<p>This study introduces an advanced adaptive sensor clustering technique for environmental monitoring in dynamic forest ecosystems, focusing on optimizing Wireless Sensor Networks (WSNs) for energy efficiency, adaptability, and data accuracy. The framework integrates Quantum Fuzzy C-Means (QFCM) clustering, energy-efficient cluster head selection, and reinforcement learning for predictive adaptation, enabling dynamic responses to environmental changes. Results from simulations demonstrate the framework’s effectiveness, achieving significant improvements in energy conservation, data accuracy, and network robustness. Sensitivity analysis reveals that node death rates are most impacted during initial operational phases, with FND showing the steepest decline (sensitivity coefficient: <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12083_2025_1974_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">$-0.989$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>-</mo> <mn>0.989</mn> </mrow> </math></EquationSource> </InlineEquation>, R-squared: 0.979, <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12083_2025_1974_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="70" /> </InlineMediaObject> <EquationSource Format="TEX">$p < 0.001$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>p</mi> <mo><</mo> <mn>0.001</mn> </mrow> </math></EquationSource> </InlineEquation>). The framework effectively extends operational lifespan by optimizing energy usage, with energy efficiency improving from 50 bits/Joule to 300 bits/Joule as Signal-to-Noise Ratio (SNR) improves. Furthermore, the system demonstrates scalability by maintaining efficient operation even as network density increases, showing its potential for advanced WSN applications in forest monitoring. These findings highlight the framework’s ability to address challenges in traditional WSN solutions, offering a scalable and sustainable approach to monitoring dynamic ecosystems.</p>

Adaptive sensor clustering for environmental monitoring in dynamic forest ecosystems

期刊论文
发表日期：

2025年4月16日

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Robertas Damaševičius,
Rytis Maskeliūnas

摘要

This study introduces an advanced adaptive sensor clustering technique for environmental monitoring in dynamic forest ecosystems, focusing on optimizing Wireless Sensor Networks (WSNs) for energy efficiency, adaptability, and data accuracy. The framework integrates Quantum Fuzzy C-Means (QFCM) clustering, energy-efficient cluster head selection, and reinforcement learning for predictive adaptation, enabling dynamic responses to environmental changes. Results from simulations demonstrate the framework’s effectiveness, achieving significant improvements in energy conservation, data accuracy, and network robustness. Sensitivity analysis reveals that node death rates are most impacted during initial operational phases, with FND showing the steepest decline (sensitivity coefficient: $-0.989$ $- 0.989$ , R-squared: 0.979, $p < 0.001$ $p < 0.001$ ). The framework effectively extends operational lifespan by optimizing energy usage, with energy efficiency improving from 50 bits/Joule to 300 bits/Joule as Signal-to-Noise Ratio (SNR) improves. Furthermore, the system demonstrates scalability by maintaining efficient operation even as network density increases, showing its potential for advanced WSN applications in forest monitoring. These findings highlight the framework’s ability to address challenges in traditional WSN solutions, offering a scalable and sustainable approach to monitoring dynamic ecosystems.