Optimizing Energy-Efficient Clustering Algorithms for Prolonged Lifetime in WSN-IoT Deployments

They noticed that energy efficiency is still a difficult problem in wireless WSNs that act in IoT conditions. The importance of clustering algorithms has raised as the approach to extending the lifetime of networks and the utilization of resources. This research aims to discover a range of new clust...

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Vydáno v:2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Ročník 3; s. 1 - 6
Hlavní autoři: Sunil, G, Tuteja, Gaurav, Nasra, Parul, Abbas, Haayder M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.03.2025
Témata:
Adaptation models
Adaptive Clustering Algorithms
Clustering algorithms
Energy efficiency
Energy-Efficient Clustering
Internet of Things
Internet of Things (IoT)
Network Lifetime Optimization
Protocols
Scalability
Smart cities
Technological innovation
Wireless communication
Wireless sensor networks
Wireless Sensor Networks (WSN)
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Shrnutí:They noticed that energy efficiency is still a difficult problem in wireless WSNs that act in IoT conditions. The importance of clustering algorithms has raised as the approach to extending the lifetime of networks and the utilization of resources. This research aims to discover a range of new clustering techniques and the consequences which are presented by such procedures for energy-efficient placement of WSN-IoT. These algorithms are judged for energy consumption, computational intensity, and flexibility and scalability of the networks. In this paper, several applications such as environmental monitoring, precision agriculture, and smart cities are analyzed. The study also aimed at pointing at the pros and cons of the different methodologies which are being used. From the results of the present work, we get the understanding of the tendencies in clustering approaches evolution to meet the IoT demands and future research prospects will concern the development of hybrid models and adaptive algorithms.
DOI:10.1109/IATMSI64286.2025.10985146