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.

通过低成本边缘物联网架构，提出了实现轻量级机器学习模型估计室内环境质量参数的两种方法，采用神经网络和分布式并行物联网架构，以减少数据处理负载和能源消耗。实验表明，在温度和照度小数据集上进行的训练和测试中，估计性能接近 0.95 的 F 分数和准确度，并且在分布式并行方法中，功耗降低了将近 37%。与相关研究的等效物联网架构相比，达到了相似或稍微更好的性能，并实现了 35% 至 76% 的误差减少，从而在性能和能源效率之间取得了恰到好处的平衡。

边缘计算方法在物联网架构中与机器学习相结合的室内环境参数高效估计的比较

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