Scholarlink

<p>Device-to-device (D2D) communication offers an infrastructure-independent solution for post-disaster connectivity, but conventional clustering methods often suffer from load imbalance, premature energy depletion, and unstable connectivity. This paper proposes an Adaptive Fuzzy–PSO Unequal Clustering (AFPUC) framework that integrates fuzzy-logic-based cluster-head selection, feedback-driven reclustering, event-triggered PSO reselection, and energy-aware multi-hop routing within a closed-loop adaptive framework. Unlike conventional approaches that treat clustering and adaptation separately, AFPUC jointly optimises resilience, load balancing, and energy sustainability. Simulation results under sparse and dense deployments, benchmarked against six state-of-the-art clustering algorithms, show that AFPUC achieves up to 89.5% and 94.45% node survivability, limits energy consumption to 9.94% and 5.22%, and attains up to 100% network-layer packet delivery under the adopted routing abstraction. Statistical analysis using ANOVA and Tukey’s HSD confirms significant performance improvements (<i>p</i> &lt; 0.001), while runtime analysis indicates manageable optimisation overhead. These results demonstrate that AFPUC provides a robust, computationally practical solution for reliable, energy-efficient disaster-oriented D2D communication.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive fuzzy–PSO unequal clustering for energy-aware D2D disaster networks

  • 期刊论文

  • 发表日期：

    2026年7月22日

查看全文
  • Norhisham Mansor,
  • Wahidah Md Shah,
  • Aslinda Hassan,
  • Najwan Khambari,
  • Ali Abdul-hussian Hassan,
  • Affero Ismail

摘要

Device-to-device (D2D) communication offers an infrastructure-independent solution for post-disaster connectivity, but conventional clustering methods often suffer from load imbalance, premature energy depletion, and unstable connectivity. This paper proposes an Adaptive Fuzzy–PSO Unequal Clustering (AFPUC) framework that integrates fuzzy-logic-based cluster-head selection, feedback-driven reclustering, event-triggered PSO reselection, and energy-aware multi-hop routing within a closed-loop adaptive framework. Unlike conventional approaches that treat clustering and adaptation separately, AFPUC jointly optimises resilience, load balancing, and energy sustainability. Simulation results under sparse and dense deployments, benchmarked against six state-of-the-art clustering algorithms, show that AFPUC achieves up to 89.5% and 94.45% node survivability, limits energy consumption to 9.94% and 5.22%, and attains up to 100% network-layer packet delivery under the adopted routing abstraction. Statistical analysis using ANOVA and Tukey’s HSD confirms significant performance improvements (p < 0.001), while runtime analysis indicates manageable optimisation overhead. These results demonstrate that AFPUC provides a robust, computationally practical solution for reliable, energy-efficient disaster-oriented D2D communication.