Revolutionize Your Technology in Wireless Sensor Networks Using Machine Learning "Algorithms, Strategies and Applications"

Authors

  • Farshid Vazifehdoost Departmant of Computer Engineering, Artificial Intelligence and Robotics, Payame Noor University International Center, Iran.
  • Somayeh Kadkhoda Dehkhani Departmant of Computer Engineering, Artificial Intelligence and Robotics, Payame Noor University International Center, Iran.
  • Shirin Khezri * Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran.

https://doi.org/10.22105/raise.v1i2.45

Abstract

This research work explores the use of Machine Learning (ML) techniques in Wireless Sensor Networks (WSNs) to address rapidly changing environmental conditions and optimize resource utilization. Through a comparative evaluation of different machine learning algorithms, this work provides a guide for WSN designers to develop effective and practical solutions for their specific application problems. Results demonstrate the potential of machine learning to improve performance, energy efficiency, and scalability in WSNs. However, the use of machine learning techniques also presents certain challenges, such as the need for large amounts of data and the risk of overfitting. This research highlights the importance of careful consideration of these challenges when implementing machine learning techniques in WSNs. Overall, this research work provides insights into the potential of machine learning to enhance the capabilities of WSNs and opens up new avenues for future research.

Keywords:

Wireless sensor network, Machine learning, Internet of things, Neural network

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Published

2024-09-02

Issue

Vol. 1 No. 2 (2024): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Revolutionize Your Technology in Wireless Sensor Networks Using Machine Learning "Algorithms, Strategies and Applications". (2024). Research Annals of Industrial and Systems Engineering, 1(2), 106-116. https://doi.org/10.22105/raise.v1i2.45