The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning Approaches for Predictive Maintenance in Wireless Sensor Networks
|
|
Author(s): Sathya Selvaraj Sinnasamy (SRM Institute of Science and Technology, Ramapuram, India), S. Kamaleswari (SRM Institute of Science and Technology, Ramapuram, India), U. Surendar (SRM Institute of Science and Technology, Ramapuram, India), Biswaranjan Senapati (University of Arkansas at Little Rock, USA), B. Vaidianathan (Dhaanish Ahmed College of Engineering, India)and M. Gandhi (Dhaanish Ahmed College of Engineering, India)
Copyright: 2026
Pages: 14
Source title:
Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems
Source Author(s)/Editor(s): Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand), Karthikeyan Chinnusamy (Veritas, USA), Joseph Jeganathan (University of Bahrain, Bahrain), Ahmed J. Obaid (University of Kufa, Iraq)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3373-4672-4.ch002
Purchase
|
Abstract
Wireless Sensor Networks (WSNs) have permeated various domains, such as industrial automation, healthcare, and environmental monitoring, due to their ability to effectively collect and communicate data in diverse environments. The reliability and durability of WSNs have become paramount, particularly in critical applications. Predictive maintenance, which anticipates faults and mitigates them preemptively, emerges as a pivotal strategy to enhance network longevity and reliability. This paper explores using machine learning (ML) approaches for predictive maintenance in WSNs. Various ML algorithms, including regression, classification, and clustering, are investigated in terms of their efficacy in predicting and diagnosing issues within the network, thereby facilitating timely interventions. The paper contributes a novel architecture integrating ML models into WSNs to monitor, analyze, and predict potential failures, ensuring optimal network functionality.
Related Content
|
P. V. Naveen, A. Poongodi.
© 2026.
24 pages.
|
|
Sathya Selvaraj Sinnasamy, S. Kamaleswari, U. Surendar, Biswaranjan Senapati, B. Vaidianathan, M. Gandhi.
© 2026.
14 pages.
|
|
B. Aarthi, A. Smruthi, Pamireddy Thanishka, G. Sakthi Prasanna, P. Mahendran.
© 2026.
18 pages.
|
|
R. Radhika, A. Muthukumaravel.
© 2026.
24 pages.
|
|
R. Regin, K. Lalith Reddy, R. Sanjay Narayanan, Y. Likhith Srinivas, R. Steffi, S. Saranya, S. R. Saranya.
© 2026.
26 pages.
|
|
R. Saranya, S. Silvia Priscila.
© 2026.
20 pages.
|
|
Manjunath Singh H., R. Tanuja.
© 2026.
28 pages.
|
|
|