Book Image

IoT and Edge Computing for Architects - Second Edition

By : Perry Lea
Book Image

IoT and Edge Computing for Architects - Second Edition

By: Perry Lea

Overview of this book

Industries are embracing IoT technologies to improve operational expenses, product life, and people's well-being. An architectural guide is needed 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 IoT devices. IoT and Edge Computing for Architects, Second Edition encompasses the entire spectrum of IoT solutions, from IoT sensors to the cloud. It examines modern sensor systems, focusing on their power and functionality. It also looks at communication theory, paying close attention to near-range PAN, including the new Bluetooth® 5.0 specification and mesh networks. Then, the book explores IP-based communication in LAN and WAN, including 802.11ah, 5G LTE cellular, Sigfox, and LoRaWAN. It also explains edge computing, routing and gateways, and their role in fog computing, as well as the messaging protocols of MQTT 5.0 and CoAP. With the data now in internet form, you'll get an understanding of cloud and fog architectures, including the OpenFog standards. The book wraps up the analytics portion with the application of statistical analysis, complex event processing, and deep learning models. The book then concludes by providing a holistic view of IoT security, cryptography, and shell security in addition to software-defined perimeters and blockchains.
Table of Contents (17 chapters)
Other Books You May Enjoy

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 later in the chapter.

A brief history of AI and machine learning milestones

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...