Energy and latency reductions at the fog gateway using a machine learning classifier

Machine Learning (ML) techniques have changed the analysis of massive data in the Internet of Things (IoT) environment very effectively. In the IoT theme of applications, reducing latency and energy consumption are the two crucial network Quality of Service (QoS) parameters and the most significant challenges because they directly impact the users' experience. Enabling intelligence at the IoT fog computing framework with ML classifiers' help determines the computing requirements that, in turn, help to execute the vast data collected in the IoT fog computing for real-time operations efficiently. In this paper, the exploration of ML algorithms on the resource constraint IoT fog computing framework and the determination of the suitable ML classifier for reducing latency and energy levels with the usage of ambient sensors in the IoT theme are presented.

Automated summary

The paper discusses how ML algorithms are applied in the resource-constrained IoT fog computing framework, and how ML classifiers are utilized to reduce latency and energy consumption by using ambient sensors in the IoT theme.