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)
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This chapter was a brief introduction to data analytics for IoT in the cloud and in the fog. Data analytics is where the value is extracted out of the sea of data produced by millions or billions of sensors. Analytics is the realm of the data scientist and consists of attempts to find hidden patterns and develop predictions from an overwhelming amount of data. To be valuable, all this analysis needs to be at or near real time to make life-critical decisions. You need to understand the problem being solved and the data necessary to reveal the solution. Only then can a data analysis pipeline be architected well. This chapter exposed several data analysis models as well as an introduction to the four relevant machine learning domains.

These analytics tools are the heart of value in IoT to derive meaning from the nuances of massive amounts of data in real time. Machine learning models can predict future events based on current and historical patterns...