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Emission scheduling strategies for massive-IoT: implementation and performance optimization

Abstract

In today's monitoring solutions, each application involves custom deployment and requires significant configuration efforts to accommodate sensor changes. In contrast, in this paper, a massive deployment of battery-powered sensors is considered and we propose a more versatile monitoring solution that is not tied to physical device deployment. We characterize a monitoring strategy by formally defining a function that modifies the transmission period of a sensor that has just transmitted. Such a function can be customized to manage the tradeoff between overall monitoring accuracy and sensor energy consumption, for which we suggest a formalization through a generic metric of monitoring accuracy (to be weighed versus the monitoring network lifespan). We introduce a specific two-parameter instantiation for the period update function, that ensures strictly periodic emissions from sensors even when new sensors join the system over time. We show through simulations how the two parameters--target emission period and number of jointly used sensors--can be chosen according to the objectives for the monitoring, by highlighting the Pareto front for accuracy and energy-efficiency.
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Dates and versions

hal-03565071 , version 1 (10-02-2022)
hal-03565071 , version 2 (22-02-2022)
hal-03565071 , version 3 (27-03-2022)

Identifiers

  • HAL Id : hal-03565071 , version 3

Cite

Gwen Maudet, Mireille Batton-Hubert, Patrick Maillé, Laurent Toutain. Emission scheduling strategies for massive-IoT: implementation and performance optimization. MANAGE-IOT (NOMS 2022 workshop), Apr 2022, Budapest, Hungary. ⟨hal-03565071v3⟩
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