Internet of Things A to Z E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A fully updated guide to cutting-edge Internet of Things (IoT) technology
The Internet of Things (IoT) has revolutionized the way we interact with technology in a highly connected world, bringing a host of new objects and points of entry into global communications networks. Internet of Things A to Z: Technologies and Applications, Second Edition, is a thorough and accessible resource to IoT for undergraduate and postgraduate students, as well as practitioners and implementers.
With a contributor team led by an editor who has decades of experience in information and communication technology (ICT), it covers all foundational subjects for understanding IoT. Now fully updated to reflect the latest developments in the field, it is an indispensable volume for students, researchers, and IT learners looking to keep pace with this rapidly growing technology.
Organized into five thematic parts, this edition offers foundational theory, emerging technologies, domain-specific applications, security and trust models, and hands-on tutorials that bridge theory and practice. Each chapter offers a research-informed overview with extensive references, making the book equally valuable as a course text and a scholarly reference.
Readers of the second edition will also find:
- Three additional chapters covering applications of artificial intelligence, machine learning, and deep learning, including information on the Internet of Military Things
- Detailed chapters on IoT architecture and ecosystems, security issues such as trust management and IoT authentication methods, big data analytics, and more
- Expanded treatment of essential technologies not covered in the first edition, including edge computing and edge intelligence, with coverage of applications such as tinyML, Digital Twins, AR/VR, and the metaverse
- Practical tutorials on building IoT prototypes and developing streaming data pipelines using widely adopted tools and platforms
- New information on design and prototyping, including updated hardware boards and instructions
Internet of Things A to Z: Technologies and Applications, Second Edition, is ideal for students interested in the Internet of Things, ICT researchers, industry professionals, and lifetime IT learners seeking a comprehensive and up-to-date reference that connects theory with real-world implementation.
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Seitenzahl: 1684
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
List of Contributors
About the Editor
About the Contributors
Preface
Audience
Organization of the Book
Acknowledgments
Reviewers
About the Companion Website
Part I: Core Concepts and Enablers
1 Introduction to the Internet of Things
1.1 Historical Roots
1.2 Related Streams: Machine‐to‐Machine Communications, Industrial Internet of Things, Industry 4.0, and Cyber‐Physical Systems
1.3 Who Works on the Internet of Things?
1.4 Internet of Things—Core Concepts
1.5 Internet of Things Framework
1.6 Derived Qualities of Modern ICT
1.7 Potential for Product, Process, and Business Model Innovations
1.8 Implications and Challenges
1.9 Conclusion
References
2 An Overview of Enabling Technologies for the Internet of Things
2.1 Introduction
2.2 Overview of IoT Architecture
2.3 Enabling Technologies
2.4 IoT Platforms and Operating Systems
2.5 Conclusion
References
3 RFID in the Internet of Things
3.1 Introduction
3.2 A Brief History of RFID
3.3 A Glimpse into RFID Technology
3.4 RFID Applications in the Internet of Things
3.5 Connecting RFID Transceivers to the Internet
3.6 Emerging Technology Trends
3.7 Challenges and Outlook
3.8 Conclusion
References
4 Sensors, Actuators, Wireless Sensor Networks, and the Internet of Things
4.1 Introduction
4.2 A Glimpse into Sensors and Actuators
4.3 Wireless Sensor Networks
4.4 Wireless Sensor Networks in the Internet of Things
4.5 Deployment Case Studies
4.6 Open Problems and Future Directions
4.7 Conclusion
References
5 Cloud and Fog Computing in the Internet of Things
5.1 Introduction
5.2 IoT System Requirements
5.3 Cloud Computing in IoT
5.4 Fog Computing in IoT
5.5 Conclusion
References
6 On Standardizing the Internet of Things and Its Applications
6.1 Introduction
6.2 Current Status
6.3 The Standardization Environment
6.4 Standardization in Selected Application Areas
6.5 Discussion and Some Speculation
6.6 Conclusion
Acknowledgments
References
Part II: AI and the Internet of Things
7 Artificial Intelligence and the Internet of Things: Algorithms, Applications, and Challenges
7.1 Introduction
7.2 AI Algorithms
7.3 AI‐Based IoT Applications
7.4 Current Challenges and Potential Solutions
7.5 More Considerations on Real‐World Applications and Moral Ramifications of AI/ML in IoT
7.6 Conclusion
References
8 Deep Learning and the Internet of Things: Applications, Challenges, and Opportunities
8.1 Introduction
8.2 Overview of Deep Learning
8.3 Edge Intelligence
8.4 tinyML
8.5 Digital Twins
8.6 Metaverse
8.7 Challenges and Opportunities
8.8 Conclusion
References
Part III: Use Cases and Application Domains
9 The Industrial Internet of Things
9.1 Introduction
9.2 Market Overview
9.3 Interoperability and Technologies
9.4 Alliances
9.5 Conclusions
Acknowledgments
References
10 The Emerging “Energy Internet of Things”
10.1 Introduction
10.2 Power Management Trends and EIoT Support
10.3 Real‐Life Power Management Optimization Approaches
10.4 Challenges and Future Directions
10.5 Conclusion
References
11 Implementing the Internet of Things for Renewable Energy
11.1 Introduction
11.2 Managing the Impact of Sustainable Energy
11.3 EIoT Deployment
11.4 Industry Standards for EIoT
11.5 Security Considerations in EIoT and Clean Energy Environments
11.6 Conclusion
References
12 Internet of Things Applications for Smart Cities
12.1 Introduction
12.2 IoT Applications for Smart Cities
12.3 Specific Smart City Applications
12.4 Optimal Enablement of Video and Multimedia Capabilities in IoT
12.5 Key Underlying Technologies for Smart Cities IoT Applications
12.6 Challenges and Future Research
12.7 Conclusion
References
13 Internet of Things Applications for Agriculture
13.1 Introduction
13.2 Internet‐of‐Things‐Based PA
13.3 IoT Application in Agriculture Irrigation
13.4 IoT Application in Agriculture Fertilization
13.5 IoT Application in Crop Disease and Pest Management
