Task Scheduling Strategies for Utility Maximization in a Renewable-Powered IoT Node

Johann Leithon, Luis A. Suarez, Muhammad Moiz Anis, Dushantha Nalin K. Jayakody

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this paper, we propose a task scheduling strategy for an Internet of Things (IoT) node powered by renewable energy (RE). The node is assumed to have a rechargeable battery and an RE harvester. Moreover, the node is requested to perform {M} tasks over a planning period of {N}~\geq ~{M} time slots. For each task, a priority rating and a reward are assigned. With these considerations we develop a mathematical framework to optimize the utility of the node, defined as the sum of rewards over the specified planning horizon. Using the proposed framework, we derive a genie-aided strategy, which serves as a performance benchmark for online algorithms. We then propose two online task scheduling strategies of different complexity level, which correspond to a Mixed Integer Linear Programming (MILP) based approach and later on, a simpler sorting-based mechanism is also introduced. The presented techniques use existing forecasting methods to estimate future RE production. We finally evaluate the performance of the proposed strategies and their robustness to forecasting errors through extensive simulations. The impact of system parameters such as battery size and RE harvesting capacity are also examined numerically.

Original languageEnglish
Article number8932586
Pages (from-to)542-555
Number of pages14
JournalIEEE Transactions on Green Communications and Networking
Issue number2
Publication statusPublished - Jun 2020


  • Internet of Things (IoT)
  • renewable energy harvesting
  • task scheduling

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

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