China has a complete industrial system and a huge market. In the process of integrating the two industries, the digital transformation and interoperability in the industrial manufacturing sector continue to promote the construction and development of related industries. A new industrialization development strategy supported by information technology has gradually formed an efficient, environmentally friendly, and sustainable development model. In May 2015, the State Council deployed a comprehensive implementation of the strategy to strengthen the manufacturing power, formulating the top-level planning and development roadmap for China’s manufacturing industry, aiming to strengthen industrial foundation capabilities and promote industrial transformation and upgrading. In October of the same year, China and Germany announced cooperation to jointly promote the new industrial revolution and business models. In December 2021, the Ministry of Industry and Information Technology and seven other departments jointly issued the “14th Five-Year Plan for Intelligent Manufacturing Development,” emphasizing that intelligent manufacturing is the main focus of building a manufacturing power, and its development level directly affects the quality level of China’s manufacturing industry. The development of intelligent manufacturing is of great importance for consolidating the foundation of the real economy, establishing a modern industrial system, and achieving new industrialization. By 2025, most manufacturing enterprises above designated size will achieve digital networking, and key industry backbone enterprises will initially apply intelligence; by 2035, manufacturing enterprises above designated size will fully popularize digital networking, and key industry backbone enterprises will basically achieve intelligence.
With the development and popularization of information and communication technologies such as edge computing and artificial intelligence, the physical resources and computing power of various interconnected systems have significantly increased.“Industrial Big Data Analysis Driven by Edge Computing: Theory, Technology, and Applications” (Gao Cong, Ma Lichuan, Chen Yanping, Feng Jie, published by Science Press, Beijing, March 2024) is supported by the Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province at Xi’an University of Posts and Telecommunications and the Key Laboratory of Blockchain and Secure Computing of Shaanxi Province at Xi’an University of Electronic Science and Technology. Since 2014, the author team has been committed to theoretical research and industrialization work in edge computing and big data analysis.
Author Profiles
Gao Cong, PhD, graduated from Xi’an University of Electronic Science and Technology with a bachelor’s, master’s, and doctoral degree. After obtaining his doctorate, he began teaching at Xi’an University of Posts and Telecommunications and is currently an associate professor at the School of Computer Science, a master’s supervisor, and a key member of the Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province. His main research and teaching work focuses on data perception and analysis, edge computing, and computer networks.
Ma Lichuan, PhD, associate professor at the School of Network and Information Security of Xi’an University of Electronic Science and Technology, master’s supervisor, and a key member of the Key Laboratory of Blockchain and Secure Computing of Shaanxi Province. In recent years, he has focused on trust management mechanisms and privacy protection in the context of edge computing.
Chen Yanping, PhD, dean of the School of Computer Science at Xi’an University of Posts and Telecommunications, professor, and master’s supervisor. His main research and teaching work focuses on service computing, industrial intelligence, and computer networks. He is the leader of the “Industrial Big Data Analysis and Intelligent Processing Innovation Team” of the Youth Innovation Team in Shaanxi Province, a senior member of the China Computer Society, a member of the CCF Network and Data Communication Professional Committee, and a member of the Edge Computing Committee of the China Communication Society.
Feng Jie, PhD, associate professor at the School of Communication Engineering of Xi’an University of Electronic Science and Technology, master’s supervisor. His main research and teaching work focuses on mobile edge computing, blockchain, deep reinforcement learning, resource scheduling, federated learning, and distributed computing.
“Industrial Big Data Analysis Driven by Edge Computing: Theory, Technology, and Applications” is based on nearly 10 years of research innovation and promotion of industry-university-research achievements by the author team, and it also gathers recent research progress from both domestic and international sources. The authors comprehensively introduce the theoretical analysis and engineering applications of the entire data lifecycle around the new computing paradigm—edge computing. By elaborating on the theory, technology, and applications of data analysis in the industrial sector under the era of big data, a knowledge system that traces back to the source, progresses step by step, and is interlinked is constructed. Instances and solutions to classic problems related to data analysis in edge computing are provided in application scenarios.