13.6 IoT Application in Precision Livestock Farming
13.7 Conclusion
References
14 Smart Landslide Monitoring and Warning Systems with IoT Technologies
14.1 Introduction
14.2 IoT‐Based Landslide Early Warning Systems (IoT‐LEWSs)
14.3 Case Studies
14.4 Challenges and Future Research
14.5 Conclusion
References
15 The Internet of Things and People in Health Care
15.1 Introduction
15.2 The Smart Health Care Ecosystem
15.3 Dimensions of Internet of Things Applications in Health Care
15.4 Examples of IoT‐Related Health Care Applications and Their Dimensions
15.5 Challenges
15.6 Conclusion
Acknowledgments
References
16 Smart Ambulance and Emergency Medicine: Mobile IoT in the 6G Era
16.1 Introduction
16.2 IoT in Emergency Medicine
16.3 Integration and Compatibility
16.4 Case Study: Chronic Obstructive Pulmonary Disease
16.5 Smart Ambulance Challenges
16.6 Moving into the 6G Era
16.7 Conclusions
References
17 Smart Connected Homes
17.1 Introduction
17.2 The Smart Connected Home Domain
17.3 Smart Connected Home Systems
17.4 The Smart Connected Home Technologies
17.5 Smart Connected Home Architectures
17.6 Smart Connected Home Challenges and Research Directions
17.7 Conclusions
Acknowledgments
References
18 The Internet of Flying Things
18.1 Introduction
18.2 Flying Things
18.3 The Internet of Flying Things
18.4 Challenges
18.5 Case Studies
18.6 Conclusions
Acknowledgments
References
19 Internet of Military Things and Weaponized AI: Technology in the Age of Conflict
19.1 Introduction
19.2 Military Systems: Basic Elements
19.3 Military Applications: Selected Topics
19.4 AI Governance: Legal and Ethical Ramifications
19.5 Conclusion
References
Part IV: Security and Privacy
20 Security Mechanisms and Technologies for Constrained IoT Devices
20.1 Introduction
20.2 Security in IoT Protocols and Technologies
20.3 Security Issues and Solutions
20.4 Conclusion
References
21 Blockchain‐Based Security Solutions for IoT Systems
21.1 Introduction
21.2 Regulatory Requirements
21.3 Blockchain Technology
21.4 Blockchains and IoT Systems
21.5 Examples of Blockchain‐Based Security Solutions for IoT Systems
21.6 Challenges and Future Research
21.7 Conclusions
References
22 Authentication Methods for the Internet of Things: Pre‐ and Post‐Quantum Computing
22.1 Introduction
22.2 Pre‐Quantum Authentication Methods for IoT
22.3 Quantum Computing and its Impact on IoT Security
22.4 Post‐Quantum Authentication Methods for IoT
22.5 Post‐Quantum Authentication and IoT Applications
22.6 Hybrid Authentication Approach for Enhanced Security
22.7 Future Directions and Challenges
22.8 Conclusion
References
23 On the Role of Dynamic Trust Management in the Evolving Internet of Things
23.1 Introduction
23.2 Trust Concepts
23.3 Dynamic Trust Management in IT/OT Convergence
23.4 Dynamic Trust Management
23.5 Dynamic Trust Management for IoT
23.6 Dynamic Trust in Internet of Sociable Autonomous Things
23.7 Conclusion
References
Part V: Practical Implementations and Tutorials
24 A Tutorial Introduction to IoT Design and Prototyping with Examples
24.1 Introduction
24.2 Main Features of IoT Hardware Development Platforms
24.3 Design and Prototyping of IoT Applications
24.4 Projects on IoT Applications
24.5 Conclusion
Acknowledgments
References
25 A Tutorial on IoT Streaming Data Pipelines: The What, Why, and How
25.1 Introduction
25.2 Overview of Streaming Data Pipelines
25.3 Foundational Technologies for IoT Streaming Data Pipelines
25.4 Demonstration: How to Build an IoT Streaming Data Pipeline
25.5 Conclusion
References
Glossary
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Overview of communication technologies for IoT (adapted f...
Chapter 2
Table 2.1 Comparison of communication technologies.
Table 2.2 Comparison of IoT data exchange protocols.
Table 2.3 Comparison of Big Data streaming processing technologies....
Table 2.4 Comparison of IoT operating systems.
Table 2.5 Comparison of IoT platforms.
Chapter 4
Table 4.1 Examples of conventional sensors classified according to...
Table 4.2 Examples of conventional actuators classified according ...
Table 4.3 Examples of WSN motes.
Table 4.4 Examples of microcontrollers in WSN motes.
Table 4.5 Features of operating systems for WSN motes.
Table 4.6 Radio transceivers for WSN motes.
Table 4.7 Categories of IoT middleware solutions.
Chapter 5
Table 5.1 Overview of some gateway devices suitable for local comp...
Chapter 6
Table 6.1 Major SSOs developing dedicated IoT‐specific standards....
Table 6.2 (E)merging application areas as identified in the litera...
Table 6.3 Disciplines involved in different application areas (exc...
Chapter 7
Table 7.1 Main advantages and disadvantages of ML algorithms.
Table 7.2 Main advantages and disadvantages of deep learning (DL) ...
Chapter 10
Table 10.1 Total U.S. electricity consumption and intensities, 20...
Table 10.2 Typical process used in energy optimization studies.
Chapter 11
Table 11.1 Distributed renewable generation sources.
Table 11.2 Energy storage technologies.
Table 11.3 Energy IoT device datasets.
Table 11.4 Smart controls and customer usage.
Chapter 12
Table 12.1 Key urban challenges and IoT‐supported solutions.
Table 12.2 List of smart cities initiatives and cities with inter...
Table 12.3 A taxonomy of the requisite synthesis to achieve broad...
Table 12.4 Reference interfaces in the evolved packet core (EPC) ...
Chapter 14
Table 14.1 Comparison of wireless communication technologies used...
Chapter 16
Table 16.1 Molar heat output of enzyme catalyzed reactions in bio...
Chapter 17
Table 17.1 Categorization of the different sensor types and their...
Chapter 18
Table 18.1 A comparison of available features of UASs, IoT, and t...
Chapter 20
Table 20.1 Overview of protocols and mechanisms.
Chapter 22
Table 22.1 Assurance Level of different authentication methods.
Table 22.2 Pre‐quantum authentication methods.
Table 22.3 Impact of quantum computing on IoT security—challenges...
Table 22.4 Drawbacks of PQC Algorithms.
Table 22.5 Comparison of Dilithium, Falcon, and Sphincs+.
Chapter 23
Table 23.1 Device layer TMS use (see Sharma et al. (2020) for an ...
Chapter 24
Table 24.1 Summary of processing and storage capacity of Arduino ...
Table 24.2 Summary of power consumption, size, and cost of Arduin...
Table 24.3 Summary of connectivity and flexibility/customizabilit...
Table 24.4 Summary of processing speed and memory/storage capacit...
