arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.08810cs.NIcs.AIcs.ARcs.DCcs.ITcs.LGmath.IT
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物联网架构中用于室内环境参数高效估计的边缘计算方法比较(基于机器学习)

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

Jose-Carlos Gamazo-Real, Raul Torres Fernandez, Adrian Murillo Armas

AI总结:

本文提出基于集中式和分布式边缘物联网架构的轻量级机器学习方法,用于估计室内环境质量参数,在保持高精度(F分数和准确率近0.95)的同时,分布式方法降低功耗约37%,误差减少35%至76%。

摘要:

物联网(IoT)设备数量的大幅增加彻底改变了数据处理的方式,加之当前从云端向边缘计算发展的趋势,使得在数据源附近使用高能效设备进行高效、可靠的数据处理成为必要。本文提出了两种基于低成本边缘物联网架构的方法,用于实现轻量级机器学习(ML)模型,以估计室内环境质量(IEQ)参数,例如多层感知器类型的人工神经网络。其实现基于通过无线连接的集中式和分布式并行物联网架构,共享商用现成模块用于数据采集和感知,如温度、湿度、照度、CO2及其他气体传感器。集中式方法使用图形处理单元(GPU)和消息队列遥测传输(MQTT)协议,而分布式方法则利用低性能的基于ARM的设备及消息传递接口(MPI)协议。尽管测量了多个IEQ参数,但ML模型的训练和测试通过专注于小型温度和照度数据集的实验完成,以降低数据处理负载,这些数据集来自突刺、方波轮廓和锯齿测试用例。结果表明,在分布式并行方法中,F分数和准确率接近0.95,实现了高估计性能,并获得了接近理论值的加速比,功耗降低了约37%。此外,与相关研究中等效的物联网架构相比,性能相当或略优,但误差减少了35%至76%,在性能与能效之间取得了适当的平衡。

英文摘要:

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO2, and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35 to 76% is accomplished with an adequate balance between performance and energy efficiency.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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