Tie Qiu – författare

The IoT topology defines the way various components communicate with each other within a network. Topologies can vary greatly in terms of security, power consumption, cost, and complexity. Optimizing the IoT topology for different applications and requirements can help to boost the network’s performance and save costs. More importantly, optimizing the topology robustness can ensure security and prevent network failure at the foundation level. In this context, this book examines the optimization schemes for topology robustness in the IoT, helping readers to construct a robustness optimization framework, from self-organizing to intelligent networking.

The book provides the relevant theoretical framework and the latest empirical research on robustness optimization of IoT topology. Starting with the self-organization of networks, it gradually moves to genetic evolution. It also discusses the application of neural networks and reinforcement learning to endow the node with self-learning ability to allow intelligent networking.

This book is intended for students, practitioners, industry professionals, and researchers who are eager to comprehend the vulnerabilities of IoT topology. It helps them to master the research framework for IoT topology robustness optimization and to build more efficient and reliable IoT topologies in their industry.

This book serves as a pivotal guide for professionals and researchers within the industrial computing domain, offering an extensive examination of edge computing in industrial environments. Tailored for individuals possessing a foundational understanding of industrial computing systems, it aims to augment their knowledge concerning the role and capabilities of edge computing in this dynamically evolving sector.

In an era where real-time, reliable, and scalable computing solutions are of paramount importance, traditional cloud computing models grapple with challenges such as latency,  bandwidth limitations, data sovereignty, and privacy concerns. This book positions edge computing as a crucial evolution in industrial data processing and analytics, specifically addressing these challenges. It introduces a distinctive three-layer industrial edge computing architecture that integrates device, edge, and application layers, explicitly designed to accommodate the intricacies of the Industrial Internet of Things (IIoT).

Beyond elucidating the theoretical foundations of edge computing, the book delves into its practical applications, with a particular emphasize on edge-assisted model inference as a key scenario. It offers insightful case studies and discussions on the integration of edge computing with artificial intelligence (AI), illustrating how this collaboration is revolutionizing industrial systems. A comprehensive understanding of the material is facilitated by a background in computer science, industrial engineering, IoT, and cloud computing.