Table 24.5 Summary of power consumption, size, and cost of Raspbe...
Table 24.6 Summary of connectivity and flexibility/customizabilit...
Chapter 25
Table 25.1 Hardware specifications.
List of Illustrations
Chapter 1
Figure 1.1 Reality‐virtuality continuum for extended reality.
Figure 1.2 Internet of Things framework.
Figure 1.3 High‐level view of an IoT architecture.
Figure 1.4 IoT application domains and related applications.
Chapter 2
Figure 2.1 The IoT five‐layer architectural model.
Figure 2.2 RPL topology.
Chapter 3
Figure 3.1 RFID tag‐reader communication. (a) Active tag with read...
Figure 3.2 Data acquisition for the IoT through RFID.
Figure 3.3 Network topologies for linking RFID sensors to the Inte...
Figure 3.4 WSN sensors as data routers for RFID tags.
Chapter 4
Figure 4.1 IoT core components.
Figure 4.2 Generic layout of wireless sensor networks.
Figure 4.3 WSN protocol stack.
Figure 4.4 Generic WSN mote.
Figure 4.5 Relationship between middleware and IoT components.
Chapter 5
Figure 5.1 Cloud computing service models.
Figure 5.2 Common cloud‐based IoT architecture.
Figure 5.3 Scale and latency in cloud‐based IoT systems.
Figure 5.4 Revised fog‐enabled architecture combining fog and clou...
Chapter 6
Figure 6.1 The generic three‐tier IoT architecture.
Figure 6.2 The web of relevant SSOs.
Figure 6.3 Formal coordination between SSOs.
Figure 6.4 Entities and links between them in ITS (automotive) sta...
Figure 6.5 Entities and links between them in ITS (automotive) sta...
Figure 6.6 Timeline of the establishment of important standardizat...
Figure 6.7 Links between SSOs in the smart manufacturing area toda...
Figure 6.8 Timeline of the establishment of important standardizat...
Figure 6.9 Links between SSOs in the smart grid sector today (well...
Figure 6.10 Timeline of the establishment of important standardiz...
Figure 6.11 Smart city model according to and adapted from Naviga...
Figure 6.12 Entities and links between them in “smart city” stand...
Figure 6.13 Timeline of the establishment of important standardiz...
Figure 6.14 Timeline of the establishment of important standardiz...
Figure 6.15 Timeline of the establishment of important IoT standa...
Chapter 7
Figure 7.1 Significant phases of the Journey of AI.
Figure 7.2 Relationship between different categories of AI.
Figure 7.3 Main categories of ML and their subcategories with comm...
Figure 7.4 AI‐based IoT application domains.
Figure 7.5 Challenges in AI‐based IoT systems.
Figure 7.6 Ethical considerations in AI‐based IoT systems.
Chapter 8
Figure 8.1 Layers of the IoT architecture.
Figure 8.2 Illustration of an MLP feed‐forward neural network and ...
Figure 8.3 Illustration of the most popular types of generative mo...
Figure 8.4 The reinforcement learning training paradigm.
Figure 8.5 Computer and power resources available at various layer...
Figure 8.6 Abstract overview of a cloud‐edge continuum.
Figure 8.7 A standard architecture for tinyML. Data is sensed from...
Figure 8.8 The digital twin model.
Chapter 9
Figure 9.1 Connectivity on the OSI layer stack.
Figure 9.2 Communication on the OSI layer stack.
Figure 9.3 Data exchange on the OSI layer stack.
Figure 9.4 Information modeling based on Pras and Schoenwaelder (2...
Figure 9.5 Semantic Web layer cake on top of the OSI model based o...
Figure 9.6 Functional domains of the IIRA based on Industrial Inte...
Figure 9.7 Functional domains of the IIRA based on Adolphs et al. ...
Figure 9.8 Asset Administration Shell based on Adolphs et al. (201...
Chapter 10
Figure 10.1 A traditional power grid that interconnects EEs suppo...
Figure 10.2 Ecosystem of an evolved power grid. (a) Traditional. ...
Figure 10.3 Typical control architecture for a distributed smart ...
Figure 10.4 DERMS using IoT principles.
Figure 10.5 MCG using IoT principles.
Figure 10.6 Concept arrangement of a DRMS using IoT principles.
Figure 10.7 Demand response principles.
Figure 10.8 Lighting management strategies.
Figure 10.9 Logical view of connected lighting arrangement.
Figure 10.10 Evolved luminaire.
Figure 10.11 ZigBee stack.
Figure 10.12 Integrated control of energy and comfort at the per...
Figure 10.13 ASHRAE Standard 90.1‐2010 Modeling of energy opport...
Figure 10.14 Energy efficiency building upgrade process.
Chapter 11
Figure 11.1 Solar output for July 1, 2011, in Oahu, HI.
Figure 11.2 Household demand curve, solar output curve, and net l...
Figure 11.3 Storage simulation.
Figure 11.4 EIoT dynamic reduction simulation.
Figure 11.5 Load shift simulation.
Figure 11.6 EIoT central control diagram.
Figure 11.7 EIoT web diagram.
Figure 11.8 EIoT gateway diagram.
Chapter 12
Figure 12.1 A logical view of an IoT ecosystem, also as applicabl...
Figure 12.2 Graphical examples of smart city IoT applications.
Figure 12.3 Global distribution of smart cities initiatives.
Figure 12.4 Dispersed city‐based networks may be in different aut...
Figure 12.5 3GPP/ETSI protocol stack specification of PMIPv6 for ...
Figure 12.6 Example of PMIPv6 usage in 3GPP context.
Chapter 13
Figure 13.1 World population, 2015–2100.
Figure 13.2 World agricultural land, 1961–2013.
Figure 13.3 IoT‐backboned PA.
Figure 13.4 Agriculture IoT architecture.
Figure 13.5 IoT irrigation system diagram.
Chapter 14
Figure 14.1 Framework of an IoT‐based LEWS.
Figure 14.2 Power management in IoT nodes.
Figure 14.3 Cloud and edge computing architecture (in the IoT-Edg...
Figure 14.4 Integrated Remote Sensing and IoT‐Based Landslide Mon...
Chapter 15
Figure 15.1 Smart health care ecosystem.
Figure 15.2 A snowflake model representing the seven dimensions o...
Figure 15.3 EpiWatch represented in the snowflake model.
Figure 15.4 Smart Sock 2 represented in the snowflake model.
Figure 15.5 Propeller represented in the snowflake model.
