Integrating IoT and 6G: Applications of Edge Intelligence, Challenges, and Future Directions
Qiang He, Jinqiu Lin, Hui Fang, Xingwei Wang, Min Huang, Xiushuang Yi, Keping Yu
Abstract
Edge intelligence (EI) entails deploying artificial intelligence algorithms at the network’s edge. Utilizing edge computing infrastructure enables local data processing and decision-making, resulting in decreased latency, bandwidth consumption, and improved privacy security. With increasing the number of Internet of Things (IoT) devices and the development of 6G communication technologies, there is a growing demand for fast, efficient, and low-latency data processing, which has led to the rise of EI. This survey comprehensively reviews and analyzes the current state of research on EI from the perspective of technological development, with a particular focus on the following key aspects: (1) We review the basic concepts of EI and its distinctions from traditional cloud computing and edge computing; (2) We explore the technological framework of EI in detail, including key technologies such as edge computing and federated learning, and analyze how these technologies integrate with modern communication technologies like IoT devices and 6G networks; (3) We discuss the challenges faced when implementing EI technologies, such as data privacy and security issues, device resource constraints, and propose corresponding solutions or research directions. The survey also outlines the main research directions and technical challenges driving the future development of EI, providing valuable insights and guidance for researchers and practitioners in the field.