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Resource allocation for large-scale phenomena

par Benhamouche, Ouassim
Président du jury Innocenti, Bernardo
Promoteur Garone, Emanuele
Publication Non publié, 2026-04-24

Thèse de doctorat

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Résumé :

This thesis investigates the problem of resource allocation for monitoring and control of large-scale dynamical systems under limited sensing and intervention capabilities. In contrast to small-scale settings, where dense instrumentation enables full observability, large-scale systems are characterized by partial, intermittent, and uncertain information, making classical approaches impractical.To address this challenge, a unified framework is proposed that integrates sensing, state estimation, and control in a closed-loop manner. Monitoring is treated as an information-driven process, where data acquisition is designed to improve estimation quality and support decision-making. The methodology is developed for both discrete and continuous system representations, enabling the modeling of phenomena such as epidemic spreading, plant disease propagation, and traffic flow dynamics.Based on the proposed estimation frameworks, resource allocation strategies are designed to guide sensing and control actions under operational constraints. The effectiveness of the approach is demonstrated through case studies in epidemiology, agriculture, and traffic systems.