Fog Computing

12Distributed Machine Learning for IoT Applications in the Fog

Aluizio F. Rocha Neto¹, Flavia C. Delicato², Thais V. Batista¹, and Paulo F. Pires²

¹DIMAp, Federal University of Rio Grande do Norte, Natal, Brazil

²PGC/IC/UFF, Fluminense, Federal University, Niterói, Brazil

12.1 Introduction

The term Internet of Things (IoT) was coined by the British Kevin Aston in 1999 [1] and since then we have seen such a paradigm come about thanks to the evolution of several enabling technologies. IoT advocates a reality in which the physical and virtual worlds mingle through rich interactions. Its boundaries become almost invisible, so as to be possible on the one hand, the augment of the physical world with virtual information, and, on the other hand, the extension of the virtual world to encompass concrete objects. To do so, the first step is to instrument physical entities, through sensors, capable of acquiring various types of environmental variables, and actuators, capable of changing the state of physical objects. By instrumenting objects, they become endowed with the ability of perceiving the surrounding world and are generally denoted as intelligent or smart objects. However, the perception capability is only a small portion of what we call intelligence. Data on perceived phenomena can be partially processed locally (in the smart objects themselves) and then transmitted (usually through wireless interfaces) until eventually they are available on the Internet via virtual representations ...