Machine Learning Paradigm for Internet of Things Applications

11Opportunities and Challenges in Machine Learning With IoT

Sarvesh Tanwar, Jatin Garg*, Medini Gupta^† and Ajay Rana

Amity Institute of Information Technology, Amity University Uttar Pradesh, Noida, India

Abstract

Machine learning (ML) is swiftly being used in wide range of applications. It has risen to popularity in recent years, owing in part to the emergence of big data. With respect to big data, ML techniques are more promising than ever before. Big data helps ML algorithms to discover finer-grained trends and make more precise and reliable predictions than ever before; however, it also raises significant obstacles for ML, such as model scalability and distributed computation. We have discussed about coupling ML with IoT, its applications, and challenges. We designed a framework MLBiD (Machine Learning Based on Big Data) and discussed about deliberation of its potential opportunities and defiant challenges. The architecture is focused on ML, which is split into three phases: preprocessing, learning, and assessment. Furthermore, it includes four other components in the framework: big data, consumer, domain, and system. Various different stages of ML as well as the components of MLBiD are stepping ahead for identifying related opportunities and challenges, and they have opened the doors for potential research analysis in a variety of previously untraveled or under expedition.

Keywords: Machine learning, IoT, supervised learning, unsupervised learning, big data, data processing, ...