Aiming to the problems of low embedding capacity and poor visual quality of the extracted secret images in existing generative data hiding algorithms， a generative data hiding algorithm based on multi-scale attention was proposed. First， a generator with dual encode-single decode based on multi-scale attention was designed. The features of the cover image and secret image were extracted independently at the encoding end in two branches， and fused at the decoding end by a multi-scale attention module. Skip connections were used to provide different scales of detail features， thereby ensuring high-quality of the stego-image. Second， self-attention module was introduced into the extractor of the U-Net structure to weaken the deep features of the cover image and enhance the deep features of the secret image. The skip connections were used to compensate for the detail features of the secret image， so as to improve the accuracy of the extracted secret data. At the same time， the adversarial training of the multi-scale discriminator and generator could effectively improve the visual quality of the stego-image. Experimental results show that the proposed algorithm can achieve an average Peak Signal-to-Noise Ratio （PSNR） and Structure Similarity Index Measure （SSIM） of 40.93 dB and 0.988 3 for the generated stego-images， and an average PSNR and SSIM of 30.47 dB and 0.954 3 for the extracted secret images under the embedding capacity of 24 bpp.

To solve the problems of large energy consumption and scarcity of spectrum resources in the traditional Internet of Things （IoT）， a Multiple Input Multiple Output-Ambient Backscatter Communication （MIMO-AmBC） system model which is constructed by an ambient backscatter， a Cooperative Receiver （CRx） and ambient Radio Frequency （RF） source was proposed. First， the system model was analyzed by using the Parasitic Symbiotic Radio （PSR） scheme to derive the Signal-to-Noise Ratio （SNR）. Secondly， the approximate expressions for the ergodic rates of the primary link and the backscatter link were derived， and the maximum expression for the ergodic rate of the backscatter link was obtained. Finally， the proposed system model was compared with the traditional cellular network and Commensal Symbiotic Radio （CSR） scheme. The experimental results verify the correctness of the theoretical derivation and give some meaningful conclusions：1） the backscatter link rate increases with the logarithm of the number of receiving antennas and has nothing to do with the number of transmitting antennas； 2） when the SNR is 10 dB， the sum rate of the PSR scheme is higher than those of the traditional scheme and the CSR scheme by 36.8% and 29.9% respectively. Although the primary link rate of the PSR scheme is 5.5% lower than that of the CSR scheme， the ergodic rate of the backscatter link is 7.7 times higher than that of the CSR scheme， which provides theoretical reference for choosing the AmBC symbiosis scheme for practical applications.

The macro handover has caused high packet loss and long handover latency in Hierarchical Mobile IPv6 (HMIPv6) protocol. To solve these problems, this paper proposed a protocol named Tunnel-based Fast Macro-Handover (TBFMH), which introduced the mechanism of tunnel, acquired care-of addresses on the grounds of handover information, conducted duplication address detection in advance and completed local binding update while building the tunnels. The simulation results show that TBFMH can decrease the handover latency by 50% at least and reduce the packet loss rate compared to HMIPv6, which effectively improves the performance in the macro handover.

With the rapid development of the natural gas industry， the demand for efficient and reliable gas well control technologies is increasing day by day. Among the key technologies for improving the gas production efficiency of low-pressure gas wells， the research on the intelligent evaluation system of plunger gas lift is of great significance. In order to improve the accuracy and efficiency of the evaluation system， an intelligent evaluation algorithm of the plunger gas lift based on deep learning was proposed. Employing Convolutional Neural Network （CNN） as the core of the algorithm， the comprehensive evaluation of performance of the plunger lift control algorithm was realized by analyzing key features such as oil pressure， casing pressure， switching well status， and production regulations. Extensive test results on actual gas well data demonstrated that the algorithm can effectively improve the accuracy and stability of evaluation. Specifically，compared with Bayesian neural network （BNN） and Attention-LSTM algorithm， the algorithm improves the prediction accuracy of normal rate by 14% and 5%， respectively， and improves the prediction accuracy of stable running rate by 6% compared with backpropagation neural network （BPNN）.