IoT Product Design and Development: Best Practices for Industrial, Consumer, and Business Applications

Description

IoT Product Design and Development Learn to incorporate IoT products into the process of building a product Internet of Things (or IoT) is currently one of the central building blocks of industry. It is the driving technology of the connected world—be it smart cars, smart homes, smart factories, or smart cities. Industrial IoT (IIoT) is one of the most impactful areas of the global market, where it has fundamentally altered industries as varied as manufacturing, electronics, automotive, consumer goods, healthcare, and process industries like oil and gas, among others. As such, it is essential that engineers working in these fields improve their IoT knowledge to keep pace with this growing demand. IoT Product Design and Development offers an accessible entry point to the methods, techniques, and best practices necessary to add IoT onto an existing product or to build new IoT products wholesale. To accomplish this, the volume examines product design requirements for industrial, business, and consumer applications. Relying on real-world examples, the book provides a blueprint of the creation process, including tips on best practices and common pitfalls. Readers will thereby gain the tools to bring IoT to specific industries and job functions. IoT Product Design and Development readers will also find: Concise content that is targeted to what practitioners need to know without the academic jargonIn-depth case studies related to power distribution systems, airports, and consumer home productsDiagrams and tables used liberally to present concepts in a visual wayAdditional sidebar examples are included throughout the book to highlight key issues like IoT security and product lifecycle IoT Product Design and Development is a useful reference for professional mechanical, electrical, and industrial engineers, as well as IoT product managers, business leads, software and hardware professionals, and data professionals. Cover Title Page Copyright Page Contents Acknowledgments Preface Chapter 1 Introduction to IoT WHAT IS IoT? WHY IS IoT IMPORTANT? WHY NOW? Chapter 2 IoT and Digital Transformation THE ROLE OF IoT IN MODERN BUSINESSES Opportunities Risks IT’S AN INTEGRATION PROCESS: THE HUMAN ASPECTS OF DIGITAL TRANSFORMATION IT-OT Integration Building Trust Is Key Chapter 3 Business Models and Market Analysis BUSINESS AND INDUSTRIAL MARKET The Shift to Software-Based PLCS Example: Improve Cost and Reliability of a Power Distribution System with IoT Example: Enhance Safety, Security, and Carbon Footprint in an Airport Using IoT CONSUMER MARKET Sources of Cost Sources of Revenue Matter, Formerly Known as Project Connected Home Over IP (CHIP) Example: Improve Quality of Life and Energy Efficiency by Smart Thermostats CONNECTIVITY CHOICE AND ITS IMPACT ON ROI CLIMATE CHANGE AND PERFORMANCE PER WATT DATA BY-PRODUCTS BEYOND THE ORIGINAL IoT APPLICATION Services and Device Management Peripheral Businesses Marketing Data Management Analytics and AI Developers Security and Safety Chapter 4 Security ENCRYPTION TECHNIQUES Symmetric Encryption Algorithms Asymmetric Encryption Algorithms SOFTWARE AND FIRMWARE UPDATE TECHNIQUES Random Key Generation Software and Firmware Update Process KEY MANAGEMENT AND PROTECTION AGAINST KNOWN THREATS SECURITY ATTACKS AND CHAIN OF TRUST Security Attacks Chain of Trust Incident Response BLOCKCHAIN SUMMARY OF BEST SECURITY PRACTICES FOR IoT SYSTEMS NETWORKING CONCEPTS Proxy VPN Firewall DMZ IoT SECURITY STANDARDS AND CERTIFICATES ioXT UL Matter, Formerly Known as Project Home over IP (CHIP) Governmental Regulations and Other General Guidelines Chapter 5 IoT System Design Process and Main Components DESIGN AND DEPLOYMENT PROCESS Roles, Responsibilities, and Team Structure Process – A Deeper Look SENSORS Common Sensors General Sensor Concepts Filtering the Sensor Output FILE SYSTEMS Data in Motion versus Data at Rest MACHINE LEARNING History and Background Categories of Learning Algorithms General Machine Learning Concepts How a Machine Learning Model Is Built Overfitting Data Set, Explainability, and Supervised Concepts What Is and What Is Not Machine Learning? Machine Learning Architecture NETWORKING AND COMMUNICATION SYSTEM, PRODUCT LIFECYCLE, DEVICE MANAGEMENT IoT System Architecture: Edge, Cloud, and Fog Networking WIRELESS COMMUNICATION PROTOCOLS Short-Range Wireless Protocols Long-Range Wireless Protocols EXAMPLES Smart Video Doorbell Energy-Efficient Office IoT SYSTEMS LIFECYCLE Chapter 6 The Process of Building a Data Product WHAT PERSONAS DO YOU NEED ON YOUR TEAM? BUSINESS UNDERSTANDING DATA UNDERSTANDING DATA PREPARATION MODELING EVALUATION DEPLOYMENT THE CYCLE REPEATS – CRISP-DM Chapter 7 Concluding Remarks Further Reading Index EULA