Figure 15.6 Ginger.io represented in the snowflake model.
Figure 15.7 SmartSole represented in the snowflake model.
Figure 15.8 CoaguChek represented in the snowflake model.
Figure 15.9 The activity tracker for oncology treatment represent...
Figure 15.10 The continuous glucose monitoring meter represented...
Chapter 16
Figure 16.1 Smart ambulance architecture.
Figure 16.2 Integrated IoT sensing at the point‐of‐care (PoC).
Figure 16.3 Biosensor designed to withstand harsh operating envir...
Figure 16.4 Control system.
Figure 16.5 Service binding.
Figure 16.6 IoT‐based local area weather observation.
Figure 16.7 Binding meta‐objects.
Figure 16.8 Range extension through a multi‐hop cellular network....
Figure 16.9 Generalized smart ambulance in an IoT environment.
Figure 16.10 Disease modeling using spatio‐temporal surveillance...
Figure 16.11 High‐tier architecture of wireless PoC sensing syst...
Figure 16.12 Co‐channel interference analysis using self‐cogniza...
Figure 16.13 Minimizing interference under frequency reuse.
Chapter 17
Figure 17.1 A classification of the three types of smart homes.
Figure 17.2 The main smart connected home environment stakeholder...
Figure 17.3 A generic architecture of the smart connected home.
Figure 17.4 The three main types of communication models. (a) Dev...
Figure 17.5 (a) Centralized and (b) distributed architectural mod...
Figure 17.6 Malicious threat agents targeting the smart connected...
Chapter 18
Figure 18.1 Examples of UAVs. (a) A drone. (b) A remotely piloted...
Figure 18.2 Relationships between different types of mobile ad ho...
Figure 18.3 The UAV can be placed either in fog or IoT layers whe...
Figure 18.4 Smart city applications taken to the next level with ...
Figure 18.5 Smart farm applications taken to the next level with ...
Figure 18.6 A wide flying ad hoc network.
Figure 18.7 Available cameras in the cloud can be accessed by end...
Figure 18.8 A comparison between OSI model and IoT.
Figure 18.9 Surveillance situations for the study of the applicat...
Figure 18.10 Applications of IoFT in smart cities.
Figure 18.11 Applications of IoFT in big events.
Chapter 19
Figure 19.1 Manned aircraft commanding unmanned “loyal wingmen.”...
Figure 19.2 Cooperating humans, humanoids, and robodogs (Soldiers...
Figure 19.3 Communicating with a station on the far side of the M...
Figure 19.4 AI‐generated voice chat, controlled by the enemy
Chapter 20
Figure 20.1 Example of resource‐constrained IoT networks with dif...
Figure 20.2 DTLS message exchange.
Figure 20.3 Denial‐of‐service attack against a server IoT device....
Figure 20.4 Denial‐of‐service attack targeting a DTLS server.
Figure 20.5 Poorly scalable storage of preshared keys on a DTLS s...
Figure 20.6 Example of LKH key material in the presence of eight ...
Chapter 21
Figure 21.1 Basic blockchain structure.
Figure 21.2 An overview of the proposed solution for secure acces...
Figure 21.3 An overview of the proposed blockchain‐based architec...
Figure 21.4 Handling a store transaction.
Figure 21.5 Handling an access transaction.
Chapter 22
Figure 22.1 Fields influenced by quantum computing.
Figure 22.2 A taxonomy of IoT pre‐quantum authentication schemes....
Figure 22.3 Post‐quantum cryptography algorithms.
Figure 22.4 Taxonomy of IoT post‐quantum authentication.
Figure 22.5 Multilayered security approach.
Figure 22.6 Future directions for post‐quantum IoT.
Figure 22.7 Emerging technologies and post‐quantum IoT.
Chapter 23
Figure 23.1 Soft and hard security.
Figure 23.2 Automation pyramid.
Figure 23.3 Zero‐trust dynamic trust management system.
Figure 23.4 Impact of IIoT on the automation pyramid.
Figure 23.5 SIoT trust management system architecture.
Figure 23.6 General IoSAT dynamic risk management system.
Chapter 24
Figure 24.1 The selected Arduino boards.(a) Arduino Mega 2560...
Figure 24.2 The selected Raspberry Pi models.(a) Raspberry Pi...
Figure 24.3 Choosing the board on the Tools menu.
Figure 24.4 Choosing the serial port.
Figure 24.5 Accessing
Manage Libraries
functionality.
Figure 24.6 Uploading the code to the device.
Figure 24.7 A breadboard diagram of the Arduino temperature logge...
Figure 24.8 Mosquitto broker running on a Linux computer (accesse...
Figure 24.9 Mosquitto subscriber running on a Linux computer (acc...
Figure 24.10 Raspberry Pi imager.
Figure 24.11 Raspberry Pi imager OS settings menu.
Figure 24.12 A breadboard diagram of the Raspberry Pi web‐contro...
Chapter 25
Figure 25.1 Batch processing vs. stream processing.
Figure 25.2 Streaming data pipeline key stages.
Figure 25.3 High‐level streaming pipeline architecture and core c...
Figure 25.4 High‐level view of a modern IoT streaming data pipeli...
Figure 25.5 Node‐RED’s GUI. 1. Node Palette (to choose from avail...
Figure 25.6 Interaction between MQTT clients and the broker follo...
Figure 25.7 Kafka architecture.
Figure 25.8 Read and write with sequential and random access.
Figure 25.9 Overview of Apache Flink Architecture.
Figure 25.10 High‐level architecture of the presented IoT stream...
Figure 25.11 RedBoard connected to the DHT11 sensor.
Figure 25.12 Sketch to read humidity and temperature.
Figure 25.13 Node‐RED flow.
Figure 25.14 JSON object created from sensor readings.
Figure 25.15 The HiveMQ Cloud cluster details.
Figure 25.16 The “mqtt out” node configured to connect to the Hi...
Figure 25.17 The subscribed MQTT topic shows sensor readings.
Figure 25.18 Kafka cluster details.
Figure 25.19 Connect HiveMQ to Confluent Kafka.
Figure 25.20 Kafka topic receiving messages from the HiveMQ Clou...
Figure 25.21 JSON message from MQTT published to a Kafka topic....
Figure 25.22 Alerts data stream as a table.
Figure 25.23 The Alerts table is also available as a Kafka topic...
Figure 25.24 Kafka connector to S3 Sink.
Figure 25.25 Data sent to S3.
