Handbook of Blockchain Intelligence
to be published in " Blockchain Technologies" series

All the book chapters published in this book indexed in ISI
Web of Science, DBLP, SCOPUS, Ulrichs, MathSciNet,
Current Mathematical Publications, Mathematical Reviews,
Zentralblatt Math: MetaPress and Springerlink.
Editors:
1. Prof. Zibin Zheng, School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
Zibin Zheng received the Ph.D. degree from The Chinese University of Hong Kong in 2011. He is currently a Full Professor with Sun Yat-sen University, Guangzhou, China. His research interests include service computing, software engineering, and blockchain. He was a recipient of the IBM Ph.D. Fellowship Award. He received the ACM SIGSOFT Distinguished Paper Award at the ICSE 2010 and the Best Student Paper Award at the ICWS 2010. He is a senior member of IEEE.

2. Prof. Hong-Ning Dai, Faculty of Information Technology, Macau University of Science and Technology, Macau, China
Hong-Ning Dai received the Ph.D. degree in computer science and engineering from the Department of Computer Science and Engineering, Chinese University of Hong Kong. He is currently an Associate Professor with the Faculty of Information Technology, Macau University of Science and Technology. He also holds visiting positions at the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, the School of Electrical Engineering and Telecommunications, the University of New South Wales and Hong Kong Applied Science and Technology Research Institute. His research interests include big data analytics, Internet of Things, and blockchain. He is an associate editor of IEEE Access and guest editors of IEEE Transactions of Industrial Informatics. He is a senior member of IEEE. He was awarded with BOC Excellent Research Award of Macau University of Science and Technology in 2015.
Scope of the book:

Blockchain technology has received extensive attention recently due to its provision of secure and decentralized resource sharing manner. Despite the merits of decentralization, immutability, non-repudiation and traceability of blockchain, the development of blockchain technology has undergone a number of challenges such as difficulty of data analytics on encrypted blockchain data, poor scalability, software vulnerabilities of blockchain and scarcity of appropriate incentive mechanisms.

The recent advances in artificial intelligence (AI) have greatly promoted the evolution of conventional computer-aided industry to intelligent industry. The integration of AI with blockchain has the potentials to overcome the limitations of blockchain. For example, machine learning-based approaches may help to analyze the blockchain data and to identify the misbehaviors in blockchain. In addition, deep reinforcement learning methods can be used to improve the reliability of blockchain systems. On the other hand, AI technology heavily depends data availability. For example, deep learning algorithms often require a training process on massive authentic data. However, the data can be falsified, polluted and poisoned thereby disturbing the training model. Thus, it is critical to assure data authentication, provenance and traceability. Blockchain offers a solution to these issues.

There have been substantial developments in blockchain technologies over the last few years. These research outcomes have been extensively reported in journals, conferences and books. Different from most existing books, this book will mainly concentrate on intelligence of blockchain systems. In other words, this book will focus on using AI to improve blockchain systems and blockchain-enabled AI. Areas of this book include (but not limited to), performance optimization of blockchain systems, auto-bug fixing of smart contracts, identifying malicious behaviors in blockchain, applications driven by blockchain intelligence.

This book aims to provide the state-of-the-art research advances in the area of artificial intelligence in blockchain. It presents insights into big data analytics of blockchain data, AI-enabled blockchain systems, and applications driven by blockchain intelligence. This book will be a valuable resource for students, researchers, engineers, policy makers working in various areas related to blockchain intelligence.

Topics of Interest:

Blockchain technology being characterized by decentralization, immutability, non-repudiation and traceability is reshaping the industry while it also poses challenges such as difficulty of data analytics on encrypted data, poor scalability, software vulnerabilities and scarcity of appropriate incentive mechanisms. Meanwhile, recent advances in artificial intelligence have the potentials to overcome the limitations of blockchain. In particular, machine learning approaches can analyse blockchain data and identify the misbehaviours in blockchain. This book provides a comprehensive coverage of up-to-date conceptual frameworks in AI-enabled blockchain systems, automated smart contracts on top of blockchain and applications driven by blockchain intelligence. Different from other existing books, this volume predominately focuses on AI-enabled blockchain systems. Areas covered big data analytics of blockchain data, AI-enabled blockchain systems, and applications driven by blockchain intelligence.

We welcome book chapter contributions on the following (but not limited to) themes.

Table of Contents:
  • AI-enabled Operation and Maintenance of Blockchain Systems
  • AI-based Optimization Strategies of Smart Contracts
  • Auto-fixing Bugs on Blockchain-enabled Smart Contracts
  • Auto-detection of Malicious Smart Contracts
  • Cloud and Edge Computing Orchestration for Data Analytics on Blockchain
  • Novel Machine Learning Approaches for Blockchain Data Analysis
  • Misbehavior Detection on Blockchain Data
  • Intelligent Identification of Illegal Blockchain Accounts
  • Blockchain Intelligence Driven Internet of Things
  • Finance Technology based on Blockchain Intelligence
  • Sharing Economy based on Blockchain Intelligence
  • Blockchain Intelligence Driven Data Provenance
  • Submission Procedure:
    Submission of chapter(s) via e-mail only:
    Please send your one-page write up (with abstract of 300- 500 words and 6 keywords) of your chapter along with tentative TOC to Prof. Hong-Ning Dai (blockchain.intelligence.handbook@gmail.com) with the subject Springer Blockchain Technologies - Book Chapter Upon acceptance of the proposal, further instructions for submission guidelines according to the Springer will be communicated.
    Important Dates:
    Last date for submission of Chapter proposal by Authors: December 15, 2019
    Notice of Book Chapter Proposal to author: January 15, 2020
    Estimated Full Chapter Submission by Author: March 1, 2020
    Final Acceptance/Rejection of Chapters: April 20, 2020
    Estimated Manuscript Completion Date: June 1, 2020
    Contact:
    Zibin Zheng
    School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
    Email: zhzibin@mail.sysu.edu.cn
    Hong-Ning Dai
    Faculty of Information Technology, Macau University of Science and Technology, Macau SAR, China
    Email: hndai@must.edu.mo