Distributed Machine Learning for IoT Applications in the Fog

The Internet of Things (IoT) is well known for being a major source of Big Data since it builds on the connection to the Internet of a huge number of smart devices that continuously report the status of their physical environments. Although the IoT paradigm is focused on the connection of objects, its real potential lies not in the objects themselves, but in the ability to generate valuable knowledge from the data extracted from these objects. It can be said that the Internet of Things is actually not about
things
, but about
data
. In this context, machine learning (ML) is a promising technique to process the generated data and transform them into information, knowledge, predict trends, produce valuable insights, and guide automated decision‐making processes. However, the use of ML techniques in IoT brings up several challenges, especially regarding the computational requirements demanded by them. Data produced by IoT devices can be typically processed in three different layers: in the data sources (Things layer), in computational clouds, and in the intermediate layer known as fog or edge. Depending on the application quality of service (QoS) parameters (such as response time) and the complexity of the required processing, each layer will be more suitable for performing a given ML technique. The overhead imposed by current ML techniques on devices of the fog and things layers, where resources are scarce, hinders their widespread adoption in this context. Recent developments in running ML algorithms on fog devices show that, due to their limited hardware and power supply, the decision‐making process in the fog is not an easy task. On the other hand, a decision‐making process built upon information extracted from several devices on the network is more reliable than by taking some limited view of the context. This chapter presents the challenges to perform big data/big stream analytics in fog computing using the recent artificial intelligence developments.