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.

Statistical Energy Neutrality in IoT Hybrid Energy-Harvesting Networks

Caruso, Antonio;
2018

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/427623
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