As the primary driver of intelligent mobile applications, deep neural networks (DNNs) have
gradually deployed to millions of mobile devices, producing massive latency-sensitive and …

Deep neural network (DNN) inference poses unique challenges in serving computational
requests due to high request intensity, concurrent multi-user scenarios, and diverse …

Unrestricted adversarial examples allow the attacker to start attacks without given clean
samples, which are quite aggressive and threatening. However, existing works for …

Deep neural network (DNN) is a promising technology for Internet of Visual Things (IoVT)
devices to extrct their visual information from unstructured data. However, it is hard to deploy …

As a driving force in the advancement of intel-ligent in-orbit applications, DNN models have
been gradually integrated into satellites, producing daily latency-constraint and computation …

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-
latency, and high-reliable intelligent services, but also enable intelligent maintenance and …

As the product of the combination of edge computing and artificial intelligence, edge
intelligence (EI) not only solves the problem of insufficient computing capacity of the end …

In 5G smart cities, edge computing is employed to provide nearby computing services for
end devices, and the large-scale models (eg, GPT and LLaMA) can be deployed at the …

With the increasing prevalence of edge devices and the exponential growth of deep learning
applications, there is a pressing need for efficient algorithms and techniques that can be …

A Complete Bibliography of ACM Transactions on Sensor Networks Page 1 A Complete
Bibliography of ACM Transactions on Sensor Networks Nelson HF Beebe University of Utah …