AI 中文总结
本文通过系统综述2023-2026年50篇相关研究，分析AI对关键任务环境中节能型WSN的优化作用，指出需构建适配实际场景的轻量可解释安全AI驱动WSN架构。
AI 中文摘要
由人工智能驱动的先进无线传感器网络，越来越多地被应用于需要持续监测、自主运行、可靠通信及快速决策支持的场景。本系统综述针对2023至2026年期间，关键任务环境中节能型AI无线传感器网络（WSNs）的最新研究，重点关注电力电子、智能电网及城市基础设施系统。作者合成了符合纳入标准的50篇DOI索引研究文献，对其进行定性主题编码和比较分析；其他参考文献仅用于提供历史、方法或技术背景，未纳入系统综述的文献 corpus。研究结果表明，AI可通过路由与分簇、边缘AI、强化学习、模糊逻辑、元启发式优化及基于AI的安全措施，改善WSN的能耗表现。同时，节能效率不能被视为孤立的性能指标，在关键任务系统中，安全性、延迟和可靠性是紧密关联的需求。综述总结指出，未来研究应脱离孤立优化协议的思路，转而聚焦于构建适用于实际运行环境的轻量、可解释、安全且经实地测试的AI驱动WSN架构。
英文摘要
Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics, and urban infrastructure systems. The authors synthesise a corpus of 50 DOI indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimisation, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimising protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments.
Comments25 pages, 3 figures, 9 tables, 80 references
