Learning Aws Iot

Learn to use AWS IoT services to build your connected applications with the help of this comprehensive guide. Key Features Gets you started with AWS IoT and its functionalities Learn different modules of AWS IoT with practical use cases. Learn to secure your IoT communication Book Description The Internet of Things market increased a lot in the past few years and IoT development and its adoption have showed an upward trend. Analysis and predictions say that Enterprise IoT platforms are the future of IoT. AWS IoT is currently leading the market with its wide range of device support SDKs and versatile management console. This book initially introduces you to the IoT platforms, and how it makes our IoT development easy. It then covers the complete AWS IoT Suite and how it can be used to develop secure communication between internet-connected things such as sensors, actuators, embedded devices, smart applications, and so on. The book also covers the various modules of AWS: AWS Greengrass, AWS device SDKs, AWS IoT Platform, AWS Button, AWS Management consoles, AWS-related CLI, and API references, all with practical use cases. Near the end, the book supplies security-related best practices to make bi-directional communication more secure. When you've finished this book, you'll be up-and-running with the AWS IoT Suite, and building IoT projects. What you will learn Implement AWS IoT on IoT projects Learn the technical capabilities of AWS IoT and IoT devices Create IoT-based AWS IoT projects Choose IoT devices and AWS IoT platforms to use based on the kind of project you need to build Deploy AWS Greengrass and AWS Lambda Develop program for AWS IoT Button Visualize IoT AWS data Build predictive analytics using AWS IoT and AWS Machine Learning Who this book is for This book is for anyone who wants to get started with the AWS IoT Suite and implement it with practical use cases. This book acts as an extensive guide, on completion of which you will be in a position to start building IoT projects using AWS IoT platform and using cloud services for your projects.

Explore IoT, data analytics, and machine learning to solve cyber-physical problems using the latest capabilities of managed services such as AWS IoT Greengrass and Amazon SageMaker Key FeaturesAccelerate your next edge-focused product development with the power of AWS IoT GreengrassDevelop proficiency in architecting resilient solutions for the edge with proven best practicesHarness the power of analytics and machine learning for solving cyber-physical problemsBook Description The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You'll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you'll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you'll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting. What you will learnBuild an end-to-end IoT solution from the edge to the cloudDesign and deploy multi-faceted intelligent solutions on the edgeProcess data at the edge through analytics and MLPackage and optimize models for the edge using Amazon SageMakerImplement MLOps and DevOps for operating an edge-based solutionOnboard and manage fleets of edge devices at scaleReview edge-based workloads against industry best practicesWho this book is for This book is for IoT architects and software engineers responsible for delivering analytical and machine learning–backed software solutions to the edge. AWS customers who want to learn and build IoT solutions will find this book useful. Intermediate-level experience with running Python software on Linux is required to make the most of this book.

In an era where the fusion of connectivity and technology is redefining industries, this book is a must-have guide for aspiring technologists and seasoned professionals alike. This meticulously crafted handbook guides you through every aspect of AWS IoT, touching upon vital spheres such as edge computing, machine learning, and industrial IoT, with a distinct spotlight on cybersecurity. Over the course of this book, industry veteran Syed Rehad unveils the secrets to setting up your AWS IoT environment and walks you through advanced concepts such as leveraging MQTT, mastering Digital Twin implementation, and plumbing the depths of edge machine learning. Whether you're experimenting with virtual devices or hands-on with Raspberry Pi setups, this book will steer you towards innovation and a transformative journey where technology meets intelligence and security, all under the expansive umbrella of AWS IoT. What You Will Learn AWS Mastery: Dive deep into AWS IoT essentials, from setup to hands-on SDK tools Advanced Connectivity: Explore advanced MQTT features and the potential of AWS IoT Core MQTT clients Device Integration: Master AWS IoT device creation, connection, and deployment, blending the digital and physical Digital Twin: Unleash IoT's future with AWS IoT device shadow, capitalizing on digital twin technology Device Management: Transform remote oversight using secure IoT tunnelling and innovative actions Edge Development: Merge IoT and Machine Learning via AWS IoT Greengrass, spotlighting image classification Data to Insights: Swiftly move from raw data to actionable insights leveraging Amazon Timestream IoT Cybersecurity: Strengthen your defenses using AWS IoT Device Defender, Zero Trust principles, and Machine Learning (ML) Detect to prepare for and counter threats Who This Book Is For IoT developers and enthusiasts, professionals who want to implement IoT solutions in industrial scenarios, system architects and designers interested in edge machine learning, business intelligence analysts, and cybersecurity professionals.

