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
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.