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

arXiv:2403.08810v1 Announce Type: cross
Abstract: 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 …

abstract architectures arxiv cloud comparison computing cs.ai cs.ar cs.dc cs.it cs.lg cs.ni current data devices edge edge computing environmental internet internet of things iot machine machine learning math.it parameters the way trend type