Leveraging neural networks (RNN_LSTM) in enhancing energy efficiency and network lifetime in WSNs

Sujeet Kumar; Sakshi Pandey; Rajesh Singh; Kamal Sharma; Rajesh Mahadeva; Vijaya Rama Raju; Violetta V. Politi; Hayidr Muhamed; Kseniia Iurevna Usanova

doi:10.1007/s41870-025-02675-x

Leveraging neural networks (RNN_LSTM) in enhancing energy efficiency and network lifetime in WSNs

Sujeet Kumar^*, Sakshi Pandey^*, Rajesh Singh^*, Kamal Sharma, Rajesh Mahadeva, Vijaya Rama Raju, Violetta V. Politi, Hayidr Muhamed, Kseniia Iurevna Usanova

^*Corresponding author for this work

School of Computer Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Wireless Sensor Networks (WSNs) are gaining popularity due to its multiple applications in gathering and monitoring data in varying environments. However, they are associated with a few challenges such as lower network lifetime, lower resource utilization and many more. To overcome this, a hybrid deep learning approach is introduced that utilizes Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation and Cluster Head (CH) selection. By doing away with the need to constantly solve intricate optimization issues inside each channel coherence time, this method drastically lowers computing complexity as compared to conventional optimization-based techniques. Numerical findings show that RNN_LSTM exhibits superior performance in terms of energy efficiency (EE) (89.03%), network stability (NS) (97.47%), network scalability (NSC) (93.76%), and QoS (91.15%) when compared to existing studies.

Original language	English
Pages (from-to)	4925-4930
Number of pages	6
Journal	International Journal of Information Technology (Singapore)
Volume	17
Issue number	8
DOIs	https://doi.org/10.1007/s41870-025-02675-x
Publication status	Accepted/In press - 2025

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Computer Networks and Communications
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s41870-025-02675-x

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title = "Leveraging neural networks (RNN\_LSTM) in enhancing energy efficiency and network lifetime in WSNs",

abstract = "Wireless Sensor Networks (WSNs) are gaining popularity due to its multiple applications in gathering and monitoring data in varying environments. However, they are associated with a few challenges such as lower network lifetime, lower resource utilization and many more. To overcome this, a hybrid deep learning approach is introduced that utilizes Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation and Cluster Head (CH) selection. By doing away with the need to constantly solve intricate optimization issues inside each channel coherence time, this method drastically lowers computing complexity as compared to conventional optimization-based techniques. Numerical findings show that RNN\_LSTM exhibits superior performance in terms of energy efficiency (EE) (89.03\%), network stability (NS) (97.47\%), network scalability (NSC) (93.76\%), and QoS (91.15\%) when compared to existing studies.",

author = "Sujeet Kumar and Sakshi Pandey and Rajesh Singh and Kamal Sharma and Rajesh Mahadeva and \{Rama Raju\}, Vijaya and \{V. Politi\}, Violetta and Hayidr Muhamed and \{Iurevna Usanova\}, Kseniia",

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year = "2025",

doi = "10.1007/s41870-025-02675-x",

language = "English",

volume = "17",

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TY - JOUR

T1 - Leveraging neural networks (RNN_LSTM) in enhancing energy efficiency and network lifetime in WSNs

AU - Kumar, Sujeet

AU - Pandey, Sakshi

AU - Singh, Rajesh

AU - Sharma, Kamal

AU - Mahadeva, Rajesh

AU - Rama Raju, Vijaya

AU - V. Politi, Violetta

AU - Muhamed, Hayidr

AU - Iurevna Usanova, Kseniia

PY - 2025

Y1 - 2025

N2 - Wireless Sensor Networks (WSNs) are gaining popularity due to its multiple applications in gathering and monitoring data in varying environments. However, they are associated with a few challenges such as lower network lifetime, lower resource utilization and many more. To overcome this, a hybrid deep learning approach is introduced that utilizes Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation and Cluster Head (CH) selection. By doing away with the need to constantly solve intricate optimization issues inside each channel coherence time, this method drastically lowers computing complexity as compared to conventional optimization-based techniques. Numerical findings show that RNN_LSTM exhibits superior performance in terms of energy efficiency (EE) (89.03%), network stability (NS) (97.47%), network scalability (NSC) (93.76%), and QoS (91.15%) when compared to existing studies.

AB - Wireless Sensor Networks (WSNs) are gaining popularity due to its multiple applications in gathering and monitoring data in varying environments. However, they are associated with a few challenges such as lower network lifetime, lower resource utilization and many more. To overcome this, a hybrid deep learning approach is introduced that utilizes Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation and Cluster Head (CH) selection. By doing away with the need to constantly solve intricate optimization issues inside each channel coherence time, this method drastically lowers computing complexity as compared to conventional optimization-based techniques. Numerical findings show that RNN_LSTM exhibits superior performance in terms of energy efficiency (EE) (89.03%), network stability (NS) (97.47%), network scalability (NSC) (93.76%), and QoS (91.15%) when compared to existing studies.

UR - https://www.scopus.com/pages/publications/105012951460

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DO - 10.1007/s41870-025-02675-x

M3 - Article

AN - SCOPUS:105012951460

SN - 2511-2104

VL - 17

SP - 4925

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JO - International Journal of Information Technology (Singapore)

JF - International Journal of Information Technology (Singapore)

IS - 8

ER -

Leveraging neural networks (RNN_LSTM) in enhancing energy efficiency and network lifetime in WSNs

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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