With the rapid development of network and communication technology, the interaction of various information data is more and more frequent, and people pay more and more attention to information security. The information encryption algorithm is a research hotspot in the field of information security. The Advanced Encryption Standard (AES) algorithm has been widely used in the field of information security with its high security and encryption efficiency. This paper mainly introduces the optimization of the AES-128 encryption algorithm of the security layer in ZigBee networking of the Internet of Things. By analyzing the principles of ZigBee networking and the AES encryption algorithm, the changes are optimized. In this paper, the new S-box cryptographic properties are used after analysis and calculation. The affine transformation period, the number of iteration cycles, and the algebraic expression of the S-box are improved. Its cryptographic properties are better than the S-box of the original algorithm, and the security of the algorithm is improved. In the theory of column hybrid algorithm, the computational complexity is reduced by changing the fixed polynomial, and the efficiency of the column hybrid algorithm is improved. In this paper, the performance of the improved AES algorithm is tested. The results show that, in the power consumption curve experiment, the recovery success rate of the original algorithm is about 42%, and the recovery success rate of the improved algorithm is nearly 60%. The improved algorithm is faster than the original algorithm in achieving a recovery success rate of 100%. Experimental results show that the design can accurately complete the encryption and decryption of the AES algorithm, and the performance is better than the original algorithm, which proves the overall superiority of the algorithm.
The advent of deep learning has enabled image super-resolution (SR) technology to achieve tremendous success. The recent exploration between convolutional neural network (CNN) and transformer has produced an impressive performance. However, the huge model size and heavy computational cost occupied by most approaches cannot be ignored. Additionally, the global and local modeling of image features is not well utilized and explored, which is important for texture content reconstruction. In this study, we present an efficient and lightweight network based on CNN and transformer for image SR, dubbed dual-aware transformer network (DATN). Specifically, a dual-aware fusion module (DAFM), consisting of spatial-aware adaptive block (SAAB) and global-aware fusion block (GAFB), learns local and global features in a complementary manner for yielding abundant content details. SAAB delivers the captured spatial information to GAFB in parallel, whereas GAFB establishes long-range spatial dependencies among diverse features to boost the network's ability for recovering texture details. Further, in the upsampling stage, our proposed transformer-empowered upsampling module aggregates global information from the transformer module to generate location-wise reassembly kernels for content-aware upsampling. In such way, feature-rich information is highlighted adaptively to reconstruct high-quality images with maximum efficiency. Experimental results reveal that our proposed DATN achieves high qualitative performance while maintaining low computational demands, surpassing most state-of-the-art SR networks.
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