Efficient Message Scheduling for Dynamic Massive IoT Monitoring
Résumé
In current sensor-based monitoring solutions, each application involves a customized deployment and requires significant configuration efforts to adapt to changes in the sensor field. This becomes particularly problematic for massive deployments of battery-powered monitoring sensors.
In this paper, we propose a generic solution for LPWAN sensors emissions scheduling, to ensure overall regular sensor data emissions over time (at a rate chosen by the user), while limiting management costs incurred by sensors' arrivals and departure.
Our objectives include monitoring quality that we evaluate through a ``diversity'' metric encompassing that information value depletes with time, plus management cost quantified by the number of orders sent to sensors.
Modeling arrivals and departures as random processes, we compute those performance metrics as functions of the overall data reception period selected, and evaluate them against alternative scheduling methods. We show that our solution is better suited for Massive IoT contexts.
Domaines
Intelligence artificielle [cs.AI] Arithmétique des ordinateurs Algorithme et structure de données [cs.DS] Modélisation et simulation Recherche opérationnelle [math.OC] Réseaux et télécommunications [cs.NI] Technologies Émergeantes [cs.ET] Mathématique discrète [cs.DM] Machine Learning [stat.ML]
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