Dear Colleagues,

With the development of the Internet of Things (IoT) and mobile communication technology, huge data can be obtained from various intelligent devices, e.g., smartphones, smart cameras, wearable devices, environmental sensors, household appliances, and vehicles. The massive amount of data has promoted the development of artificial intelligence, especially the performance of deep neural networks, which has been significantly improved. Meanwhile, artificial intelligence helps IoT/mobile devices to make decisions and make them more intelligently.

Edge computing is a feasible and promising technique to meet the challenges. It places a large number of computing nodes near the terminal device to meet the high computing and low latency requirements of deep learning applications. It also provides additional benefits in terms of bandwidth efficiency, privacy, and scalability. However, the edge computing system is much more resource-sensitive than the cloud side, and thus, a more efficient sensing network model is necessary.

Considering the distribution and heterogeneity of edge computing systems, it brings great challenges to the design of efficient sensing networks for edge computing. The current network design considers less the scenarios and frameworks where the model is to be deployed and does not regard the design of efficient models for edge computing systems as a specific research topic. Therefore, efficient sensing network designs should be deeply investigated on edge computing scenarios. This Special Issue will bring together academic and industrial researchers to identify and discuss technical challenges and recent results related to the efficient sensing network design for convergence of AI and edge computing.

Combining AI and edge computing empowers the Internet of Things (IoT) and mobile devices with intelligence and brings a tremendous impact on people’s lives. For the characteristics of edge computing, the AI learning models to be deployed should be high-performance and efficient, but recent efficient sensing network design research hardly considers the particularity of edge computing. From our perspective, much more work should concentrate on this issue. This Special Issue will encourage academic and industrial researchers to focus on research on efficient sensing network theory and methods at the intersection of AI and edge computing. Due to its uniqueness and timeliness, we expect a large number of submissions to this Special Issue.

Topics of interest include but are not limited to the following:

• Compact and high-performing sensing network design for edge computing;
• Inference efficiency improvement for edge computing;
• Automatic machine learning methods for sensing networks on edge computing;
• Reasonable evaluation methods of efficiency of sensing network algorithms and architectures on edge computing platforms;
• Sensing network training method for edge computing;
• Efficient sensing network software infrastructure for edge computing;
• Hardware design for inference and training of sensing networks on edge computing systems;
• Other topics for efficient network design for AI and edge computing.

Prof. Dr. Shiping Wen
Prof. Dr. Zhongkai Feng
Prof. Dr. Cihui Yang
Guest Editors

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• edge computing
• artificial intelligence
• sensing network
• IoT
• deep learning