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Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems.

James Adu Ansere1, Mohsin Kamal2, Izaz Ahmad Khan3

  • 1Department of Electrical and Electronics Engineering, Sunyani Technical University, Sunyani P.O. Box 206, Ghana.

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Summary
This summary is machine-generated.

This study optimizes energy efficiency for Internet of Things (IoT) devices by managing radio sub-system power and resource allocation. The proposed method significantly enhances energy efficiency in 6G IoT networks.

Keywords:
Internet of ThingsKuhn–Munkres algorithmLagrangian decompositionenergy efficiencyrobust joint resource optimization

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Area of Science:

  • Wireless Communication
  • Internet of Things (IoT)
  • Energy Efficiency

Background:

  • IoT devices, especially those powered by renewables, face energy constraints.
  • The radio sub-system is a major energy consumer in IoT devices.
  • Energy efficiency is critical for 6G IoT network performance.

Purpose of the Study:

  • To maximize the energy efficiency of the radio sub-system in IoT networks.
  • To address the significant energy consumption of IoT devices.

Main Methods:

  • Formulated a mixed-integer nonlinear programming problem for joint optimization.
  • Optimized power allocation, sub-channel allocation, user selection, and activated remote radio units (RRUs).
  • Solved the NP-hard problem using fractional programming properties and the Lagrangian decomposition method with an improved Kuhn-Munkres algorithm.

Main Results:

  • The proposed optimization technique significantly improves IoT system energy efficiency.
  • Achieved better energy efficiency compared to existing state-of-the-art methods.

Conclusions:

  • Joint optimization of radio sub-system parameters is effective for enhancing IoT energy efficiency.
  • The developed method provides a viable solution for energy-constrained IoT systems in 6G networks.