Machine Learning Paradigm for Internet of Things Applications

The aim of the book is to explore the benefits of deploying Machine Learning (ML)in Internet of Things (IoT) environment. As a growing number of internet-connected sensors are built into cars, planes, trains and buildings, businesses are amassing vast amounts of data. Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning (ML) -- a subset of the field of artificial intelligence (AI). In order to provide smarter environment, their needs to be implemented IoT with machine learning. Machine learning will allow these smart devices to be smarter in a literal sense. It can analyze the data generated by the connected devices and get an insight into human's behavioral pattern. Hence, it would not be wrong to say that if the IoT is the digital nervous system, then ML acts as its medulla oblongata.


This book provides the state-of-the-art applications of Machine Learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store 'contextualized marketing' and intelligent transportation systems. Readers will gain an insight into the integration of Machine Learning with IoT in various application domains.