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Optimizing IoT Applications with AI-Enhanced Wireless Sensor Networks

Nagilla Laxmaiah1 Masku Naveen Kumar2 Danavath Seva3 Poloju Mahesh4 Kurva Thirumalesh5

1 2 5Department of CSE, Scient Institute of Technology, Hyderabad, Telangana, India. 3 4 Department of ECE, Scient Institute of Technology, Hyderabad, Telangana, India.

Published Online: May-August 2024

Pages: 22-25

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Abstract

This paper provides a comprehensive review of the integration of artificial intelligence (AI) with wireless sensor networks (WSNs), highlighting the transformative potential of this convergence in data collection and environmental monitoring. By combining self-configured sensor nodes with advanced AI techniques, the integration enhances adaptability, energy efficiency, and real-time decision-making capabilities. Various AI methodologies, including machine learning, deep learning, and evolutionary algorithms, are explored for their roles in optimizing network performance and resource management. The paper also addresses critical challenges such as processing limitations, security vulnerabilities, and the complexity of interoperability. Key applications across healthcare, smart agriculture, and environmental monitoring are discussed, illustrating the practical benefits of this integration. Finally, the paper outlines future perspectives, emphasizing the role of emerging technologies in overcoming existing barriers and further enhancing the functionality of AI integrated WSNs. This integration is poised to revolutionize smart environments, offering innovative solutions for continuous monitoring and efficient resource management

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Citations

Nagilla Laxmaiah, Masku Naveen Kumar, Danavath Seva, Poloju Mahesh, Kurva Thirumalesh, “Optimizing Iot Applications with AI-Enhanced Wireless Sensor Networks”, Indian Journal of Electronics and Communication Engineering, Volume 01, Issue 02, May-August 2024, PP: 22-25.

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© 2026 The Author(s). Published by Fifth Dimension Research Publication.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

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