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For Authors
Submit a ProposalSharded Blockchain for In-Game Asset Management
One Line Description
Master the technology powering the future of gaming with this definitive guide, combining theoretical depth and hands-on implementation to teach you how to build scalable, fraud-resistant, and fully decentralized virtual economies using blockchain sharding.
Description
The gaming industry is rapidly evolving into decentralized, user-driven ecosystems where players own, trade, and monetize digital assets. However, traditional blockchains face scalability and performance limitations, making them ill-suited for real-time, high-volume gaming environments. Blockchain sharding offers a transformative solution by partitioning networks to boost throughput and reduce transaction latency. This book contextualizes blockchain sharding within broader trends such as tokenization, NFTs, cross-platform gaming, and metaverse integration, providing a multidisciplinary framework that aligns with advances in AI, cryptography, and distributed systems to support dynamic, interoperable virtual economies. It begins by establishing the foundational understanding of digital asset management in gaming, followed by an exploration of the limitations of current systems and the emergence of blockchain as a secure alternative. The core of the book explains blockchain sharding, its principles, protocols, and scalability benefits, before delving into its practical applications in gaming ecosystems. Through detailed chapters, the book unpacks how sharded infrastructures enhance transaction throughput, prevent fraud, enable real-time peer-to-peer trading, and ensure asset immutability in distributed environments. By combining theoretical depth with practical implementation, the book not only equips readers with the knowledge to design robust decentralized gaming systems but prepares them to contribute to research and innovation in the evolving landscape of digital economies and decentralized infrastructures.
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The gaming industry is rapidly evolving into decentralized, user-driven ecosystems where players own, trade, and monetize digital assets. However, traditional blockchains face scalability and performance limitations, making them ill-suited for real-time, high-volume gaming environments. Blockchain sharding offers a transformative solution by partitioning networks to boost throughput and reduce transaction latency. This book contextualizes blockchain sharding within broader trends such as tokenization, NFTs, cross-platform gaming, and metaverse integration, providing a multidisciplinary framework that aligns with advances in AI, cryptography, and distributed systems to support dynamic, interoperable virtual economies. It begins by establishing the foundational understanding of digital asset management in gaming, followed by an exploration of the limitations of current systems and the emergence of blockchain as a secure alternative. The core of the book explains blockchain sharding, its principles, protocols, and scalability benefits, before delving into its practical applications in gaming ecosystems. Through detailed chapters, the book unpacks how sharded infrastructures enhance transaction throughput, prevent fraud, enable real-time peer-to-peer trading, and ensure asset immutability in distributed environments. By combining theoretical depth with practical implementation, the book not only equips readers with the knowledge to design robust decentralized gaming systems but prepares them to contribute to research and innovation in the evolving landscape of digital economies and decentralized infrastructures.
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Author / Editor Details
Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 15 years of teaching experience. He has authored seven books, edited 110 books, and published more than 260 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.
Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 15 years of teaching experience. He has authored seven books, edited 110 books, and published more than 260 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.
Pooja Dixit is an Assistant Professor in the Department of Computer Science at Shri Ratanlal Kanwarlal Patni Girls' College, Kishangarh, India. With more than ten years of academic teaching and two years of research experience, she has published more than 30 research papers in reputed journals, books, and conferences. Her research interests include artificial intelligence, machine learning, and data mining.
Pramod Singh Rathore, PhD is an Associate Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur, Jaipur, India. He has more than 125 publications in reputable, peer-reviewed national and international journals, authored and edited books, and conferences. His research interests include spatial network, computer networks, medical image processing and machine learning.
C. Dhaya, PhD is a Professor and Head of Computer Science and Engineering at Adhiparasakthi Engineering College, Melmaruvathur, Tamil Nadu, India. She has published more than twenty research papers in reputed international journals and conferences and several patents. Her areas of expertise include machine learning, data science and big data, software architecture evaluation, genetic algorithms, and multi-criteria decision making.
R. Srivel, PhD is an Assistant Professor of Computer Science and Engineering and a Research Coordinator in the Civil Engineering Department at Adhiparasakthi Engineering College, Melmaruvathur, Tamil Nadu, India. With a strong academic foundation and over a decade of teaching and research experience, he has published more than thirty research papers in science citation journals, book chapters, and conferences, and several patents. His expertise encompasses diverse fields, including drone technology, artificial intelligence, and healthcare applications.
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