Figure 25.26 S3 file showing persisted sensor readings.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
List of Contributors
About the Editor
About the Contributors
Preface
Acknowledgments
Reviewers
About the Companion Website
Begin Reading
Glossary
Index
Wiley End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor‐in‐Chief
Moeness Amin
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Hai Li
Tony Quek
James Duncan
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Behzad Razavi
Hugo Enrique Hernandez Figueroa
Joydeep Mitra
Thomas Robertazzi
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Patrick Chik Yue
Internet of Things A to Z
Technologies and Applications
Second Edition
Edited by
Qusay F. Hassan
Copyright © 2026 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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To Mishu and Kooky
Your love and quiet companionship shaped the first edition of this book.
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List of Contributors
Abdullah AbuhusseinDepartment of Information SystemsSt. Cloud State UniversitySt. CloudMNUSA
Farhad AhamedSchool of Social SciencesWestern Sydney UniversityPenrithAustralia
Ala Al‐AreqiSchool of Social SciencesWestern Sydney UniversityPenrithAustralia
Faisal S. AlsubaeiFaculty of Computing and Information TechnologyUniversity of JeddahJeddahSaudi Arabia
Theodoros AslanidisSchool of Computer ScienceUniversity College DublinDublin Ireland
Adarsha BhattaraiDepartment of Electrical and Computer EngineeringUniversity of NebraskaLincolnUSA
Kalinka Regina Lucas Jaquie Castelo BrancoDepartment of Computer ScienceInstitute of Mathematics and Computer Sciences (ICMC)University of São Paulo (USP)São CarlosSão PauloBrazil
Willie L. Brown Jr.Department of Engineering and Aviation SciencesUniversity of Maryland Eastern ShorePrincess AnneMDUSA
Joseph BugejaInternet of Things and People Research Center and Department of Computer Science and Media TechnologyMalmö UniversityMalmöSweden
John ByabazaireSchool of Computer ScienceUniversity College DublinDublin Ireland
Mirothali ChandACS LabIndian Institute of Technology MandiHimachal PradeshIndia
Dimitris ChatzopoulosSchool of Computer ScienceUniversity College DublinDublin, Ireland
Andreas ChouliarasSchool of Computer ScienceUniversity College DublinDublin, Ireland
Ibibia K. DabipiDepartment of Engineering and Aviation SciencesUniversity of Maryland Eastern ShorePrincess AnneMDUSA
Paul DavidssonInternet of Things and People Research Center and Department of Computer Science and Media TechnologyMalmö UniversityMalmöSweden
Varun DuttACS LabIndian Institute of Technology MandiHimachal PradeshIndia
Jeanette ErikssonDepartment of Computer Science and Media TechnologyInternet of Things and People (IoTaP) Research CenterMalmö UniversityMalmöSweden
Akaa Agbaeze EtengDepartment of Electrical/Electronic EngineeringUniversity of Port HarcourtRivers StateNigeria
Farnaz FaridSchool of Social SciencesWestern Sydney UniversityPenrithAustralia
Lucas FincoPrincipal ConsultantStrategainNew YorkNYUSA
Alvis FongDepartment of Computer ScienceWestern Michigan UniversityKalamazoo MIUSA
Bernard FongDepartment of Electrical EngineeringChang Gung UniversityTaiwan
João Vitor de Carvalho FontesDepartment of Mechanical Engineering São Carlos School of Engineering (EESC)University of São Paulo (USP)São CarlosSão PauloBrazil
Virginia N. L. FranqueiraInstitute of Cyber Security for Society (iCSS) & School of ComputingUniversity of KentCanterburyUnited Kingdom
Mário M. FreireDepartment of Computer ScienceInstituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
Daniel HappQAware GmbHBerlinGermany
Qusay F. HassanIndependent ResearcherCairoEgypt
Muhammad Babar ImtiazSoftware Research InstituteTechnological University of the ShannonAthloneCo WestmeathIreland
Pedro R. M. InácioDepartment of Computer ScienceInstituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
Andreas JacobssonInternet of Things and People Research Center and Department of Computer Science and Media TechnologyMalmö UniversityMalmöSweden
Kai JakobsComputer Science DepartmentRWTH Aachen UniversityAachenGermany
Jonny KarlssonDepartment of Business Management and AnalyticsArcada University of Applied SciencesHelsinkiFinland
Haesik KimVTT Technical Research Centre of FinlandOuluFinland
Vlasis KoutsosSchool of Computer ScienceUniversity College DublinDublin, Ireland
Praveen KumarACS LabIndian Institute of Technology MandiHimachal PradeshIndia
Brian LeeSoftware Research InstituteTechnological University of the ShannonAthloneCo WestmeathIreland
Chee Yen LeowWireless Communication CentreFaculty of Electrical EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
Chi Kwong LiClinical ResearchAlpha Positive ClinicHong Kong
Yutong LiuDepartment of RadiologyUniversity of Nebraska Medical CenterLincolnUSA
Kieran McLaughlinSchool of ElectronicsElectrical Engineering and Computer ScienceQueens University BelfastBelfastUnited Kingdom
Manuel MerujeDepartment of Computer ScienceInstituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
Daniel MinoliIoT DivisionDVI CommunicationsNew YorkNYUSA
Benedict OcchiogrossoIntellectual Property DivisionDVI CommunicationsNew YorkNYUSA
Ioannis PanagiotidisSchool of Computer ScienceUniversity College DublinDublin Ireland
Neha PandeyHelloFresh SEChicago USA
Dongming PengDepartment of Electrical and Computer EngineeringUniversity of NebraskaLincolnUSA
Daniel Fernando PigattoDepartment of Computer ScienceGraduate Program in Electrical and Computer Engineering (CPGEI)Federal University of Technology Paraná (UTFPR)CuritibaParanáBrazil
Alex Sandro Roschildt PintoFederal University of Santa Catarina (UFSC)BlumenauSanta CatarinaBrazil
Göran PulkkisDepartment of Business Management and AnalyticsArcada University of Applied SciencesHelsinkiFinland
Sharul Kamal Abdul RahimWireless Communication CentreFaculty of Electrical EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
Shahid RazaRISE SICS Security LabKistaStockholmSweden
Mariana RodriguesDepartment of Computer ScienceInstitute of Mathematics and Computer Sciences (ICMC)University of São Paulo (USP)São CarlosSão PauloBrazil
Nancy L. RussoDepartment of Computer Science and Media TechnologyInternet of Things and People (IoTaP) Research CenterMalmö UniversityMalmöSweden
Fariza SabrinaSchool of Engineering and TechnologyCQ UniversitySydneyAustralia
Vyasa SaiIntel Corp.Folsom CAUSA
Musa G. SamailaDepartment of Computer ScienceInstituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
Centre for Geodesy and GeodynamicsNational Space Research and Development AgencyToroBauchiNigeria
Chathumi SamaraweeraDepartment of Electrical and Computer EngineeringUniversity of NebraskaLincolnUSA
Sahil SankhyanACS LabIndian Institute of Technology MandiHimachal PradeshIndia
Detlef SchoderCologne Institute for Information Systems (CIIS)Chair of Information Systems and Information ManagementUniversity of CologneCologne, Germany
