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Summary
This summary is machine-generated.

This study introduces a machine learning approach for optimizing transmission power in wireless sensor networks. The reinforcement learning protocol enables nodes to minimize power, reducing energy waste and interference while maintaining network quality.

Keywords:
Q-learningenergy efficiencygame theorymulti-agentquality of servicereinforcement learningtransmission power controlwireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Internet of Things (IoT) applications drive high-density communications in wireless sensor networks (WSNs).
  • Over-congested unlicensed radio bands necessitate improved spectrum management and energy efficiency.
  • Existing transmission power control protocols often increase power, exacerbating interference and energy waste.

Purpose of the Study:

  • To investigate the application of machine learning (ML) for optimizing transmission power in WSNs.
  • To develop a protocol that enables wireless nodes to operate at the lowest feasible transmission power.
  • To ensure network quality requirements are met while minimizing power consumption and interference.

Main Methods:

  • A multi-agent reinforcement learning (MARL) system was developed for transmission power control.
  • Independent agents utilized a unified exploration strategy and reward structure for cooperative learning.
  • Simulations were conducted to evaluate the protocol's performance.

Main Results:

  • The MARL system converged to an equilibrium state.
  • Each node achieved minimum transmission power levels.
  • High packet reception ratio constraints were successfully maintained.
  • Significant reductions in energy consumption and packet delay were observed.

Conclusions:

  • Machine learning, specifically MARL, offers an effective solution for dynamic transmission power control in WSNs.
  • The proposed protocol balances energy efficiency and network performance.
  • This approach mitigates interference and improves overall network operation in dense IoT environments.