In edge computing， computing resources are deployed at edge computing nodes closer to end users， and selecting the appropriate edge computing node deployment location from the candidate locations can enhance the node capacity and user Quality of Service （QoS） of edge computing services. However， there is less research on how to place edge computing nodes to reduce the cost of edge computing. In addition， there is no edge computing node deployment algorithm that can maximize the robustness of edge services while minimizing the deployment cost of edge computing nodes under the constraints of QoS factors such as the delay of edge services. To address the above issues， firstly， the edge computing node placement problem was transformed into a minimum dominating set problem with constraints by building a model about computing nodes， user transmission delay， and robustness. Then， the concept of overlapping domination was proposed， so that the network robustness was measured on the basis of overlapping domination， and an edge computing node placement algorithm based on overlapping domination was designed， namely CHAIN （edge server plaCement algoritHm based on overlAp domINation）. Simulation results show that CHAIN can reduce the system latency by 50.54% and 50.13% compared to the coverage oriented approximate algorithm and base station oriented random algorithm， respectively.

Convolutional Neural Networks （CNN） are used for image forensics because of their high recognizable property， easy understanding， and strong learnability. However， their inherent disadvantages of the receptive field increasing slowly and neglecting long-range dependencies， and high computational cost cause the unsatisfactory accuracy and lightweight deployment of deep learning algorithms. To solve the above problems， a lightweight network-based image copy-paste tamper detection algorithm namely LKA-EfficientNet （Large Kernel Attention EfficientNet） was proposed. The characteristics of long-range dependencies and global receptive field were contained in LKA-EfficientNet， and the number of EfficientNetV2 parameters was optimized. As a result， the localization speed and detection accuracy of image tamper were improved. Firstly， the image was inputted into and processed in the backbone network based on Large Kernel Attention （LKA） to obtain the candidate feature maps. Then， the feature maps of different scales were used to construct the feature pyramid for feature matching. Finally， the candidate feature maps after feature matching were fused to locate the tampered area of the image. In addition， the triple cross entropy loss function was used by LKA-EfficientNet to further improve the accuracy of the algorithm in image tamper localization. Experimental results show that LKA-EfficientNet can not only reduce the floating-point operations by 29.54% but also increase the F1 by 4.88% compared to the same type algorithm — Dense-InceptionNet. The above verifies that LKA-EfficientNet can reduce computational cost and maintain high detection performance at the same time.