Distributed edge intelligence is a disruptive research area that enables the execution of
machine learning and deep learning (ML/DL) algorithms close to where data are generated …

The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant
traffic data, some of which is encrypted malicious traffic, creating a significant problem for …

Edge intelligence, Artificial Intelligence (AI) on the edge can have a significant impact on
modern Community Area Network (CAN). This paper proposes an edge intelligence method …

By taking advantages of graphs and potential functions, a security reinforcement method for
edge computing terminals is proposed in this paper. A risk graph of the terminal security …

Abstract Recently, training CNNs (Convolutional Neural Networks) on-chip has attracted
much attention. With the development of the CNNs, the proportion of the BN (Batch …

Edge computing offsets the computing power devices to close the terminals, which avoids
delay caused by long-distance transmission and network congestion under the cloud …

In a smart grid system, an FDI attacker can construct a false data injection attack vector
without changing the measurement residual, and traditional bad data detection methods …

It is important to predict microgrid user's behaviour and the power generation quota of the
microgrid for the optimal interaction of power distribution between microgrid generators and …

Recently, training CNNs (Convolutional Neural Networks) on-chip has attracted much
attention. With the development of the CNNs, the proportion of the BN (Batch Normalization) …

The digital age has seen explosion in data generated, and with this rise in data comes the
need for intelligent systems to interpret and make decisions. This has created a wider …