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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Basic data analytics in IoT

Data analytics intends to find events, usually in a streaming series of data. There are multiple types of events and roles that a real-time streaming analysis machine must provide. The following is a superset of analytic functions based on the work of Srinath Perera and Sriskandarajah Suhothayan (Solution patterns for real-time streaming analytics. Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS '15). ACM, New York, NY, USA, 247-255. The following is an enumerated listing of these analytic functions:

  • Preprocessing: This includes filtering out events of little interest, denaturing, feature extraction, segmentation, transforming data to a more suitable form (although data lakes prefer no immediate transformation), and adding attributes to data such as a tag (data lakes do need tags).
  • Alerting: Inspect data, and if it exceeds some boundary condition, then raise an alert. The simplest example...