A Fast and Energy-Efficient Method for Online and Incremental Pareto-Front Update

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Ferreira, PJS (Autor) Outra; João Mendes-Moreira (Autor) FEUP; João M. P. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: 10th IEEE World Forum on Internet of Things, WF-IoT 2024, Ottawa, ON, Canada, November 10-13, 2024

Páginas: 1-6

10th IEEE World Forum on Internet of Things, WF-IoT 2024

Ottawa, 10 November 2024 through 13 November 2024

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-Y5Z

DOI: 10.1109/wf-iot62078.2024.10811392

Abstract (EN): Self-adaptive Systems (SaS) are becoming increasingly important for adapting to dynamic environments and for optimizing performance on resource-constrained devices. A practical approach to achieving self-adaptability involves using a Pareto-Front (PF) to store the system's hyper-parameters and the outcomes of hyperparameter combinations. This paper proposes a novel method to approximate a PF, offering a configurable number of solutions that can be adapted to the device's limitations. We conducted extensive experiments across various scenarios, where all PF solutions were replaced, and real world scenarios were performed using actual measurements from a Human Activity Recognition (HAR) system. Our results show that our method consistently outperforms previous methods, mainly when the maximum number of PF solutions is in the order of hundreds. The effectiveness of our method is most apparent in real-case scenarios where it achieves, when executed in a Raspberry Pi 5, up to 87% energy consumption reduction and lower execution times than the second-best algorithm. Additionally, our method ensures a more evenly distributed solution across the PF, preventing the high concentration of solutions. © 2024 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica