Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs
Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1141 |
| _version_ | 1797297023965200384 |
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| author | Il-kyu Ha |
| author_facet | Il-kyu Ha |
| author_sort | Il-kyu Ha |
| collection | DOAJ |
| description | Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods. |
| first_indexed | 2024-03-07T22:15:15Z |
| format | Article |
| id | doaj.art-b733d7a528774dd795bddd9e2fabf1f9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:15Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b733d7a528774dd795bddd9e2fabf1f92024-02-23T15:33:41ZengMDPI AGSensors1424-82202024-02-01244114110.3390/s24041141Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVsIl-kyu Ha0School of Computer Science, Kyungil University, Gyeongsan 38428, Republic of KoreaRecently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods.https://www.mdpi.com/1424-8220/24/4/1141unmanned aerial vehiclesprobabilistic searchair pollution detectionUAV air pollution detection |
| spellingShingle | Il-kyu Ha Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs Sensors unmanned aerial vehicles probabilistic search air pollution detection UAV air pollution detection |
| title | Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs |
| title_full | Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs |
| title_fullStr | Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs |
| title_full_unstemmed | Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs |
| title_short | Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs |
| title_sort | improved a star search algorithm for probabilistic air pollution detection using uavs |
| topic | unmanned aerial vehicles probabilistic search air pollution detection UAV air pollution detection |
| url | https://www.mdpi.com/1424-8220/24/4/1141 |
| work_keys_str_mv | AT ilkyuha improvedastarsearchalgorithmforprobabilisticairpollutiondetectionusinguavs |