João B. F. SequeirosDepartment of Computer ScienceInstituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
Hamid SharifDepartment of Electrical and Computer EngineeringUniversity of NebraskaLincolnUSA
Sajjan ShivaComputer Science DepartmentUniversity of MemphisMemphisTNUSA
Jan SliwaSchool of Engineering and Computer ScienceBerne University of Applied SciencesBerneSwitzerland
James SmithComputer Science and Creative Technologies (FET)University of the West of England (UWE)BristolEnglandUnited Kingdom
Marco TilocaRISE SICS Security LabKistaStockholmSweden
Joana TiranaSchool of Computer ScienceUniversity College DublinDublin, Ireland
Kala Venkata UdaySchool of Civil and Environmental EngineeringIndian Institute of Technology MandiHimachal PradeshIndia
Magnus WesterlundDepartment of Business Management and AnalyticsArcada University of Applied SciencesHelsinkiFinland
Alexander WillnerFraunhofer FOKUSSoftware‐based Networks (NGNI)BerlinGermany
Technische Universität BerlinNext Generation Networks (AV)BerlinGermany
Yuhang YeSoftware Research InstituteTechnological University of the ShannonAthloneCo WestmeathIreland
Lei ZhangDepartment of Engineering and Aviation SciencesUniversity of Maryland Eastern ShorePrincess AnneMDUSA
About the Editor
Qusay F. Hassan, PhD, is an independent researcher and technology evangelist with over 20 years of industry experience in information communication technology (ICT). Throughout his career, he has held various roles in software development, IT services, and teaching. He is currently an independent technical consultant, advising on emerging ICT trends and enterprise systems. In his previous position as a systems analyst at the U.S. Agency for International Development (USAID), he implemented and managed large‐scale data and software systems and actively contributed to digital transformation initiatives, helping to adopt new technologies and deliver innovative solutions across sectors. He received his PhD in computer and information sciences from Mansoura University, Egypt, in 2015. His technical experience and research interests span the areas of software engineering, big data engineering, and distributed systems. He has authored several publications and reviewed many articles and books in these areas. He is also the editor of several academic and research books, including his latest: Advances in the Internet of Things: Challenges, Solutions, and Emerging Technologies (CRC Press, 2025). He is a senior member of IEEE.
About the Contributors
Abdullah Abuhussein received the B.Sc. degree in computer science, in 1999, the M.Sc. degree in information systems management from Ferris State University, in 2002, and the Ph.D. degree in computer science from The University of Memphis. In 2017, he joined the Department of Information Systems, Herberger Business School, St. Cloud State University, as an Assistant Professor. In his Ph.D. research, he focused on pragmatic cloud security assessment framework and stakeholder’s perspective in cybersecurity. He is teaching courses on security, software engineering, and cloud computing as a Lecturer with various educational institutions. His research interests include cloud computing, cloud security, security economics, the Internet of Things (IoT), software engineering, security and privacy, and security metrics. He holds a number of refereed publications in related venues, presented articles in various conferences, and served as a reviewer for some journals, including the IEEE Transactions on Cloud Computing.
Farhad Ahamed is an academic and researcher focusing in Cybersecurity, Applied Machine Learning, and Computational Intelligence. Farhad has navigated through academia, research, and the IT industry for over 18 years. He has contributed to research areas such as Biometrics, Smart Aging and Network Security. His research often integrates machine learning with other disciplines, such as cybersecurity, healthcare, environmental science, and industrial automation. He has worked on predictive diagnostics in healthcare, including the early detection of dementia using machine learning and IoT, as well as multimodal fall detection utilizing wearable and remote sensors, and multimodal heart rate‐based authentication using AI. His work also extends to AI‐based flood estimation by analyzing flood catchment data. He was a Google mentor of Cybersecurity and Machine Learning at Western Sydney University (Google exploreCSR) and supervised ML‐based projects focusing on biometrics, fake news and image identification. He is actively involved in mentoring Ph.D. students and early‐career researchers, fostering the next generation of machine‐learning experts.
Ala Al‐areqi is currently a Lecturer in Cybersecurity and Behavior at Western Sydney University, with over 15 years of teaching experience. He also served as an Associate Professor of Computer Science and the Dean of Faculty at the American University of Afghanistan until July 2024. He holds a Doctor of Philosophy and a Master of Computer Science in Information Security from University Technology Malaysia, and a Bachelor of Information Technology (Honours) in Security Technology from Multimedia University. Dr. Al‐areqi has made significant scholarly contributions, with over 19 research papers in international conferences and journals. His research interests include Biometrics, Penetration Testing, Cryptography, Machine Learning in Cybersecurity, and Data Science. In recognition of his scholarly achievements, he received the Second‐Best Scholarship Award in 2019 at the American University of Afghanistan. Currently, he is conducting research on Cyberwarfare and Penetration Testing. Beyond academia, Dr. Al‐areqi has served as a cybersecurity consultant for AIbiotex IT Solutions, where he developed secure methodologies to protect systems and networks from potential threats. He is also an experienced programmer with expertise in Python, Java, C++, C, R, PHP, JavaScript, Perl, HTML, and CSS, along with extensive knowledge of databases, networking, and web technologies.
Faisal S. Alsubaei (Member, IEEE) received the B.S. (Eds.) degree in computer science from King Abdulaziz University, Saudi Arabia, the M.Sc. degree in computer science, concentrating in security in computing from The Royal Melbourne Institute of Technology University, Australia, and the Ph.D. degree in computer science from the University of Memphis, USA. He is currently the Vice‐Dean of the Deanship of Scientific Research and an Assistant Professor with the Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia. He was a Software Engineer with Shaker and Associates Pty Ltd., Australia. He is also a Microsoft Certified Technology Specialist, Microsoft Certified Professional, and Cisco Certified Entry Networking Technician. He is also an active member of Australian Computer Society and Linux Users of Victoria. His research interests include security and privacy in the Internet of Medical Things and cloud computing.
