Scholarlink

Wireless sensor networks (WSNs) have emerged as a crucial technology for various applications including environmental monitoring, healthcare, and industrial automation. With the proliferation of sensor nodes in WSNs, there is a growing need for efficient data processing and management techniques. Machine learning (ML) has shown promise in addressing the diverse requirements of WSNs, including data routing, energy management, and fault detection. This review paper explores the requirements of ML techniques in WSNs, discusses the limitations of applying ML in such networks, and provides a brief introduction to ML techniques in the context of WSNs. Furthermore, the paper examines the challenges encountered in WSNs and ML techniques, particularly focusing on routing issues. Finally, various ML techniques employed in WSNs routing are surveyed, highlighting their advantages and limitations.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimizing Energy Efficiency in Wireless Sensor Networks Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • A. Hemalatha Reddy,
  • Jalapala Sinjini,
  • M. Priyadharshini,
  • V. Indumathi,
  • V. V. Bhavani,
  • Tahseen Jahan

摘要

Wireless sensor networks (WSNs) have emerged as a crucial technology for various applications including environmental monitoring, healthcare, and industrial automation. With the proliferation of sensor nodes in WSNs, there is a growing need for efficient data processing and management techniques. Machine learning (ML) has shown promise in addressing the diverse requirements of WSNs, including data routing, energy management, and fault detection. This review paper explores the requirements of ML techniques in WSNs, discusses the limitations of applying ML in such networks, and provides a brief introduction to ML techniques in the context of WSNs. Furthermore, the paper examines the challenges encountered in WSNs and ML techniques, particularly focusing on routing issues. Finally, various ML techniques employed in WSNs routing are surveyed, highlighting their advantages and limitations.