低功耗嵌入式传感器节点用于实时环境监测,搭载机器学习推理
1Engineering Department, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种低功耗的传感器节点,用于使用机器学习进行实时环境监测. 该设计优化了能源效率,并减少了智能城市应用程序的数据传输.
科学领域:
- 嵌入式系统工程 嵌入式系统工程
- 环境监测技术 环境监测技术
- 机器学习在边缘的边缘
背景情况:
- 实时环境监测需要有效的数据采集和处理.
- 现有的传感器节点经常面临电力限制和高数据传输成本.
- 车载机器学习为本地化数据分析和减少网络负载提供了潜力.
研究的目的:
- 设计和优化低功耗嵌入式传感器节点架构.
- 整合空气质量和环境参数的异质传感.
- 为实时事件/异常检测实施车载机器学习.
主要方法:
- 开发一个模块化嵌入式平台,配备低功耗微控制器和神经推理加速器.
- 端到端的能源优化使用自适应性工作循环,分层功率域和边缘级数据减少.
- 在设备上使用量子化神经模型和MATLAB Simulink模拟进行系统分析进行多类分类.
主要成果:
- 实现了94%的推断准确度,延迟时间为0.87毫秒.
- 在能源自主运行中,已证明平均功耗约为2.9mWh.
- 与定期报告相比,自适应的LoRaWAN通信减少了~88%的数据传输.
结论:
- 拟议的架构使节能环境传感与设备上的推断成为可能.
- 在设备上的推断有效地减少了网络流量,同时保持了可靠的事件检测.
- 该系统支持可持续的智能城市和气候监测部署.
关键词:
洛拉旺人 洛拉旺人边缘的人工智能 边缘的人工智能嵌入式传感器系统 嵌入式传感器系统环境监测 环境监测 环境监测低功率的设计设计.微控制器架构中的微控制器架构.在设备上推断推断.量子化神经网络是一种量子化神经网络.更多相关视频
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