Technologies for urban and rural internet of things

Marini, Riccardo (2023) Technologies for urban and rural internet of things, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Ingegneria elettronica, telecomunicazioni e tecnologie dell'informazione, 35 Ciclo. DOI 10.48676/unibo/amsdottorato/10652.
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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
Marini, Riccardo
Dottorato di ricerca
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
Data di discussione
24 Marzo 2023

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