ABSTRACT
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Internet of Things (IoT) is a system of connected physical objects, accessible through the Internet without human intervention, service providers, or establishments. The advancement of information technology (IT) has resulted in physical devices getting connected through the Internet with the potentiality for a device to be recognized by other devices. The IoT, one of the most prominent constituents of ubiquitous wireless communication, necessitates embedding computational potential linking to each other devices in multiple systems. As IoT is used in automated and living spaces, including homes and workplaces, ubiquitous wireless communication (UWC) integrates both wired and wireless technologies. The applications of big data analytics have increased rapidly in the past few years and resulted in next-generation intelligence. Practicable applications span from probably administration of assembling infrastructure in a remote location to supervising the environment and detecting malicious tasks. Machine learning proves to be a mandatory tool for big data, as the volume, the variety, and the velocity of data are frequently improvising the potentialities of typical data analytics. Deep learning is a contemporary blooming branch of machine learning that increases the learning performance via multiple layers of processing. This chapter aims to address the concerns encompassing IoT devices, their interrelations, services they may offer, including the efficient analysis of big data produced by IoT with machine learning techniques, and models that will develop IoT to become part and parcel of everyday life.
