English(EN) Exposure-Length Trade-off Optimization for Obstacle-Aware Intrusion Paths in Heterogeneous Wireless Sensor Networks

新算法优化无线传感器网络中的入侵路径

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-01 05:47

研究人员开发了一种新算法OG-MOEA/D，旨在优化异构无线传感器网络中的入侵路径。该方法解决了规划路径的挑战，该路径在避开障碍物的同时最大化传感暴露并最小化长度。广泛的模拟表明，OG-MOEA/D在生成分布良好的帕累托前沿和提高超体积方面，始终优于MOEA/D和NSGA-II等现有算法。 AI

影响这项研究可能导致物联网环境中更有效的监控和入侵检测系统。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种针对特定技术问题的新算法。[lever_c_demoted from research: ic=1 ai=0.4]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Dang Trong Hop · 2026-11-01 05:47

Exposure-Length Trade-off Optimization for Obstacle-Aware Intrusion Paths in Heterogeneous Wireless Sensor Networks

Barrier coverage is a key paradigm for intrusion detection and surveillance in IoT-enabled heterogeneous wireless sensor networks, where monitoring quality depends not only on the existence of sensing barriers but also on the detectability of feasible crossing paths. In this pape…