With the rapid development of high-performance embedded chips, the computing power of edge devices has been greatly enhanced, enabling the devices to handle computation-intensive tasks in real time. More and more artificial intelligence (AI) applications are migrating to edge computing platforms for ensuring real-time response. The combination of edge computing and AI has given rise to edge intelligence, which is an emerging technology that integrates the core capabilities of network, computing, storage, and applications. Edge intelligence can provide users with high-quality intelligent services more efficiently by deploying intelligent algorithms on edge devices closer to users. Compared with AI models deployed on the cloud, edge intelligence has the advantages of lower power consumption, shorter latency, higher security, and closer to users. Therefore, edge intelligence has become a hot research topic in academia in recent years. In addition, Huawei, Google, Microsoft, Amazon, Cisco, and other major global enterprises have also launched their layout on this aspect, in order to accelerate the development of precision agriculture, smart healthcare, smart city, industrial Internet of Things, and other fields and related industries.

The focus of this special issue is to publish new research outcomes that are furthering the level of current research or otherwise contributing to the overall comprehension of edge intelligence related challenges like AI model deployment, task scheduling, resource management, smart design and optimization, collaborative methodologies, and software and systems solutions. The topics include, but are not limited to:

• Embedded systems and software for edge intelligence
• Real-time systems and software for edge intelligence
• Multimodal sensing fusion system and software for edge intelligence
• AI chip design for edge intelligence
• Lightweight AI model design for edge intelligence
• Task scheduling and resource management for edge intelligence
• Low-power and high-reliability designs for edge intelligence
• Embedded AI model training methods
• AI framework design and optimization for edge devices
• Intelligent sensing, interaction, and decision system design
• Edge-edge, edge-cloud, and end-edge-cloud collaborative methodologies
• Model structure optimization, operator optimization, and hardware feature optimization for robotics, UAVs, autonomous driving, etc.
• Security and privacy for edge intelligence systems
• Other edge intelligence systems and applications

Important dates:

Submission deadline: June 30, 2024

Guest editors:

Qingguo Zhou, Lanzhou University, E-mail: zhouqg@lzu.edu.cn

Qingxu Deng, Northeastern University, E-mail: dengqx@mail.neu.edu.cn

Guoqi Xie, Hunan University, E-mail: xgqman@hnu.edu.cn

Xiaoli Gong, Nankai University, E-mail: gongxiaoli@nankai.edu.cn

Junlong Zhou, Nanjing University of Science and Technology, E-mail: jlzhou@njust.edu.cn

Xin Liu, Lanzhou University, E-mail: bird@lzu.edu.cn