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| Titre : | Data collection based on Q-learning method in terrestrial networks assisted by UAV | | Type de document : | texte manuscrit | | Auteurs : | Rahil Benzid, Auteur ; Omar Sami Oubbati, Directeur de thèse | | Editeur : | Laghouat : Université Amar Telidji - Département d'électronique | | Année de publication : | 2021 | | Importance : | 41p. | | Format : | 30cm. | | Note générale : | Specialty: Telecommunication System | | Langues : | Anglais | | Résumé : | Nowadays, Unmanned Aerial Vehicle (UAVs) are playing an integral and
sustainable function in many verticals touching exclusive components of our
lives; such as civil, public and military applications. The objective is to appoint
a self-trained UAV as a flying cell unit gathering data from ground sensor nodes
fairly distributed in a given geographical area through a predefined period of
time. In this approach, Q-learning (QL) algorithm is employed to train the
UAV to learn the environment and provide appropriate scheduling to
accomplish its data collection mission while minimizing the data collection
time. As a matter of fact, collecting information from sensors may face some
noticeable challenges. However, due to use the flexibility of UAVs, collecting
information would be much easier. In this thesis, we tried to figure out about
UAVs that support data collection over WSN (wireless sensor network), while
maximizing the amount of collected data and minimizing the flight duration of
time. | | note de thèses : | mémoire de master électronique |
Data collection based on Q-learning method in terrestrial networks assisted by UAV [texte manuscrit] / Rahil Benzid, Auteur ; Omar Sami Oubbati, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'électronique, 2021 . - 41p. ; 30cm. Specialty: Telecommunication System Langues : Anglais | Résumé : | Nowadays, Unmanned Aerial Vehicle (UAVs) are playing an integral and
sustainable function in many verticals touching exclusive components of our
lives; such as civil, public and military applications. The objective is to appoint
a self-trained UAV as a flying cell unit gathering data from ground sensor nodes
fairly distributed in a given geographical area through a predefined period of
time. In this approach, Q-learning (QL) algorithm is employed to train the
UAV to learn the environment and provide appropriate scheduling to
accomplish its data collection mission while minimizing the data collection
time. As a matter of fact, collecting information from sensors may face some
noticeable challenges. However, due to use the flexibility of UAVs, collecting
information would be much easier. In this thesis, we tried to figure out about
UAVs that support data collection over WSN (wireless sensor network), while
maximizing the amount of collected data and minimizing the flight duration of
time. | | note de thèses : | mémoire de master électronique |
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| Thc 09-57 | Thc 09-57 | Thése | BIBLIOTHEQUE DE FACULTE DE TECHNOLOGIE | Genie electrique (TEC) | Disponible |
