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Efficient Emission Scheduling for Dynamic Massive IoT Monitoring

Abstract : 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 sensor emission scheduling to ensure overall regular sensor data emissions over time (at a rate chosen by the user), with limited management costs incurred by sensors' arrivals and departure. Our objectives include monitoring quality--which we quantify through a ``diversity'' metric encompassing that information value depletes with time--and some 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 an alternative scheduling method existing in the literature. We show that our solution provides similar monitoring quality (diversity), but with significantly reduced management costs, making it better suited for Massive IoT contexts.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-03632557
Contributor : Gwen Maudet Connect in order to contact the contributor
Submitted on : Wednesday, April 20, 2022 - 8:08:40 AM
Last modification on : Friday, May 6, 2022 - 10:07:11 AM

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  • HAL Id : hal-03632557, version 2

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Gwen Maudet, Mireille Batton-Hubert, Patrick Maille, Laurent Toutain. Efficient Emission Scheduling for Dynamic Massive IoT Monitoring. 2022. ⟨hal-03632557v2⟩

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