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

Leithon, Johann; Suárez, Luis; Anis, Muhammad Moiz; Jayakody, Dushantha Nalin K.

doi:10.1109/tgcn.2019.2959730

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In current years, with the prevalence of mobile computing and edge computing, scheduling algorithm has become an important technique for task management and resource allocation in IoT assisted applications. Leithon et al [20] designed a framework to optimize the task scheduling within the off-grid IoT nodes. They proposed a mixed linear programming method based online scheduling strategy with a sorting-based mechanism, which could result in a lower computational complexity.…”

Section: B Task Scheduling For Iot Applicationsmentioning

confidence: 99%

Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Zhou

Liang

Yan

et al. 2023

IEEE Internet Things J.

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Internet of Everything (IoE) is playing an increasingly indispensable role in modern intelligent applications. These smart applications are known for their real-time requirements under limited network and computing resources, in which it becomes a high consuming task to transform and compute tremendous amount of raw data in cloud center. The edge-cloud computing infrastructure allows large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an edge-cloud based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying data security constraint. In this study, a deep reinforcement learning enhanced scheduling method is proposed to address the NP-hard challenge of two-stage scheduling, which is able to allocate computing resources within an edge-cloud infrastructure to ensure computing task to be completed with minimum cost. The proposed reinforcement learning algorithm, which incorporates the Johnson's rule, is designed to achieve an optimal schedule in IoE. The performance of our method is evaluated and compared with several existing scheduling techniques, and experiment results demonstrate the ability of our proposed algorithm in achieving a more efficient schedule with 1.1-approximation to the targeted optimal IoE applications.

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Section: B Task Scheduling For Iot Applicationsmentioning

confidence: 99%

Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Zhou

Liang

Yan

et al. 2023

IEEE Internet Things J.

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“…The goal is to maximize the utility function by executing tasks with the highest quality values. In [28], the authors propose a Mixed Integer Linear Programming (MILP) task scheduling approach to maximize the rewards whilst considering the available energy. However, the works [26][27][28] differ from our problem as they schedule the tasks on a single node and disregard the criticality and dependency of the tasks.…”

Section: High-level Task Schedulingmentioning

confidence: 99%

“…In [28], the authors propose a Mixed Integer Linear Programming (MILP) task scheduling approach to maximize the rewards whilst considering the available energy. However, the works [26][27][28] differ from our problem as they schedule the tasks on a single node and disregard the criticality and dependency of the tasks. In addition, to the best of our knowledge, the previous works in the literature do not take into account the environmental factors that affect EH and the effect of EHMU with respect to the charging.…”

Section: High-level Task Schedulingmentioning

confidence: 99%

Ambient Source and Energy Prediction for Energy-Aware Task Scheduling in IoT

Azzam¹

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The deployment of the Internet of things (IoT) is lagging when compared to the forecasted data. This is due to the battery-limited IoT devices. One possible solution is to deploy energy harvesters and use energy management schemes. However, due to the time-varying availability of environmental factors and their effect on harvested power, a structured solution from ambient source and state of charge (SoC) prediction, to the utilization of energy management schemes needs to be presented. In this dissertation, we propose a costeffective ambient source prediction model, which we then feed into an energy harvesting management unit to predict the batteries' SoC. Lastly, we feed the predicted SoC into our scheduling algorithm to fulfill the application and balance the energy across the IoT network, by distributing the tasks. This solution reduces the deviation of the available energy of the nodes, whilst completing the application and abiding by its quality of service.

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