LoraWAN based Data Collection in Large Scale Agricultural Fields
Riferimenti esterni Prof. Simone Silvestri - University of Kentucky, USA
Gruppi di ricerca DAUIN - GR-03 - COMPUTER NETWORKS GROUP - NETGROUP
Descrizione The Internet of Agricultural Things (IoAT) is an increasingly important opportunity to improve farmersí awareness and monitoring capabilities for decision making. As an example, IoAT data could provide information on irrigation, use of fertilizers, detection of illnesses, and general crop health. However, the large scale of agricultural fields makes it inconvenient, and often impossible, to form a connected network of IoAT devices. LoraWAN technologies offer an opportunity to collect data in these contexts exploiting their long range and low power consumption capabilities. However, the low data rate offered by these technologies makes the collection of agricultural data of significant size (e.g., RGB or hyperspectral images) less efficient. In this thesis, we will develop optimization and machine learning solutions to improve the capabilities of LoraWAN technologies for the collection of IoAT data. The student will make hands-on experience on LoraWAN devices based on Raspberry-PI.
Scadenza validita proposta 04/05/2024 PROPONI LA TUA CANDIDATURA