基于环境感知的固定路径无人机与地面WSN机会性通信分簇优化算法
Environment-AwareClustering Optimization Algorithm for Fixed-Path UAV and Ground WSN Opportunistic Communication
摘要: 针对东南沿海复杂环境下固定路径无人机与地面无线传感器网络(WSN)协同通信中能耗失衡与抗毁需求并存的问题，提出一种融合环境感知的机会性通信分簇算法(SEB-GFCM-OCT)。算法构建地形–气象–电磁干扰综合影响模型，在模糊聚类框架下融入节点战术价值与连通度约束，并引入基尼系数度量实现簇首能量均衡；同时结合机会窗口的动态频谱感知与轻量化调度，降低重传与无效通信开销。仿真结果表明：所提算法在网络生命周期、能量效率与抗毁覆盖率三项核心指标上均显著优于经典算法。
Abstract: To address the coexistence of energy consumption imbalance and survivability requirements in collaborative communication between fixed-path unmanned aerial vehicles (UAV) and ground wireless sensor networks (WSN) in complex southeast coastal environments, an environment-aware opportunistic communication clustering algorithm (SEB-GFCM-OCT) is proposed. The algorithm constructs a comprehensive impact model of terrain-meteorology-electromagnetic interference, incorporates node tactical value and connectivity constraints within the fuzzy clustering framework, and introduces Gini coefficient metrics to achieve cluster head energy balance. Combined with dynamic spectrum sensing and lightweight scheduling in opportunistic windows, it reduces retransmission and invalid communication overhead. Simulation results show that the proposed algorithm achieves significant improvements in network lifetime, energy efficiency, and survivability coverage compared to classical algorithms.
文章引用：吴迪, 米志超, 路颜霞. 基于环境感知的固定路径无人机与地面WSN机会性通信分簇优化算法[J]. 建模与仿真, 2026, 15(5): 173-184. https://doi.org/10.12677/mos.2026.155081

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