• Ashwini Gundopant Dhumal
• Mairaj U. Inamdar

Wireless Sensor Networks (WSNs) play a crucial role in various domains, including environmental monitoring, healthcare, surveillance, and industrial automation. With the increasing complexity and scale of WSNs, there is a growing need for efficient data processing and decision-making mechanisms. Machine learning techniques have emerged as powerful tools to leverage the vast amount of data collected by WSNs and extract valuable insights. This research paper provides a comprehensive review and analysis of machine learning techniques applied to WSNs. The paper begins by introducing the fundamental concepts of WSNs, including their architecture, and energy constraints. It then delves into the potential applications and challenges faced by WSNs, such as limited resources, data heterogeneity, and dynamic environments, security. A detailed survey of machine learning techniques suitable for WSNs is presented, including supervised learning, unsupervised learning, and reinforcement learning. Various algorithms within these categories, such as decision trees, support vector machines, clustering methods, and deep learning models, are discussed in terms of their applicability and performance in WSNs. It explores how machine learning algorithms can enhance network performance, optimize energy consumption, and improve data analysis capabilities in WSNs.

Wireless sensor networks, Machine learning, security

"MACHINE LEARNING TECHNIQUES FOR WIRELESS SENSOR NETWORKS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.b94-b97, June-2024, Available :http://www.jetir.org/papers/JETIR2406112.pdf

"MACHINE LEARNING TECHNIQUES FOR WIRELESS SENSOR NETWORKS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppb94-b97, June-2024, Available at : http://www.jetir.org/papers/JETIR2406112.pdf