Theodoros Aslanidis is a Ph.D. Student in the School of Computer Science of University College Dublin. He received his Diploma in Electrical and Computer Engineering from the University of Thessaly. On his Ph.D., he works on the MLSysOps project, focusing on AI for autonomic system operation. His research interests include cloud computing, machine learning for systems, and deep reinforcement learning.
Adarsha Bhattarai received a B.S. degree in Electrical and Electronics Engineering in July 2021. He is currently a Ph.D. student in the Department of Electrical and Computer Engineering at the University of Nebraska–Lincoln. His primary research interests include digital signal processing, the Internet of Things, artificial intelligence, and security in wireless networks. He has co‐authored a number of academic research papers in the area of AI applications that have been published in professional journals such as IEEE journals and Springer Nature journals.
Kalinka Regina Lucas Jaquie Castelo Branco received her Master’s and Ph.D. degrees in Computer Science from University of São Paulo in 1999 and 2004, respectively. She is currently a Professor of the Institute of Mathematics and Computer Science (ICMC‐USP), working in the department of Computer Systems. She is an active researcher with extensive experience in computer networks, security, embedded systems, and distributed computing.
Willie L. Brown Jr. is Vice Provost for Faculty Affairs at the University of Maryland Eastern Shore, Maryland. He earned his Ph.D. in Business Administration with a specialization in Homeland Security Leadership and Policy (Aviation Safety and Security) from North‐central University and a Master’s degree in Software Engineering from Embry‐Riddle Aeronautical University. Brown also received dual degrees from Elizabeth City State University, North Carolina, with a Bachelor of Science in Aviation Science, and a Bachelor of Science in Computer Science. In addition, Brown is a Federal Aviation Administration (FAA) licensed private pilot. He earned a Management Development Graduate Continuing Education Certification from Harvard University and completed the Leadership Management Program from the Federal Emergency Management Agency. As a member of several aviation professional organizations, he has received numerous awards, most notably from the North Carolina Department of Transportation and the Office of Naval Research/National Aeronautics and Space Administration. Brown serves as the Vice President of Administration of the Eastern North Carolina Institute of Electrical and Electronics Engineers (IEEE) for Geoscience and Remote Sensing Society. Brown has authored/coauthored publications in both national and international peer‐reviewed journals with conference proceedings involving aviation and engineering education.
Joseph Bugeja is an award‐winning computer scientist, advisor, and academic specializing in cyber security, data privacy, and machine learning. He graduated from Malmö University (Sweden) with a Ph.D. in Computer Science in 2021, where he was subsequently awarded the Dissertation of the Year Award for his doctoral dissertation work. After obtaining his Ph.D., Joseph completed a 2‐year postdoctoral fellowship in Computer Science, where he was a member of the Internet of Things and People (IoTaP) research center at Malmö University. He also holds an MSc. degree in Information Security from Royal Holloway University of London, a BSc. (Hons) degree in Computer Science and Artificial Intelligence from the University of Malta, and several industry certifications. Since 2015, Joseph has served as the course director, course developer, and main lecturer for several university courses in Sweden and Malta, with a primary focus on information security. He is acknowledged as a subject matter expert on cyber security, data protection, and the IoT by the European Union Agency for Cybersecurity (ENISA). His research primarily centers on cyber security, privacy, and trust in IoT applications, along with corporate security. Notably, he recently authored an academic textbook on the subject of data protection and IT security. Joseph has more than 20 years of experience working professionally in the software industry, occupying development, leadership, consultancy, and managerial roles specializing in web technologies and information security.
John Byabazaire received his B.Sc. and M.Sc. degrees (by research) in Computer Science from Gulu University, Gulu, Uganda, in 2013 and South East Technological University, Waterford, Ireland, in 2018, respectively. He obtained a Ph.D. in Computer Science from the School of Computer Science, University College Dublin, Dublin, Ireland, in 2024, where he worked on IoT systems for data collection. In 2018, he was an assistant lecturer at Gulu University, Uganda. Currently, John works as a Postdoctoral Research Fellow under the School of Computer Science, University College Dublin, where he researches AI techniques for autonomic end‐to‐end system management across the full cloud‐edge continuum. His research interests include IoT systems for data collection, remote sensing, the Internet of Things (IoT), fog analytics, and applying AI‐based techniques to IoT systems.
Mirothali Chand is currently pursuing his Postdoctoral Research at the ACS Lab, IIT Mandi, where he contributes to a research project on landslide monitoring and early warning systems in the Himalayan region. His work focuses on applying advanced Machine Learning and Reinforcement Learning techniques for real‐time hazard prediction and risk assessment. He received his Ph.D. in Computer Science and Engineering from the National Institute of Technology Puducherry and holds an M.Tech in Computer Science and Engineering from Pondicherry University. His doctoral research was centered on addressing combinatorial optimization problems in Disassembly Sequence Planning using AI, with a particular emphasis on Reinforcement Learning and Meta‐Heuristics. His research interests include Reinforcement Learning, Machine Learning, and Optimization Techniques.
Dimitris Chatzopoulos, Assistant Professor, School of Computer Science at University College Dublin, received his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology and his Diploma and MSc in Computer Engineering and Communications from the University of Thessaly, Greece. In 2014, he was a visiting researcher at École Polytechnique Fédérale de Lausanne and in 2016 at Tsinghua University and Cambridge University. In 2017, he was selected to participate in the 5th Heidelberg Laureate Forum. His research interests include privacy‐preserving and AI‐enabled decentralized applications for mobile and distributed systems.
Andreas Chouliaras has received his Diploma in Electrical and Computer Engineering from the University of Thessaly, Greece, and is currently pursuing his Ph.D. at the School of Computer Science of University College Dublin, Ireland. His research focuses on Human‐AI collaboration and, more specifically in Reinforcement Learning from Human Feedback. His research topic is on Explainable and Interactive Reinforcement Learning. He is investigating how explainable methods can improve understanding and communication efficiency between humans and RL systems and lead to improved performance.
