For those in similar professional circles as I am in, i.e. looking forward into the Generative AI space, yet with one foot pragmatically and firmly stuck in Machine Learning (ML) and Deep Learning (DL) techniques of the (recent, ok, not very distant) past, you will find Dr Amita Kapoor's recent book Hands-On Artificial Intelligence for IoT: Expert Machine Learning and Deep Learning Techniques for developing smarter IoT systems, 2/ed published by PackT a very useful resource into the use of these techniques applied to applications in the Internet of Things (IoT) domain. My own interest in IoT is driven primarily by previous personal (and failed) forays into Home Automation, but I do have some background in ML and DL techniques. So I approached this book from the perspective of a reader trying to understand the challenges and applications of these techniques in the IoT domain. This perspective shaped my reading of the book, and to some extent this review as well, as I looked for insights that would help me bridge my existing knowledge with the nuances of the IoT domain.
The book is organized into 4 parts. The first part introduces foundational techniques that are common to both the fields of AI (this term includes ML and DL) and IoT, while the second part covers advanced techniques. The third part focuses on specific IoT applications and AI techniques to handle them, while the fourth part covers IoT applications at different levels of granularity (personal/home, industrial, smart cities, etc.). The book is quite large (approximately 400 pages) and covers a lot of ground, some of which you may already be familiar with depending on your background. However, even in those cases, it may be worthwhile to skim the text to make sure you don't miss something you didn't know about, since things move quickly in this field. In any case, I present below my summary of each chapter, organized into a loose table of contents type structure. Hopefully they help you make the decision to read versus skim and optimize your reading experience.
- Part I: Principles and Foundations of IoT and AI
- Principles and Foundations of IoT and AI -- covers the theoretical foundations of IoT (think ISO network stack), various applications, and the necessity of using Big Data techniques and ML. It concludes with a list of tools used in the text, which includes Keras3.0 to support DL in IoT applications.
- Data Access and Distributed Processing for IoT -- this chapter covers processing data in various formats (text, CSV, Excel, JSON, HDFS, and various SQL and NoSQL databases) using Python. This is because IoT devices often present data in proprietary formats, and you need to be able to read it into your application.
- Machine Learning for IoT -- covers traditional ML algorithms such as Naive Bayes, Logistic Regression, Decision Trees, SVM, etc (remember my quip about having one foot firmly in the distant ML past? This is about as far back you would go), and one example using a simple DL model. Even though these may not be on par with more recent models such as BERT or small LLMs, these are typically deployed for solving simpler problems and have lower latency requirements, and are often adequate for the problem at hand.
- Part II: Advanced AI Techniques and their application in IoT
- Deep Learning for IoT -- introductory DL chapter, covers DL basics, CNN, RNN and AutoEncoders. It also provides a brief description of OpenVINO for IoT vision applications and TinyML for low-power on-device analytics, and using Keras Tuner for Hyperparameter Tuning.
- Techniques for IoT -- explores alternative optimization techniques to Gradient Descent (GD) such as Simulated Annealing and Swarm Optimization. Also covers the use of Evolutionary and Genetic Algorithms (EA and GA) using libraries such as PyGAD and DEAP. While not mentioned explicitly, I will guess that EA/GA are included here because they are less resource intensive compared to GD, and can often be more efficient depending on application.
- Reinforcement Learning for IoT -- this chapter covers the basics of Reinforcement Learning (RL), Q-Learning (DQN, DDQN, Policy Gradients, etc). As before RL based training can be particularly suitable for IoT applications because they are physics based and reinforcement signals can be cheaper to obtain and more relevant compared to supervision signals.
- Generative Models for IoT -- this chapter covers Generative Adversarial Networks (GAN) and Variational AutoEncoders (VAE), which are probably not the Generative Models you had in mind if you are in the current "GenAI" space, but these are the OG models that generate images from noise (rather than the next token from a stream of tokens). Primrily their utility in the IoT space seems to be data generation and simulation (GAN) and anomaly detection (VAE).
- Part III: Implementing Intelligent IoT Solutions in Diverse Domains
- Distributed Learning using Keras -- this chapter covers Distributed training using Keras3 (using the JAX backend). This is useful information if you were just curious about Keras3 distributed capabilities. The relevance of this to the IoT space is that training data may be aggregated from multiple edge devices, say for recommendations, or multiple resource constrained edge devices may be used to retrain on new data, such as maintenance models in industrial IoT systems.
- AI Cloud Platforms for IoT -- covers the need for Cloud based APIs in the context of IoT, and IoT adjacent services provided by popular providers such as AWS, Azure and Watson. Also covers these providers from the point of view of ML services, including Google VertexAI and AutoML, AWS SageMaker and Bedrock, and IoT specific services such as AWS IoT Core, Azure IoT Hub and GCP IoT code.
- Deep Learning for Time Series Data from IoT -- covers working with time series data using traditional algorithms such as Prophet and Spark-ML, and wirth recurrent neural networks (RNN), and using pre-trained Temporal Convolutional Networks (TCN) models such as Chronos. This is particularly relevant since IoT devices emit streams of data over time that can be analyzed and extrapolated to predict the future.
- Leveraging AI for Visual Data from IoT -- covers the processing of visual data from IoT systems, including image segmentation and object detection and classification. Architectures covered include CNN, TCN, and ViT (Visual Transformers).
- AI for Text, Audio and Speech Data from IoT -- IoT devices can listen for particular sounds or speech patterns in their input, so this chapter covers mechanisms for IoT devices to process speech and audio, as well as free-form text input from users.
- Part IV: Applying AI and IoT in Real-World Scenarios
- AI for Personal and Home IoT -- mainly covers Personal and Home IoT applications, and considerations for creating them, along with a case study on a Smart Home implementation. It also includes pointers on getting started on your own IoT projects.
- AI for IIoT -- there are already many IoT applications in use in industrial environments, and this chapter describes instances of these in various industries. Application areas are not only in manufacturing support, but could also be for preventative maintenance and forecasting load.
- AI for Smart Cities IoT -- I felt initially that this may a bit of an aspirational chapter, in the sense that the typical reader of this book is unlikely to be in a position to influence the use of AI for smart cities, but the examples proved me wrong. Many of these are examples of smart solutions to everyday problems that are well within the realm of influence of people working for cities or local government, directly or indirectly.
In summary, I found this book to be a comprehensive resource to understand the concepts behind IoT applications. It's breadth of coverage is truly impressive -- spanning essential principles of IoT and AI, traversing through machine learning, deep learning, and optimization techniques, and culminating in thorough discussions on real-world deployments across domains such as smart homes, industrial IoT, and smart cities. While the book’s extensive coverage of fundamentals in areas like machine learning and distributed processing may at times feel broader than strictly necessary for readers already well-versed in these fields, it ensures that the material remains accessible to a broader spectrum of readers.
The progression of chapters from core principles to practical case studies equips readers with a strong theoretical foundation as well as a practical understanding of how intelligent systems can be implemented in the IoT space. The inclusion of dedicated chapters on time series analysis, computer vision (CV), and Natural Language and Audio processing, offer readers additional perspective in these areas. While I don't see an IoT applications in my immediate future, it was an interesting read, and having read it, I feel more confident about being able to tackle one should it come about.
