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Distributed event-triggered adaptive partial diffusion strategy under dynamic network topology.

Minyu Feng1, Shuwei Deng1, Feng Chen1

  • 1College of Artificial Intelligence, Southwest University, Chongqing 400715, China.

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Summary
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This study introduces an event-triggered adaptive partial diffusion least mean-square algorithm (ET-APDLMS) for wireless sensor networks. The ET-APDLMS algorithm enhances estimation performance and conserves communication resources in dynamic network environments.

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Area of Science:

  • Wireless Sensor Networks
  • Distributed Signal Processing
  • Adaptive Filtering

Background:

  • Dynamic network topologies in wireless sensor networks (WSNs) can degrade distributed algorithm performance.
  • Limited communication resources pose challenges for estimation accuracy in WSNs.

Purpose of the Study:

  • To propose a novel algorithm that addresses performance degradation and resource limitations in WSNs.
  • To enhance the estimation performance and communication efficiency of distributed algorithms in WSNs.

Main Methods:

  • Developed an event-triggered adaptive partial diffusion least mean-square algorithm (ET-APDLMS).
  • Employed an adaptive partial diffusion strategy to manage dynamic network topology.
  • Integrated an event-triggered mechanism to minimize data redundancy and conserve communication resources.

Main Results:

  • The ET-APDLMS algorithm demonstrates asymptotic unbiasedness and convergence in mean and mean-square senses.
  • Communication cost analysis confirms resource-saving benefits.
  • Simulations show improved mean-square deviation performance compared to other diffusion algorithms.

Conclusions:

  • The proposed ET-APDLMS algorithm effectively adapts to dynamic WSN topologies.
  • The algorithm significantly reduces communication costs while maintaining estimation performance.
  • Simulation results validate the theoretical analysis and practical effectiveness of ET-APDLMS.