor localization |
| spellingShingle | Tamer Ramadan Badawy Nesreen I. Ziedan Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms Sensors indoor localization 2D position estimation swarm intelligence Crow Search Algorithm mobile localization indoor localization |
| title | Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms |
| title_full | Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms |
| title_fullStr | Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms |
| title_full_unstemmed | Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms |
| title_short | Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms |
| title_sort | optimized intelligent localization through mathematical modeling and crow search algorithms |
| topic | indoor localization 2D position estimation swarm intelligence Crow Search Algorithm mobile localization indoor localization |
| url | https://www.mdpi.com/1424-8220/25/15/4804 |
| work_keys_str_mv | AT tamerramadanbadawy optimizedintelligentlocalizationthroughmathematicalmodelingandcrowsearchalgorithms AT nesreeniziedan optimizedintelligentlocalizationthroughmathematicalmodelingandcrowsearchalgorithms |