Multi-modal abstractive summarization is commonly based on the Sequence-to-Sequence （Seq2Seq） framework， and the objective function optimizes the model at the character level， which searches locally optimal results to generate words and ignores the global semantic information of the summary samples. It may cause a problem of semantic deviation between the summary and multimodal information， resulting in factual errors. In order to solve the above problems， a multi-modal summarization model based on semantic relevance analysis was proposed. Firstly， the summary generator based on Seq2Seq framework was trained to generate candidate summaries with semantic multiplicity. Secondly， a summary evaluator based on semantic relevance analysis was applied to learn the semantic differences among candidate summaries and the evaluation mode of ROUGE （Recall-Oriented Understudy for Gisting Evaluation） from a global perspective， so that the model could be optimized at the level of summary samples. Finally， the summary evaluator was used to carry out reference-free evaluation of the candidate summaries， making the finally selected summary sample as similar as possible to the source text in semantic space. Experiments on benchmark dataset MMSS show that the proposed model can improve the evaluation indexes of ROUGE-1， ROUGE-2 and ROUGE-L by 3.17， 1.21 and 2.24 percentage points respectively compared with the current optimal MPMSE （Multimodal Pointer-generator via Multimodal Selective Encoding） model.

Internet of Vehicles （IoV） traffic monitoring requires the transmission， storage and analysis of private data of users， making the security guarantee of private data particularly crucial. However， traditional security solutions are often hard to guarantee real-time computing and data security at the same time. To address the above issue， security protocols， including two initialization protocols and a periodic reporting protocol， were designed， and a Software Guard Extensions （SGX）-based IoV traffic monitoring Secure Data Processing Framework （SDPF） was built. In SDPF， the trusted hardware was used to enable the plaintext computation of private data in Road Side Unit （RSU）， and efficient operation and privacy protection of the framework were ensured through security protocols and hybrid encryption scheme. Security analysis shows that SDPF is resistant to eavesdropping， tampering， replay， impersonation， rollback， and other attacks. Experiment results show that all computational operations of SDPF are at millisecond level， specifically， all data processing overhead of a single vehicle is less than 1 millisecond. Compared with PFCF （Privacy-preserving Fog Computing Framework for vehicular crowdsensing networks） based on fog computing and PPVF （Privacy-preserving Protocol for Vehicle Feedback in cloud-assisted Vehicular Ad hoc NETwork （VANET）） based on homomorphic encryption， SDPF has the security design more comprehensive： the message length of a single session is reduced by more than 90%， and the computational cost is reduced by at least 16.38%.

For the low security and capacity shortages of steganographic methods based on the single data, a new text steganography method with hierarchical security was proposed. First, multiple types of data in the whole cover document were regarded as optional steganographic covers to build up a hierarchical security steganographic model upon the the steganographic security levels defined by taking the characteristics of different types of data and the steganalysis as evaluation criterions. Then, a security level was adaptively determined by the secret message length, and the secret message was embedded into the selected independent different types of data in a cover document with the help of the built model. Theoretical analysis and experimental results show that compared with the steganography based on single data, the proposed method has expanded the steganographic capacity and reduced the modifications of the statistic characteristics of a single type of data in the cover document when the same secret message was embedded. In conclusion the proposed method improves the security of the secret message.

In order to mitigate the network congestion, a novel active queue management algorithm RQQM (Rate and Queue-based Queue Management algorithm) is proposed by particle swarm optimization. In this algorithm, actual queue length is deducted with particle swarm optimization and variation factor, and the dropping strategy and dropping rate are presented based on arrival rate and actual queue length. Then, a simulation with actual data was conducted to study of the algorithm performance between RQQM and RFQM (Rate-based Fair Queue Management algorithm), as well as ABLUE (Adaptive BLUE algorithm). The result shows that it is better adaptability for RQQM.

According to the characteristics of parallel BCH decoder, the multiplication of constant coefficient in finite field was realized by using XOR gates to reduce hardware complexity. The part of the error location polynomial was calculated, and then the remaining error location polynomial could be obtained using the theory of affine polynomial and Gray code. The proposed algorithm reduces the system resources occupied.Through timing simulation on Field Programmable Gate Array (FPGA)'s development software ISE10.1, the high-efficiency of the algorithm on time and space has got verified.