Journal: IET Computers & Digital Techniques

Abbreviation

Publisher

Institution of Engineering and Technology

Journal Volumes

ISSN

1751-8601
1751-861X

Description

Filters

2010 - 201922020 - 20221
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Publications 1 - 3 of 3
  • Selected papers from the 18(th) International Conference on Field Programmable Logic and Applications (FPL 2008)
    Item type: Other Journal Item
    Kebschull, Udo; Platzner, Marco; Teich, Jürgen (2010)
    IET Computers & Digital Techniques
  • A case for three-dimensional stacking of tightly coupled data memories over multi-core clusters using low-latency interconnects
    Item type: Journal Article
    Azarkhish, Erfan; Loi, Igor; Benini, Luca (2013)
    IET Computers & Digital Techniques
  • Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling
    Item type: Journal Article
    Stricker, Naomi; Da Forno, Reto; Thiele, Lothar (2022)
    IET Computers & Digital Techniques
    Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non-deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting-based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non-robust system's performance by up to 13.8 times in a real-world setting.
Publications 1 - 3 of 3