Exploring Explainable IoT: Recent Trends, Challenges, and Future Directions offers an in-depth exploration of the emerging field of explainability within the Internet of Things (IoT) landscape. As IoT systems become more intelligent and autonomous—integrating AI and machine learning for tasks such as smart decision-making, automation, and real-time analytics—the demand for transparency and interpretability has grown significantly. This book provides a comprehensive overview of the latest developments in Explainable IoT, addressing how explainability can enhance trust, accountability, and human understanding in complex, data-driven environments. It examines the current trends driving research and innovation, from edge-based explainable models to user-centric design, while also confronting critical challenges such as the trade-off between model accuracy and interpretability, limited computational resources on IoT devices, and data privacy concerns. The book brings together interdisciplinary perspectives, combining insights from artificial intelligence, human-computer interaction, embedded systems, and cloud-edge computing to propose novel frameworks and architectures for building more transparent IoT solutions. It also outlines promising future directions, including the role of explainability in ethical AI, regulatory frameworks, and adaptive systems that learn and explain in real-time. Aimed at researchers, practitioners, and students, this book serves as both a foundational resource and a forward-looking guide for developing explainable and trustworthy IoT technologies across a range of application domains.