Get familiar with the building blocks of IoT solutions using off–the-shelf IoT platforms. Key Features Work with various trending IoT platforms such as AWS IoT, Azure IoT, Google IoT, IBM Watson IoT, and Kaa IoT Gain hands-on knowledge working with Cloud-based IoT platforms, IoT Analytics, and so on. A practical guide that will help you build IoT strategies for your organization Book Description There is a lot of work that is being done in the IoT domain and according to Forbes the global IoT market will grow from $157B in 2016 to $457B by 2020. This is an amazing market both in terms technology advancement as well as money. In this book, we will be covering five popular IoT platforms, namely, AWS IoT, Microsoft Azure IoT, Google IoT Core, IBM Watson IoT, and Kaa IoT middleware. You are going to build solutions that will use a Raspberry Pi 3, a DHT11 Temperature and humidity sensor, and a dashboard to visualize the sensor data in real-time. Furthermore, you will also explore various components of each of the platforms that are needed to achieve the desired solution. Besides building solutions, you will look at how Machine Learning and IoT go hand in hand and later design a simple predictive web service based on this concept. By the end of this book, you will be in a position to implement an IoT strategy best-fit for your organization What you will learn Connect a Temperature and Humidity sensor and see how these two can be managed from various platforms Explore the core components of AWS IoT such as AWS Kinesis and AWS IoTRules Engine Build a simple analysis dashboard using Azure IoT and Power BI Understand the fundamentals of Google IoT and use Google core APIs to build your own dashboard Get started and work with the IBM Watson IoT platform Integrate Cassandra and Zeppelin with Kaa IoT dashboard Review some Machine Learning and AI and get to know more about their implementation in the IoT domain. Who this book is for This book is targeted at IoT architects and engineers, or any stakeholders working with IoT solutions in an organization. This book will also help decision makers and professionals from small- and medium-sized enterprises build an IoT strategy for their venture.

Machine learning (ML) and the internet of things (IoT) are the top technologies used by businesses to increase efficiency, productivity, and competitiveness in this fast-paced digital era transformation. ML is the key tool for fast processing and decision making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. IoT technology has proven efficient in solving many real-world problems, and ML algorithms combined with IoT means the fusion of product and intelligence to achieve better automation, efficiency, productivity, and connectivity. The Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries highlights the importance of ML for IoT’s success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. Covering topics such as agriculture, pattern recognition, and smart applications, this premier reference source is an essential resource for engineers, scientists, educators, students, researchers, and academicians.

This book is a friendly and approachable guide to getting started programming IoT devices to communicate with IoT-centric services on the AWS cloud. The intended goal of the book is for the reader to use either a real IoT device, or a free virtual IoT device, to transmit data to AWS. Once the IoT data is on the AWS cloud, then that data can be stored, transformed, queried, filtered, and visualized with AWS IoT-centric services.This book covers various AWS services such as IoT Core, Lambda, S3, QuickSight, SageMaker, API Gateway, DynamoDB, Timestream, WebSocket's, IoT Analytics, and other AWS services. The IoT devices used to connect to AWS explained in this book will be the ESP8266, ESP32, or the Raspberry Pi. The book is designed to use any or all of these popular and inexpensive IoT devices. Alternately, the reader can choose to use a free virtual IoT device if the reader doesn't intend to purchase any IoT hardware.

The brain has an inherent advantage over traditional computers in that it possesses the ability to acquire knowledge and skills via the process of learning. Nevertheless, the edge is being swiftly eradicated by a new cohort of artificial intelligence programs known as deep neural networks. The primary objective of this book is to assist readers in comprehending fundamental principles before progressing towards refining their programming abilities, ultimately enabling them to become proficient practitioners in the field of deep learning. The book covers fundamental principles in deep learning, exploring various deep learning designs such as recurrent neural networks. Additionally, it delves into contemporary advancements such as generative adversarial networks. The book serves as a comprehensive manual for the implementation of deep neural networks, including several architectures such as Multilayer Perceptron’s (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and others, inside the frameworks of Kera’s and TensorFlow. This book aims to serve as a concise resource for training and optimising deep neural networks. The book encompasses the foundational principles of neural networks, as well as the training methods used in deep neural networks. The book is highly recommended for students seeking a comprehensive reference manual on deep learning, as well as industry practitioners from many disciplines who want to embark on their data science endeavours.

The book features original papers from International Conference on Cryptology & Network Security with Machine Learning (ICCNSML 2022), organized by PSIT, Kanpur, India during 16 – 18 December 2022. This conference proceeding will provide the understanding of core concepts of Cryptology & Network Security with ML in data communication. The book covers research papers in public key cryptography, elliptic curve cryptography, post quantum cryptography, lattice based cryptography, non-commutative ring based cryptography, cryptocurrency, authentication, key agreement, Hash functions, block/stream ciphers, polynomial based cryptography, code based cryptography, NTRU cryptosystems, security and privacy in machine learning, block chain, IoT security, wireless security protocols, cryptanalysis, number theory, quantum computing, cryptographic aspects of network security, complexity theory, and cryptography with machine learning.