Ibibia K. Dabipi’s research interests include computer security and network management, parallel computing and algorithms development, performance evaluation of computer networks, optimization of transportation networks, and economic analysis of transportation facilities. Dabipi holds Ph.D. in Electrical Engineering (1987) and a Master of Science in Electrical Engineering (1981), Louisiana State University, Louisiana; Bachelor of Science in Electrical Engineering and Bachelor of Science in Physics/Mathematics (1979) from Texas A&I University, Texas. Dabipi was the chairman, Department of Engineering and Aviation Sciences, University of Maryland Eastern Shore. Prior to coming to University of Maryland Eastern Shore, he was the Interim Chairman, and Chairman, Electrical Engineering Department, Southern University, Louisiana. His experiences include working at Bell Communications Research and AT&T Bell Labs as a member of technical staff during the summers of 1984 through 1987. He has authored/co‐authored many technical articles for publications and presentations. He is currently a professor of Electrical Engineering in the Engineering and Aviation Sciences Department.
Paul Davidsson is Professor of Computer Science at Malmö University Sweden. He received his Ph.D. in Computer Science in 1996 from Lund University, Sweden. Davidsson is the Director of the Internet of Things and People Research Centre at Malmö University. His research interests include the application of agent technology, simulation, artificial intelligence, information systems, and data mining. Current application areas include transport and energy systems. The results of this work have been reported in more than 180 peer‐reviewed scientific articles published in international journals, conference proceedings, and books. Davidsson has been member of the program committee for more than one hundred scientific conferences and workshops.
Varun Dutt is a Professor in the School of Computing and Electrical Engineering at IIT Mandi. He earned his Ph.D. in Engineering and Public Policy from Carnegie Mellon University (CMU) in 2011, where he also completed several M.S. degrees. Prior to joining IIT Mandi, he was a post‐doctoral fellow at CMU’s Dynamic Decision‐Making Laboratory. His interdisciplinary research lies at the intersection of computer science, economics, and decision‐making, with a focus on applying computational and experimental models to solve problems in management, environmental policy, and socio‐economic domains. Dr. Dutt serves as an Associate Editor for the Frontiers in Cognitive Science journal and as a Review Editor for the Frontiers in Decision Neuroscience journal. He is also a senior member of IEEE.
Jeanette Eriksson holds a Ph.D. in Software Engineering from Blekinge Institute of Technology, Sweden. Her dissertation, “Supporting the cooperative design process of end‐user tailoring,” emphasizes the need for collaboration between different roles (stakeholders) such as end users, administrators, and software developers in order to achieve sustainable systems. Eriksson’s research has addressed tailorable business systems, sensor games for emotion regulation, games for well‐being, and wearables for managing health and chronic disease. She has worked with health care‐related research for several years and has collaborated with a range of different health care institutions, including hospitals, retirement homes, and senior day care centers. Eriksson is leader of the application area Smart Health in the Internet of Things and People Research Center (IoTaP RC, http://iotap.mah.se/). The IoTaP Research Center collaborates with over 30 industrial partners to conduct research that contributes to the development of Internet of Things (IoT) technologies and applications that are both useful and usable. In particular, the Research Center emphasizes the importance of including “people” as a part of IoT systems.
Akaa Agbaeze Eteng received the BEng degree in electrical/electronic engineering from the Federal University of Technology Owerri, Nigeria, in 2002, the MEng. degree in telecommunications and electronics from the University of Port Harcourt, Nigeria, in 2008, and the Ph.D. degree in electrical engineering from Universiti Teknologi Malaysia, in 2016. He is currently a lecturer with the Department of Electrical/Electronic Engineering, University of Port Harcourt. His research interests include radio frequency identification, wireless energy transfer, radio frequency energy harvesting, and wireless powered communications. He has many publications in these areas.
Farnaz Farid is a Senior Lecturer and Academic Program Advisor for the Cyber Security and Behavior program in the School of Social Sciences. Farnaz is also co‐leading the University's Global Challenge initiative, “Realizing Digital Futures.” Her research spans multidisciplinary fields, including cybersecurity, healthcare technology, and distributed networking, with a strong focus on mitigating online harms, misinformation, and fraud, particularly among vulnerable populations such as Culturally and Linguistically Diverse (CALD) communities, while fostering healthier and safer communities. She has an established track record with over 40 peer‐reviewed publications. Her projects aim to work towards a sustainable future by addressing cybersecurity challenges in critical infrastructure (e.g., healthcare, water), smart cities, smart agriculture, and marine pollution mitigation, leveraging AI and human‐centered digital technologies. She has led multiple projects on chronic diseases, including adolescent diabetes detection, skin cancer detection, reducing hospital readmissions, and assessing mental health conditions in the workplace using Artificial Intelligence Technologies. Farnaz has secured multiple external research grants and was a key researcher in obtaining the prestigious Google exploreCSR funding for two consecutive years. In 2023, she co‐led the successful delivery of the Google exploreCSR program with a diverse cohort of students under the theme: “The Development of AI and Machine Learning to Detect and Analyze Threats for SOC Operations at the Western Clinic for Cybersecurity Aid and Community Engagement (CACE).” Farnaz has over 15 years of experience spanning academia and industry. She joined Western Sydney University as a Lecturer in 2021, following her role as an Associate Lecturer in the School of Computer Science at the University of Sydney. Prior to transitioning to academia, she held several roles at IBM, beginning as an IT Specialist and Application Developer and later advancing to Project Manager. At IBM, she contributed to B2B application integrations, web marketing, and development projects for the company and its business partners, driving revenue growth through digital innovation.
Lucas Finco has years of experience working with utilities on managing the demand side of their business with data analytics, forecasting, and planning. He is a proven innovator, providing state‐of‐the‐art ideas and analyses that are enabling a new, smarter electric grid to emerge. He continues to innovate new concepts and valuation methods that are opening up possibilities for new energy paradigms. Mr. Finco has a B.S. in Applied Math, Physics, & Engineering, an M.A. in Physics, and an MBA in Finance & Entrepreneurship.
Alvis Fong (Senior Member, IEEE) is currently a professor with the Department of Computer Science, Western Michigan University. Prior to that he was a professor at the Auckland University of Technology, New Zealand, and an associate professor with Nanyang Technological University, Singapore. His research interests include information processing and management, multimedia, and communications.
Bernard Fong received his Bachelor’s degree in electronics from the University of Manchester Institute of Science and Technology and Doctor of Philosophy degree in health information systems from the University of New South Wales in 1993 and 2005, respectively. He is a professor with the Department of Electrical Engineering, Chang Gung University. He currently serves as the managing editor for the IEEE Smart Cities Newsletter and Academic Editor for PLOS ONE. He has over 150 scientific publications with some of his work published in top tier journals such as IEEE Communications Magazine, IEEE Wireless Communications, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Evolutionary Computation, Annals of the Rheumatic Diseases, and Nature.
João Vitor de Carvalho Fontes
