Book Image

Internet of Things for Architects

By : Perry Lea
Book Image

Internet of Things for Architects

By: Perry Lea

Overview of this book

The Internet of Things (IoT) is the fastest growing technology market. Industries are embracing IoT technologies to improve operational expenses, product life, and people's well-being. An architectural guide is necessary if you want to traverse the spectrum of technologies needed to build a successful IoT system, whether that's a single device or millions of devices. This book encompasses the entire spectrum of IoT solutions, from sensors to the cloud. We start by examining modern sensor systems and focus on their power and functionality. After that, we dive deep into communication theory, paying close attention to near-range PAN, including the new Bluetooth® 5.0 specification and mesh networks. Then, we explore IP-based communication in LAN and WAN, including 802.11ah, 5G LTE cellular, Sigfox, and LoRaWAN. Next, we cover edge routing and gateways and their role in fog computing, as well as the messaging protocols of MQTT and CoAP. With the data now in internet form, you'll get an understanding of cloud and fog architectures, including the OpenFog standards. We wrap up the analytics portion of the book with the application of statistical analysis, complex event processing, and deep learning models. Finally, we conclude by providing a holistic view of the IoT security stack and the anatomical details of IoT exploits while countering them with software defined perimeters and blockchains.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
1
The IoT Story

Machine learning in IoT


Machine learning is not a new computer science development. On the contrary, mathematical models for data fitting and probability go back to the early 1800s, and Bayes' theorem and the least squares method of fitting data. Both are still widely used in machine learning models today, and we will briefly explore them in the chapter.

It wasn't until Marvin Minsky (MIT) produced the first neural network devices called perceptrons in the early 1950s that computing machines and learning were unified. He later wrote a paper in 1969 that was interpreted as a critique of the limitations of neural networks. Certainly, during that period, computational horsepower was at a premium. The mathematics were beyond the reasonable resources of IBM S/360 and CDC computers. As we will see, the 1960s introduced much of the mathematics and foundations of artificial intelligence in areas such as neural nets, support vector machines, fuzzy logic, and so on.

Evolutionary computation such as...