Preface: The 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering ^†

This volume presents the proceedings of the 2024 IEEE 6th Eurasia Conference on the IoT, Communication, and Engineering (IEEE ECICE 2024). The IEEE ECICE is an interdisciplinary science and engineering conference organized through the collaboration of the Tainan Section Sensors Council (IEEE TSSC), the College of Engineering at National Formosa University, the Smart Machinery and Intelligent Manufacturing Research Center, and the International Institute of Knowledge Innovation and Invention (IIKII). IEEE ECICE 2024 provides a unified communication platform for researchers investigating the Internet of Things (IoT) and advanced manufacturing. The booming economic development in Asia, chiefly driven by the automobile, machinery, computer, communication, consumer product, flat panel display, semiconductor, and micro/nano industries, has greatly facilitated collaboration between universities, research institutions, and industrial corporations. Therefore, the IEEE ECICE serves as a broad international forum for researchers, engineers, and professionals worldwide, fostering discussion and the exchange of ideas on scientific, technological, and management aspects in the fields of IoT and manufacturing. The main theme of this conference is smart manufacturing, focusing on new and emerging technologies. This conference fosters strong interactions between researchers as they share and disseminate high-quality research results. Figure 1 shows a group photo of the conference’s opening ceremony.

At IEEE ECICE 2024, two valuable keynote speeches related to the conference topics were presented. The first keynote speech was entitled “Toward 6G-Enabled Mobile Edge Intelligence” and presented by Distinguished Professor Ai-Chun Pang from National Taiwan University, Taiwan (Figure 2). With the explosive development of AI, edge intelligence is considered a must in developing future 6G mobile communications systems and providing timely responses to emerging applications on mobile devices. In 6G, the computation-intensive AI tasks will be distributed at the network edge, and the communications paradigm will shift from conventional symbol transmission to semantic information delivery. Professor Ai-Chun Pang presented the key features of 6G mobile networks regarding distributed AI learning driven by edge intelligence. The effects of limited labeled and non-IID data in the edge-intelligence environment were also elaborated. Professor Ai-Chun Pang discussed the vulnerability of the edge intelligence framework and defense methods against privacy leakage and security threats and introduced a GenAI-based semantic encoder to prioritize task-oriented communication, featuring the concept of understanding before transmitting and delivering the intended meaning of messages for the pervasive computing of connected intelligence.

The second keynote speech was delivered by Luther Brown-Distinguished Chair Professor F. Frank Chen from the University of Texas at San Antonio, the USA. Professor F. Frank Chen presented “Realizing Smart Manufacturing through the Lean AI Paradigm: an AI-Enabled Lean Manufacturing Practice with Versatile Convolutional Neural Network” (Figure 3). The integration of lean manufacturing tools with artificial intelligence (AI) is transforming smart manufacturing by optimizing production processes, minimizing waste, and enhancing overall efficiency. Traditional lean practices focus on waste reduction and process improvement, primarily relying on human expertise for problem identification and resolution. AI algorithms, on the other hand, excel in pattern recognition, data analysis, and decision-making. The integration of lean tools and AI enables more precise, data-driven solutions for common manufacturing challenges. AI algorithms automate and refine lean techniques, such as value stream mapping, Kanban, and 5S, and provide actionable insights based on big data. This fusion of lean manufacturing and AI enables predictive maintenance, quality control, and optimization, enhancing the efficiency and responsiveness of the manufacturing process. Moreover, AI’s capacity for machine learning allows the integrated system to adapt and improve processes autonomously over time, further aligning with the continuous improvement ethos of lean manufacturing. Case studies were presented to show how this alignment facilitates lean manufacturing. Professor F. Frank Chen pointed out that the successful implementation of lean manufacturing necessitates overcoming data quality and algorithmic bias challenges. Despite these hurdles, lean tools and AI are redefining the best practices in manufacturing, setting new standards for operational excellence.

The IEEE ECICE 2024 was held in a hybrid form, constituting a mix of on-site and online presentations. The conference received a total of 229 submissions, with 162 papers finally selected and registered. The participants represented a diverse group from 17 countries, including Cambodia, China, Croatia, Ecuador, Germany, Indonesia, Japan, Macau, Malaysia, Taiwan, the Philippines, the USA, Turkey, the UK, and Vietnam. The participants presented their findings on various topics across 17 Regular Sessions and one Invited Session. Figure 4 and Figure 5 show on-site and online oral presentation sessions held at IEEE ECICE 2024.

At IEEE ECICE 2024, a wealth of substantial findings were shared by enthusiastic participants. Following a rigorous peer-review process, 103 outstanding papers in relevant engineering fields were selected for publication in Engineering Proceedings (ISSN: 2673-4591, indexed by Scopus). The conference proceedings are expected to foster interdisciplinary collaboration among science and engineering academicians, enhance academic and industrial collaboration, and strengthen international networking.