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For centuries, the concept of a smart, autonomous learning machine has fascinated people. The machine learning philosophy is to automate the development of analytical models so that algorithms can learn continually with the assistance of accessible information. Machine learning (ML) and deep learning (DL) methods are implemented to further improve an application's intelligence and capacities as the quantity of the gathered information rises. Because IoT will be one of the main sources of information, data science will make a significant contribution to making IoT apps smarter. There is a rapid development of both technologies, cloud computing and the internet of things, considering the field of wireless communication. This chapter answers the questions: How can IoT intelligent information be applied to ML and DL algorithms? What is the taxonomy of IoT's ML and DL and profound learning algorithms? And what are real-world IoT data features that require data analytics?

Nowadays, there is an increasing demand for energy saving techniques in residential, industrial, institutional, clinical and other multipurpose indoor and outdoor applications. Lights play an ubiquitous role around the Earth in all types of structures and outdoor surroundings. Hence, the authors propose a universal lighting control device—named Pervasive Adaptive Resourceful Smart Lighting and Alerting Device—accomplished mainly by the use of Arduino UNO R3. The Pervasive Adaptive Resourceful Smart Lighting and Alerting Device works in two modes, namely, light control and alert, by deploying the perceptive light automation and perceptive light automation with buzzer activation algorithms, respectively. The contributions of the paper are: a common lighting control solution for both incandescent and light emitting diode light bulbs for all indoor and outdoor environments. A profound power consumption analysis, and investigation of the proposed device by estimating the Energy Consumption Ratio (ECR) and Relative Energy Saving Ratio (RESR) through the real time deployment in diverse circumstances with 60 W incandescent, 8 W and 0.5 W LED light bulbs is executed. In addition to the evaluation of RESR and ECR characteristics the power consumption of light bulbs in terms of scalable conditions of number of light bulbs is also analyzed. The proposed model is proved to work efficiently for both incandescent and LED light bulbs.

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The evolution of digital era and improvements in technology have enabled the growth of a number of devices and web applications leading to the unprecedented generation of huge data on a day-to-day basis from many applications such as industrial automation, social networking cites, healthcare units, smart grids, etc. Artificial intelligence acts as a viable solution for the efficient collection and analyses of the heterogeneous data in large volumes with reduced human effort at low time. Machine learning and deep learning subspaces of artificial intelligence are used for the achievement of smart intelligence in machines to make them intelligent based on learning from experience automatically. Machine learning and deep learning have become two of the most trending, groundbreaking technologies that enable autonomous operations and provide decision making support for data processing systems. The chapter investigates the importance of machine learning and deep learning algorithms in instilling intelligence and providing an overview of machine learning, deep learning platforms.