A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors
The number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/22/6495 |
| _version_ | 1797547944449146880 |
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| author | Muhammad Daud Kamal Ali Tahir Muhammad Babar Kamal Faisal Moeen M. Asif Naeem |
| author_facet | Muhammad Daud Kamal Ali Tahir Muhammad Babar Kamal Faisal Moeen M. Asif Naeem |
| author_sort | Muhammad Daud Kamal |
| collection | DOAJ |
| description | The number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects; for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov model. This study can be beneficial for multicriteria decision-making for various disciplines, including health care. |
| first_indexed | 2024-03-10T14:52:15Z |
| format | Article |
| id | doaj.art-dd5e169040054e05a365ebc923c7168e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:52:15Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dd5e169040054e05a365ebc923c7168e2023-11-20T20:56:05ZengMDPI AGSensors1424-82202020-11-012022649510.3390/s20226495A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless SensorsMuhammad Daud Kamal0Ali Tahir1Muhammad Babar Kamal2Faisal Moeen3M. Asif Naeem4Institute of Geographical Information Systems, National University of Sciences and Technology, Islamabad 44000, PakistanInstitute of Geographical Information Systems, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Computer Science, COMSATS University, Islamabad 44000, PakistanDepartment of Computer & Decision Engineering (CoDE), Université Libre de Bruxelles, 1050 Bruxelles, BelgiumDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), Islamabad 44000, PakistanThe number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects; for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov model. This study can be beneficial for multicriteria decision-making for various disciplines, including health care.https://www.mdpi.com/1424-8220/20/22/6495wireless sensorsglobal positioning system (GPS)prediction algorithmMarkov modelhidden Markov modelT-pattern tree |
| spellingShingle | Muhammad Daud Kamal Ali Tahir Muhammad Babar Kamal Faisal Moeen M. Asif Naeem A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors Sensors wireless sensors global positioning system (GPS) prediction algorithm Markov model hidden Markov model T-pattern tree |
| title | A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors |
| title_full | A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors |
| title_fullStr | A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors |
| title_full_unstemmed | A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors |
| title_short | A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors |
| title_sort | survey for the ranking of trajectory prediction algorithms on ubiquitous wireless sensors |
| topic | wireless sensors global positioning system (GPS) prediction algorithm Markov model hidden Markov model T-pattern tree |
| url | https://www.mdpi.com/1424-8220/20/22/6495 |
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