DVFS-Based Long-Term Task Scheduling for Dual-Channel Solar-Powered Sensor Nodes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Tongda Wu
  • Yongpan Liu
  • Daming Zhang
  • Jinyang Li
  • Xiaobo Sharon Hu
  • Huazhong Yang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8016408
Pages (from-to)2981-2994
Journal / PublicationIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume25
Issue number11
Publication statusPublished - Nov 2017

Abstract

Solar-powered sensor nodes (SCSNs) with energy storages have the greatest potential and are widely used in the coming era of the Internet of Things, since they avoid tedious battery maintenance tasks. However, because the solar energy source is unstable and limited, the sensor nodes suffer from high deadline miss ratio (DMR). To achieve better DMR, the existing scheduling algorithms find the best scheduling scheme in a single period of the recurring task queue and, hence, ignore the long-term performance. To tackle this challenge, this paper proposes a three-level dynamic voltage-frequency scaling (DVFS)-based scheduling strategy to minimize long-term DMR for dual-channel SCSNs. This approach includes a day-level scheduler to achieve a coarse-grained task arrangement, two artificial neural networks to determine the task priorities, and a DVFS-based task selection algorithm for slot-level execution. Experiments show that the proposed scheduler reduces DMR by over 30% on average.

Research Area(s)

  • Dual-channel solar-powered sensor nodes (DCSPs), dynamic voltage-frequency scaling (DVFS), task scheduling

Citation Format(s)

DVFS-Based Long-Term Task Scheduling for Dual-Channel Solar-Powered Sensor Nodes. / Wu, Tongda; Liu, Yongpan; Zhang, Daming; Li, Jinyang; Hu, Xiaobo Sharon; Xue, Chun Jason; Yang, Huazhong.

In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 25, No. 11, 8016408, 11.2017, p. 2981-2994.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review