logo SBA

ETD

Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-04122023-111108


Tipo di tesi
Tesi di laurea magistrale
Autore
CIOFFO, DANIELE
URN
etd-04122023-111108
Titolo
Q-learning-based distributed algorithm for hole recovery in Wireless Sensor Networks
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Dott.ssa Giada, Simionato
Parole chiave
  • coverage
  • coverage maximization
  • hole recovery
  • Q-learning
  • reinforcement learning
  • sensor node
  • wireless sensor network
  • WSN
Data inizio appello
28/04/2023
Consultabilità
Non consultabile
Data di rilascio
28/04/2026
Riassunto
In this work, the implementation of a distributed algorithm based on Q-learning for hole recovery in Wireless Sensor Networks is described. Initially, a detailed analysis of the state-of-the-art for Hole Detection and Recovery has been carried out, focusing on Reinforcement Learning approaches. Then, a simulation environment for multi-agent reinforcement learning hole recovery algorithms has been implemented in Matlab. Subsequently, a state-of-the-art Q-learning based algorithm has been developed in the simulation environment, adding also a decaying rate for the exploration rate. Finally, Differential Evolution has been used for Hyperparameters Optimization.
File