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Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to: • Information Sciences • Optimization Sciences • Control Theory • Operational Research • Decision Sciences • Information Theory • Information Technology • Computer Networks and Communications • Mathematical Programming • Modelling and Simulation • Database Management • Applications to Engineering Sciences • Applications to Technology

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Open Access Research Article

Energy-efficient protocols for wireless sensor networks optimizing power consumption for prolonged network lifespan

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pp. 1879–1886Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2280XML
Received:
01 Apr 2025
Published Online:
01 May 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2280
Pages:
1879–1886

Abstract

There is a constrained in wireless sensor network by the limited battery as a resources, make efficiently use of energy efficiency in design protocol. Premature node death reduces coverage, connectivity, and data reliability, thereby limiting the operational lifespan of the network. To address this challenge, we propose a novel protocol named Adaptive Duty-Cycled Clustering and Routing for WSNs (ADCR-WSN), which integrates energy-aware cluster head election, duty-cycled intra-cluster communication, and gradient-based multi-hop inter-cluster routing. Unlike existing protocols that optimize clustering, routing, or duty cycling in isolation, ADCR-WSN employs a lightweight feedback controller to dynamically adjust duty cycles based on real-time residual energy, ensuring balanced energy consumption across the network. Using a first-order radio model, ADCR-WSN is evaluated against canonical protocols including LEACH, HEED, TEEN, and PEGASIS. 

Keywords

Subject Classifications

31A1033E30

References

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