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March 6, 2026IEIE Transactions on Smart Processing and Computing0 citations

Optimizing Resource Allocation in Industrial IoT through Distributed Multi-Resource Management: An Age of Information Approach

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F(Feng Liu (72874)The University of QueenslandZLZongchen Liu

Key Points

  • The research aims to develop a distributed algorithm for optimizing resource allocation in IIoT environments.
  • Introduced a distributed algorithm for resource management.
  • Focused on energy, computation, and channel availability constraints.
  • Integrated Age of Information as a decision-making parameter.
  • Conducted preliminary simulations to test the model.
  • The algorithm improved resource allocation efficiency in simulations.
  • Demonstrated scalability and adaptability to changing industrial demands.
  • Indicated potential for timely data processing and system responsiveness.

Abstract

In the landscape of the Industrial Internet of Things (IIoT), efficient resource allocation remains a pivotal challenge. Addressing this, we introduce a novel distributed algorithm that synergizes with the dynamic nature of IIoT systems. Grounded in the pragmatic constraints of energy, computation, and channel availability, our model innovates through its integration of the Age of Information (AoI) as a central decision-making parameter, ensuring timely data processing and system responsiveness. The algorithm’s distributed nature allows for scalability and adaptability, crucial for the fluctuating demands of industrial settings. Preliminary simulations suggest its potential to optimize resource management, indicating its readiness for further empirical validation.

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Cite This Study

(72874) et al. (2026) studied this question.

synapsesocial.com/papers/69aa7087531e4c4a9ff5a60fhttps://doi.org/10.5573/ieiespc.2026.15.1.108
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