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.

Vehicle detection and identification and safe distance keeping technology have become the main content of current intelligent transportation system research. Among them, vehicle detection and recognition is one of the most important research contents, and it is also crucial to the safe driving of vehicles. Real-time detection and recognition of current vehicles can effectively prevent the occurrence of malignant traffic accidents such as rear-end collision. Because the infrared image has some shortcomings such as poor contrast, loud noise, and blurred edge, this paper mainly studies the color space preprocessing of the image and uses threshold segmentation method and infrared image enhancement to segment the front vehicle and background. That is to say, by analyzing the infrared image captured by infrared CCD, we use median filter to remove noise from the collected infrared image and then use the improved histogram equalization to enhance the contrast of the image. Vertical Sobel operator is selected to enhance the vertical edge of the image, and the image is segmented by binary segmentation method. Finally, vehicle detection and recognition are realized by vertical edge symmetry, aspect ratio, and gray-scale symmetry. The experimental image and experimental data analysis results show that the image processing technology studied in this paper has achieved the intended research purpose.