模糊认知图
LPWAN公司
计算机科学
可靠性(半导体)
模糊逻辑
群体行为
图层(电子)
数据挖掘
模糊控制系统
人工智能
功率(物理)
计算机网络
广域网
神经模糊
化学
物理
有机化学
量子力学
作者
Hancong Wang,Wenjie Yin,Yanyi Liu,Yanyi Liu
标识
DOI:10.1109/wcnc55385.2023.10119004
摘要
In order to improve the working efficiency and reliability of energy-harvesting low-power wide area networks (EH-LPWANs) applied in smart forest monitoring, a cross-layer collaboration control model is proposed. Taking the influencing factors of EH-LPWAN as concept nodes, a fuzzy cognitive map (FCM) was devised, and the relationship between each concept was utilized to establish the cross-layer model for optimally satisfying multiple objectives and conflicting constraints. Here, a dynamic FCM scheme based on adaptive glowworm swarm optimization (AGSO) is given to determine the concept weights and sustain online updates. The results show that the energy neutrality of EH nodes and the data throughput of the entire network completely meet the real-time and stability requirements for precise forest monitoring.
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