Documenti full-text disponibili:
|
Documento PDF (English)
- Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato.
Download (11MB)
|
Abstract
Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require
communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the
interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide
Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive
machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the
two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations
and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars
of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then,
machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize
the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.
Abstract
Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require
communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the
interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide
Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive
machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the
two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations
and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars
of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then,
machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize
the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.
Tipologia del documento
Tesi di dottorato
Autore
Marini, Riccardo
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
35
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Internet of Things, LPWAN, LoRaWAN, NB-IoT, UAV, UABS, V2X, Smart City, Smart Agriculture, Machine Learning, Reinforcement Learning, Meta-learning, Trajectory Design, Radio Resource Management
URN:NBN
DOI
10.48676/unibo/amsdottorato/10652
Data di discussione
24 Marzo 2023
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Marini, Riccardo
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
35
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Internet of Things, LPWAN, LoRaWAN, NB-IoT, UAV, UABS, V2X, Smart City, Smart Agriculture, Machine Learning, Reinforcement Learning, Meta-learning, Trajectory Design, Radio Resource Management
URN:NBN
DOI
10.48676/unibo/amsdottorato/10652
Data di discussione
24 Marzo 2023
URI
Statistica sui download
Gestione del documento: