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Wireless Sensor Networks (WSNs) play a crucial role in a variety of applications, from environmental monitoring to smart cities, in an era when the Internet of Things (IoT) is becoming increasingly widespread. However, due to limitations in energy, computing resources, and the requirement for robust data processing capabilities, optimizing these networks for improved performance and efficiency presents significant difficulties. The novel integration of deep learning methods as a means of optimizing WSNs is the subject of this paper. In addition to providing a comprehensive analysis of how deep learning can be used in WSN optimization, this study also identifies best practices and potential areas for future research.

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Estimation and Optimization of Wireless Sensor Network Performance Using Neural Network Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Mahendra Prasad Sharma,
  • Laveena Sehgal

摘要

Wireless Sensor Networks (WSNs) play a crucial role in a variety of applications, from environmental monitoring to smart cities, in an era when the Internet of Things (IoT) is becoming increasingly widespread. However, due to limitations in energy, computing resources, and the requirement for robust data processing capabilities, optimizing these networks for improved performance and efficiency presents significant difficulties. The novel integration of deep learning methods as a means of optimizing WSNs is the subject of this paper. In addition to providing a comprehensive analysis of how deep learning can be used in WSN optimization, this study also identifies best practices and potential areas for future research.