基于雾计算的智能农场系统在物体环境的人工智能中的实施
Sukjun Hong1, Seongchan Park2, Heejun Youn2
1Department of Smart System, Graduate School of Smart Convergence, Kwangwoon University, Seoul 01897, Republic of Korea.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
雾计算显著减少了智能农场物联网 (IoT) 系统中的数据量. 这种使用MQTT等协议的AIoT方法提高了效率,并使可靠的咖啡成熟度检测成为可能.
科学领域:
- 计算机科学 计算机科学
- 农业技术 农业技术
- 人工智能的人工智能
背景情况:
- 云计算面临着物联网 (IoT) 应用中大规模数据传输的挑战.
- 雾计算提供了一个分散的解决方案,以减轻云计算的开销.
- 智能农业需要高效的数据处理来实现实时决策.
研究的目的:
- 在智能农场环境中实施和评估使用雾计算的物体人工智能 (AIoT) 系统.
- 评估雾计算对网络流量量和通信协议性能的影响.
- 开发和验证一个人工智能驱动的算法来确定咖啡树的成熟度水平.
主要方法:
- 一个基于雾计算的AIoT系统被部署在一个咖啡树农场.
- 网络流量在雾和非雾系统之间进行比较.
- 评估了超文本传输协议 (HTTP),消息队列遥测传输 (MQTT) 和受限制应用协议 (CoAP) 的性能.
- 卷积神经网络 (CNN) 模型用于咖啡果成熟度分类.
主要成果:
- 与非雾系统相比,雾计算系统在累积数据量上实现了26%的减少.
- 消息队列遥测运输 (MQTT) 在数据量和丢失率方面表现稳定.
- 基于CNN的算法为确定咖啡水果成熟度水平提供了可靠的结果.
结论:
- 雾计算有效地减少了智能农场AIoT应用中的数据传输开销.
- 在这种情况下,MQTT是一个适合稳定数据传输的协议.
- 人工智能和雾计算集成为智能农业优化提供了一个有希望的方法.
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