Through theoretical research, experimental simulation, and empirical testing, the authors believe that the research on industrial big data analysis driven by edge computing has the following important significance:
Due to the particularity of the industrial sector, the industrial supply chain system is highly complex, requiring more reasonable and efficient industrial collaboration and resource allocation to maintain the security and stability of the industrial chain. Based on digital technology and service products carried by the industrial internet platform, the construction of national manufacturing innovation centers, intelligent manufacturing, industrial strength enhancement, green manufacturing, and high-end equipment innovation should be implemented as five major projects to achieve breakthroughs in key common technologies that have long restricted the development of China’s manufacturing industry, thus accelerating China’s transformation from a manufacturing giant to a manufacturing power.
In the industrial manufacturing environment, based on the information contained in physical and network spaces, networked devices can form efficient collaborations, and various production lines and processes continuously generate massive amounts of data. To better manage and utilize massive data, traditional factories need to be transformed into smart factories under the Industry 4.0 environment. The number of devices at the network edge and the scale of data generated in the era of Industry 4.0 are experiencing explosive growth, and the centralized processing mode of traditional cloud computing cannot efficiently handle the massive data generated at the network edge. As a complementary computing paradigm to cloud computing, edge computing compensates for the inherent shortcomings of cloud computing, deploying computing power on the data generation side at the network edge, which can significantly reduce transmission delays and alleviate network bandwidth pressure, avoiding the bottleneck of centralized processing. Furthermore, user privacy data is stored and processed on edge devices, eliminating the need to upload to the cloud, ensuring data security and privacy. Therefore, edge computing has been recognized by academia and industry as an indispensable driving force for innovative development in the field of industrial big data.
This book researches the theory and application of industrial big data analysis driven by edge computing.
-
First, it introduces the research background of industrial big data from the perspectives of Industry 4.0, cyber-physical systems, and big data, elaborating on the basic principles and core components in the industrial sector, and discussing the definition, sources, types, and challenges of big data in detail.
-
Then, based on a comparative analysis of edge computing and traditional cloud computing, it elaborates on the overall architecture and unique advantages of edge computing, discusses key technologies and development trends of edge computing in typical application scenarios, presents the challenges faced by edge computing, and proposes an edge data collection scheme for wireless sensor networks.
-
Next, it analyzes data anomaly detection and data pattern anomaly detection in distributed environments under edge-cloud collaboration, detailing the pros and cons of existing methods, analyzing existing problems, and designing new algorithms based on three basic components: support vector machines, locality-sensitive hashing, and high-dimensional feature representation, proposing innovative solutions.
-
Regarding the evaluation and selection of massive homogeneous services in mobile edge computing environments, it designs a time-aware tensor model and proposes a service quality data prediction scheme based on temporal regularized tensor decomposition.
-
Finally, it looks forward to Industry 5.0 and provides future research directions.
Book Features
-
Thoroughly elaborates on the theory, technology, and applications of data analysis in the industrial sector in the era of big data.
-
Comprehensively introduces theoretical analysis and engineering applications of the entire data lifecycle around the new computing paradigm—edge computing.
-
Constructs a knowledge system that traces back to the source, progresses step by step, and is interlinked.
-
Each chapter includes theoretical reviews, key issues, innovative solutions, references, and annotations.
-
Provides instances and solutions to classic problems related to data analysis in application scenarios.
This book is the first academic monograph to comprehensively introduce industrial big data analysis driven by edge computing in China and abroad, with content that progresses step by step and is easy to understand, emphasizing the close integration of theory and practical application. It discusses core knowledge in the field of industrial big data analysis oriented towards edge computing in detail, covering related issues, theoretical methods, technical implementations, and solutions. It is suitable for master’s students, doctoral students, and other researchers conducting research in related fields.
Read Science Together!
Science Press | WeChat ID: sciencepress-cspm
Professional Quality Academic Value
Original Good Read Scientific Taste
Science Press Video Account
Hardcore and Informative Audiovisual Science
Disseminating science, welcome to light up★ the star mark, like, and see ▼