Different organizations and industries describe IoT differently. One way of defining it simply and tangibly is as a network of smart objects, which connects the physical and digital world together. Examining the E2E life cycle of the IoT solution or, more generally, of the IoT ecosystem, will help us to understand it further and show us how it is applicable to machine learning and DL.

Similar to the definition of IoT, there is no single consensus on the E2E life cycle or the IoT architecture that is agreed universally. Different architectures or layers have been proposed by different researchers. The most commonly proposed options are the three and five-layer life cycles or architectures, as shown in the following diagram:

In the preceding diagram, (a) presents a three-layer IoT life cycle or architecture, and (b) presents a five-layer IoT...

By enabling easy access to, and interaction with, a wide variety of physical devices or things such as vehicles, machines, medical sensors, and more, IoT facilitates the development of applications in many different domains. The following diagram highlights the key application domains of IoT:

These include healthcare, industrial automation (that is, Industry 4.0), energy management and smart grids, transportation, smart infrastructure (such as the smart home and the smart city), retail, and many other areas that will transform our lives and societies for the better. These applications will have a global economic impact of $4 to $11 trillion per year by 2025. The key contributors (in order of their predicted contribution) of this quantity of money include the following:

• Factories or industries, including operation management and predictive maintenance
...

The use of IoT in various application domains will only be effective if those applications can extract some business value from the data generated and collected by IoT devices. In this context, analysis of IoT data is essential in IoT solutions. Gartner identified IoT analytics as one of the two top technologies used in IoT.

IoT analytics is the application of data analysis tools and procedures to unlocking insights from the huge volumes of data generated by IoT devices in different ways. IoT analytics is essential for extracting insights from the data generated by IoT devices or things. More specifically, IoT business models analyze the information generated and collected by things in many ways – for example, to understand customer behavior, to deliver services, to improve products and services, and to identify and intercept business...

In recent years, many IoT applications have been actively exploiting sophisticated DL technologies, which use neural networks to capture and understand their environments. Amazon Echo, for example, is considered to be an IoT application as it connects the physical and human world with the digital world; it can understand human voice commands using DL.

Additionally, Microsoft's Windows face-recognition security system (an IoT application) uses DL technology to perform tasks such as unlocking a door when it recognizes its user's face. DL and IoT are among the top three strategic technology trends for 2017, and were announced at the Gartner Symposium/ITxpo 2016. The intensive publicity around DL is due to the fact that traditional machine learning algorithms do not address the emerging analytic needs of IoT systems. On the...

The data from IoT applications exhibits two characteristics that require different treatment from the analytics approach. Many IoT applications, such as remote patient monitoring or autonomous vehicles, generate streams of data continuously, and this leads to a huge volume of continuous data. Many other applications, such as consumer product analysis for marketing or inhabitant monitoring in forests or underwater, produce data that accumulates as a source of big data. Streaming data is generated or captured within short intervals of time and need to be quickly analyzed to extract immediate and useful insights and make fast decisions.

On the contrary, the term big data refers to huge datasets that commonly used hardware and software platforms are not able to store, manage, process, and analyze. These two types of data need to...

In this chapter, we presented two different layered views of the E2E life cycle of the IoT. We also looked at the IoT system architecture and key application domains of IoT. Following this, we defined what is meant by IoT analytics and its importance in IoT applications, with special emphasis on DL. We discussed the key characteristics of IoT and their corresponding requirements in analytics. Finally, we presented a few real IoT examples, which generate fast and streaming data as well as big data. In the next chapter, you will be introduced to several common DL models and the most cutting-edge architectures that have been introduced in recent years, and learn how they can be useful in analyzing IoT streaming and big data.

It is essential to know the basics of different DL models and their different implementation frameworks in order to use them in different IoT applications...

• Pallavi Sethi and Smruti R. Sarangi, Internet of Things: Architectures, Protocols, and Applications, Journal of Electrical and Computer Engineering, vol. 2017, Article ID 9324035, 25 pages, 2017.
• Atlam, H.F.; Walters, R.J.; Wills, G.B. Fog Computing and the Internet of Things: A Review. Big Data Cogn. Comput. 2018, 2, 10.
• James Manyika, Michael Chui, Peter Bisson, Jonathan Woetzel, Richard Dobbs, Jacques Bughin, and Dan Aharon, Unlocking the potential of the Internet of Things, which is available at https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world.
• Stamford, Conn, Gartner Identifies the Top 10 Internet of Things Technologies for 2017 and 2018, which is available online at https://www.gartner.com/newsroom/id/3221818.
• J. Tang, D. Sun, S. Liu and J. Gaudiot, Enabling Deep Learning...