About the Book
The rapid proliferation of edge devices is transforming how data is generated and processed in real time. Traditional cloud-based approaches often introduce latency, inefficiencies, and security concerns, making them less suitable for time-sensitive applications. At the same time, generative AI has emerged as a powerful paradigm, capable of producing complex and creative outputs but typically reliant on large-scale, resource-intensive infrastructure. Bridging the gap between these capabilities and the constraints of edge environments presents a critical challenge for enabling responsive, efficient, and intelligent systems at the network's edge. Enhancing Edge Devices With Generative Intelligence: Architecture, Challenges, and Applications examines how to bridge the gap between generative AI capabilities and the constraints of resource-limited edge computing environments. By enabling generative intelligence to operate directly on edge devices, it highlights the potential to create applications that are not only powerful and creative but also fast, secure, and efficient. This book outlines the key architectures, integration strategies, and challenges associated with deploying generative models at the edge, while also exploring practical applications across diverse domains. Covering topics such as real-time decision-making, adaptive feedback-bound generative architecture (AFGA), and distributed compliance intelligence, this book is a critical emerging resource for graduate and doctoral students, educators, researchers, developers, and industry professionals working on generative AI, IoT, Industrial IoT, edge AI, and more.
About the Author :
Sarbagya Shakya , a Senior IEEE member, is an Assistant Professor at Eastern New Mexico University, New Mexico, USA. He received a B.Eng. in Electronics Engineering from National College of Engineering, Tribhuvan University of Nepal in 2009 and an M.Eng. in Computer Engineering from Nepal College of Information Technology, Pokhara University of Nepal in 2014. He received his Ph.D. in Computational Science (Computer Science) from the School of Computer Science and Computer Engineering, University of Southern Mississippi, USA. He has over 10 years of experience in academia and research. His research interests include applied machine learning, deep learning, image processing, Internet of Things (IoT), Industrial IoT, Edge Computing, and TinyML, and he has published journal papers, conference papers, and book chapters in different domains on applied machine learning and deep learning. Tej Shahi , IEEE Member, is a dedicated researcher and academic with over fifteen years of experience in teaching and research within Computer Science and information technology across Nepal and Australia. He received a B.Sc and an MSc in Computer Science with distinction from Tribhuvan University, Nepal, and a PhD in Computer Science from Central Queensland University, Australia. He was awarded gold medals by the president of Nepal for his outstanding academic achievements in his undergraduate and postgraduate studies. His doctoral research focused on the integration of remote sensing, image processing, and machine learning to optimise agricultural systems using artificial intelligence and drone technology, aiming to enhance efficiency and sustainability. His scholarly impact is evidenced by his significant presence in the academic community, including an H-index of 21 and an i10-index of 29 on Google Scholar. He is listed among the top 2% scientist by the Stanford Elsevier ranking for 2025. His research findings have been disseminated through high-quality publications in esteemed journals such as AI Review, Remote Sensing, and IEEE Transactions. Edgar Ceh-Varela is an Assistant Professor of Computer Science at Eastern New Mexico University (ENMU). He received his Bachelor's degree in Computer Systems Engineering from the Instituto Tecnológico de Mérida, a Master's in Information Technology from the Universidad Interamericana para el Desarrollo and a Doctorate in Computer Systems in 2013 from Universidad del Sur, all from Mexico. In 2021, he obtained his Ph.D. in Computer Science from New Mexico State University (NMSU), USA. Dr. Ceh-Varela has been working in the computer science field for over 25 years. In the past years, he has published articles on applied machine learning, deep learning, natural language processing, agentic AI, wearable devices and edge computing. Michael Cowling has been a leader in educational technology for over 20 years and was the recipient of the Universities Australia 2020 AAUT Award for Teaching Excellence (Physical Sciences & Related Studies). He is currently a Professor in Computing Technologies in the STEM College at RMIT University, where he serves as Director, Hub for Apple Platform Innovation (HAPI), an Apple-focussed teaching/research space using novel computing platforms to create dynamic new experiences. He is the President (2023 to 2025) of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), where he is also a Community Fellow, as well as the Vice-President of the Open Access Publishing Association (OAPA). He is also a Fellow of the Australian Computer Society (ACS) and a Senior Fellow of the Higher Education Academy (Advance HE UK). Through his mantra ‘pedagogy before technology’, he fosters thoughtfulness in technology for students, educators and the public.
