- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even to render batteries obsolete. Such systems employ an energy scheduler to optimize their behavior and thus performance by adapting the node operation. Predictive models of harvesting sources, which are inherently non-deterministic and consequently challenging to predict, are often necessary for the scheduler to optimize performance. Therefore the accuracy of the predictive model inevitably impacts the scheduler and system performance. This fact has been largely overlooked in the vast amount of available results on energy management systems. We define a novel robustness metric for energy-harvesting systems that describes the effect prediction errors have on the system performance. Furthermore, we show that if a scheduler is optimal when predictions are accurate, it is not very robust. Thus there is a tradeoff between robustness and performance. We propose a prediction scaling method to improve a system's robustness and demonstrate the results using energy harvesting data sets from both outdoor and indoor scenarios. The method improves a non-robust system's performance by up to 75 times in a real-world setting. Show more
Book titleENSsys '20: Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems
Pages / Article No.
PublisherAssociation for Computing Machinery
SubjectEnergy Harvesting; Scheduling; Robustness; Internet of Things
Organisational unit03429 - Thiele, Lothar (emeritus) / Thiele, Lothar (emeritus)
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
MoreShow all metadata