Statistical Energy Neutrality in IoT Hybrid Energy-Harvesting Networks
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
soledad.escolar@uclm.es
No disponemos de la información de contacto de este autor.
No disponemos de la información de contacto de este autor.
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
Xavier.delToro@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
Felix.Villanueva@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
juancarlos.lopez@uclm.es
CONFERENCE — IEEE Symposium of Computers and Communications
PAGES — 1-6
DATE — 25/06/2018 – 28/06/2018
ISBN — 978-1-5386-6950-1
DOI — 10.1109/ISCC.2018.8538532
PUBLISHER — IEEE
YEAR — 2018
LOCATION — Natal, Brazil
@inproceedings{escolar2018statistical,
title={Statistical Energy Neutrality in IoT Hybrid Energy-Harvesting Networks},
author={Escolar, Soledad and Caruso, Antonio and Chessa, Stefano and del Toro, Xavier and Villanueva, F{\'e}lix J and L{\'o}pez, Juan C},
booktitle={2018 IEEE Symposium on Computers and Communications (ISCC)},
pages={00444--00449},
year={2018},
organization={IEEE}
}
Abstract
Scheduling tasks appropriately in an IoT device powered by multiple energy-harvesting sources is a challenging problem. In this paper, we model this problem, and we present a scheduling algorithm that optimally sets the overall node power consumption based on the utility, and on the energy required by tasks. The algorithm schedules high-level tasks, it uses the weather forecast informations available at the beginning of each scheduling period (typically a day), and the level of the battery, to define an optimal schedule. The main goal is to find a schedule that is energy neutral on average, over a period longer than the single scheduling window, for example a week. We test our scheduler on a simulated platform with the same specs of an Arduino node, equipped with a small (portable) solar panel, and attached to a small wind turbine. We see from the simulations that the scheduler performs as expected and that the utility of the scheduling improves as the error between the expected forecast and the real harvested energy is reduced.
