Multi-objective Reinforcement Learning for Energy Harvesting Wireless Sensor Nodes

Shaswot Shresthamali, Masaaki Kondo, Hiroshi Nakamura

研究成果: Conference contribution

抄録

Modern Energy Harvesting Wireless Sensor Nodes (EHWSNs) need to intelligently allocate their limited and unreliable energy budget among multiple tasks to ensure long-term uninterrupted operation. Traditional solutions are ill-equipped to deal with multiple objectives and execute a posteriori tradeoffs. We propose a general Multi-objective Reinforcement Learning (MORL) framework for Energy Neutral Operation (ENO) of EHWSNs. Our proposed framework consists of a novel Multi-objective Markov Decision Process (MOMDP) formulation and two novel MORL algorithms. Using our framework, EHWSNs can learn policies to maximize multiple task-objectives and perform dynamic runtime tradeoffs. The high computation and learning costs, usually associated with powerful MORL algorithms, can be avoided by using our comparatively less resource-intensive MORL algorithms. We evaluate our framework on a general single-task and dual-task EHWSN system model through simulations and show that our MORL algorithms can successfully tradeoff between multiple objectives at runtime.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ98-105
ページ数8
ISBN(電子版)9781665438605
DOI
出版ステータスPublished - 2021
イベント14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021 - Singapore, Singapore
継続期間: 2021 12月 202021 12月 23

出版物シリーズ

名前Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021

Conference

Conference14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021
国/地域Singapore
CitySingapore
Period21/12/2021/12/23

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 電子工学および電気工学

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