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Journal Articles IEEE Internet of Things Journal Year : 2022

Network Size Estimation for Direct-to-Satellite IoT


The worldwide adoption of the Internet of things (IoT) depends on the massive deployment of sensor nodes and timely data collection. However, installing the required ground infrastructure in remote or inaccessible areas can be economically unattractive or unfeasible. Cost-effective nanosatellites deployed in low Earth orbits (LEO) are emerging as an alternative solution: on-board IoT gateways provide access to remote IoT devices, according to direct-to-satellite IoT (DtS-IoT) architectures. One of the main challenges of DtS-IoT is to devise communication protocols that scale to thousands of highly constrained devices served by likewise constrained orbiting gateways. In this paper, we tackle this issue by first estimating the (varying) size of the device set underneath the (mobile) nanosatellite footprint. Then, we demonstrate applicability of the estimation when used to intelligently throttle DtS-IoT access protocols. Since recent works have shown that MAC protocols improve the throughput and energy efficiency of a DtS-IoT network when a network size estimation is available, we present here a novel and computationally-efficient network size estimator in DtS-IoT: our optimistic collision information (OCI) based estimator. We evaluate OCI's effectiveness with extensive simulations of DtS-IoT scenarios. Results show that when using network size estimations, the scalability of a frame slotted Aloha-based DtS-IoT network is boosted 8-fold, serving up to 4 × 10 3 devices, without energy efficiency penalties. We also show the effectiveness of the OCI mechanism given realistic detection ratios and demonstrate its low computational cost implementation, making it a strong candidate for network estimation in DtS-IoT.
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hal-03935406 , version 1 (11-01-2023)



Pablo Ilabaca, Samuel Montejo-Sánchez, Juan Fraire, Richard Demo Souza, Sandra Céspedes. Network Size Estimation for Direct-to-Satellite IoT. IEEE Internet of Things Journal, 2022, ⟨10.1109/JIOT.2022.3224678⟩. ⟨hal-03935406⟩
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