Scholarlink

<p>Wireless Sensor Networks (WSNs) face fundamental engineering challenges due to resource-constrained sensor nodes with limited battery capacity, processing power, and memory, necessitating optimal energy management to maximize network operational lifetime. Clustering has emerged as an effective topology management strategy to enhance energy efficiency, but existing approaches suffer from inadequate adaptation to dynamic network conditions and suboptimal cluster head (CH) selection. This work presents a novel hybrid framework that integrates Adaptive Fuzzy Logic with Stackelberg Evolutionary Game Theory (AF-SEGT) for CH selection and the Narwhal Optimizer Algorithm (NOA) for energy-efficient routing. The adaptive fuzzy logic system dynamically computes CH suitability scores using real-time network parameters including residual energy, node degree, mobility, trust metrics, and centrality, while the Stackelberg game-theoretic model frames CH selection as a leader–follower interaction that achieves equilibrium for optimal energy distribution. The NOA metaheuristic, inspired by narwhal echolocation behavior, discovers reliable routing paths by optimizing multiple conflicting objectives: energy consumption, path reliability, and network congestion. Extensive MATLAB simulations show that the proposed framework achieves 99% packet delivery ratio, maintains 0.4&#xa0;J residual energy, reduces average end-to-end delay to 2.1&#xa0;ms, and significantly improves network lifetime compared to existing protocols. The results validate the framework's effectiveness in addressing the core engineering challenge of energy-efficient WSN operation.</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 logic-based energy-efficient clustering and routing in wireless sensor networks using Stackelberg game theory and narwhal optimization

  • 期刊论文

  • 发表日期：

    2026年6月23日

查看全文
  • M. Vivek Kumar,
  • O. Saraniya

摘要

Wireless Sensor Networks (WSNs) face fundamental engineering challenges due to resource-constrained sensor nodes with limited battery capacity, processing power, and memory, necessitating optimal energy management to maximize network operational lifetime. Clustering has emerged as an effective topology management strategy to enhance energy efficiency, but existing approaches suffer from inadequate adaptation to dynamic network conditions and suboptimal cluster head (CH) selection. This work presents a novel hybrid framework that integrates Adaptive Fuzzy Logic with Stackelberg Evolutionary Game Theory (AF-SEGT) for CH selection and the Narwhal Optimizer Algorithm (NOA) for energy-efficient routing. The adaptive fuzzy logic system dynamically computes CH suitability scores using real-time network parameters including residual energy, node degree, mobility, trust metrics, and centrality, while the Stackelberg game-theoretic model frames CH selection as a leader–follower interaction that achieves equilibrium for optimal energy distribution. The NOA metaheuristic, inspired by narwhal echolocation behavior, discovers reliable routing paths by optimizing multiple conflicting objectives: energy consumption, path reliability, and network congestion. Extensive MATLAB simulations show that the proposed framework achieves 99% packet delivery ratio, maintains 0.4 J residual energy, reduces average end-to-end delay to 2.1 ms, and significantly improves network lifetime compared to existing protocols. The results validate the framework's effectiveness in addressing the core engineering challenge of energy-efficient WSN operation.