This chapter brings out the perspective outcomes of combining three terminologies: artificial intelligence, cloud, and internet of things. The relation between artificial intelligence, machine learning, and deep learning is also emphasized. Intelligence, which is the capability to attain and apply knowledge in addition to skills, is analysed in the following sections of the chapter along with its categories that include natural intelligence, artificial intelligence, and hybrid intelligence. Analysis of artificial intelligence-based internet of things system is deliberated on two approaches, namely criterion-based analysis and elemental analysis. Criterion-based analysis covers the parameter-based investigation to highlight the relation between machine learning and deep learning. Elemental analysis involves four main components of artificial intelligence-based internet of things system, such as device, data, algorithm, and computation. Research works done using deep learning and internet of things are also discussed.

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

Big data and the internet of things (IoT) are two major ruling domains in today's world. It is observed that there are 2.5 quintillion bytes of data created each day. Big data defines a very huge amount of data in terms of both structured and unstructured formats. Business intelligence and other application domains that have high information density use big data analytics to make predictions and better decisions to improve the business. Big data analytics is used to analyze a high range of data at a time. In general, big data and IoT were built on different technologies; however, over a period of time, both of them are interlinked to build a better world. Companies are not able to achieve maximum benefit, just because the data produced by the applications are not utilized and analyzed effectively as there is a shortage of big data analysts. For real-time IoT applications, synchronization among hardware, programming, and interfacing is needed to the greater extent. The chapter discusses about IoT and big data, relation between them, importance of big data analytics in IoT applications.