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

Summary


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 in attempts to find the 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. One needs 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. We see how RNN and CNN cases satisfy this...