Social recommendation aims to improve recommendation effect of traditional recommendation algorithms by integrating social relations. Currently， social recommendation algorithms based on Network Embedding （NE） face two problems： one is that inconsistency between objects is not considered when constructing network， and algorithms are often restricted by positive objects that are difficult to obtain and have many constraints； the other is that the elimination of overfitting in algorithm training process based on the number of ratings cannot be realized by these algorithms. Therefore， an Adaptive Social Recommendation algorithm based on Negative Similarity （ASRNS） was proposed. Firstly， homogeneous networks with positive correlations were constructed by consistency analysis. Then， embedded vectors were obtained by combining weighted random walk with Skip-Gram algorithm. Next， similarities were calculated， and Matrix Factorization （MF） algorithm was constrained from the perspective of negative similarity. Finally， the number of ratings was mapped to the ideal rating range based on adaptive mechanism， and different penalties were imposed on bias terms of the algorithm. Experiments were conducted on FilmTrust and CiaoDVD datasets. The results show that compared with algorithms such as Collaborative User Network Embedding （CUNE） algorithm and Consistent neighbor aggregation for Recommendation （ConsisRec） algorithm， ASRNS has the Root Mean Square Error （RMSE） reduced by at least 2.60% and 5.53% respectively， and the Mean Absolute Error （MAE） reduced by at least 1.47% and 2.46% respectively. It can be seen that ASRNS can not only reduce rating prediction error effectively， but also improve over-fitting problem in algorithm training process significantly， and has good robustness for objects with different ratings.

Refined short-term traffic flow prediction is the premise to ensure the rational decision making in Intelligent Transportation System （ITS）. In order to establish the lane-changing model of self-driving car， predict vehicle trajectories， and guide vehicle routes， the timely traffic flow prediction for each lane has become an urgent problem to solve. However， refined short-term traffic flow prediction faces the following challenges： first， with the increasing diversity of traffic flow data， the traditional prediction methods cannot meet the requirements of ITS for high precision and short time delay； second， training prediction model for each lane make a huge waste of resources. To solve the above problems， a refined short-term traffic flow prediction model combined Convolutional-Gated Recurrent Unit （Conv-GRU） with Grey Relational Analysis （GRA） was proposed to predict lane flow. Considering the characteristics of long training time and relatively short reasoning time of deep learning， a cloud-fog deployment scheme was designed. Meanwhile， to avoid training prediction models for each lane， a model migration deployment scheme was proposed， which only needs to train the prediction model of some lanes， and then the trained prediction models were migrated to the associated lane for prediction through GRA. Experimental results of extensive comparisons on a real-world dataset show that， compared with traditional deep learning prediction methods， the proposed model has more accurate prediction performance； compared with Convolutional-Long Short-Term Memory （Conv-LSTM） network， the model has shorter running time. Furthermore， the model migration is realized by the proposed model under the condition of ensuring high-precision prediction， which saves about 49% of training time compared to training prediction model for each lane.

In the real-time large data applications of Peer-to-Peer (P2P), to avoid free-riding behavior in the Video on Demand (VOD) system, a new incentive scheme of auction algorithm based on the discriminatory second price was proposed. The nodes obtained the video data block they needed using distributed dynamic auction between nodes. In auction, the bidding node firstly determined whether the budget was enough to bid based on discrimination rule, and set the upload bandwidth according to the number of bidding nodes. Secondly, the winner node was determined by the bid price. Finally, the bidding node paid the auction node according to the second highest price after it got the data block as its income. Analysis of the revenue of nodes, the bandwidth utilization and the proportion of selfless or selfish nodes indicate that the proposed scheme can effectively motivate nodes to take active part in sharing of video data blocks, and make efficient use of the upload bandwidth at the same time.

Traditional Collaborative Filter (CF) often suffers from the shortage of historic information. A transfer-Bagging algorithm based on hybrid classifiers was proposed for question recommendation. The main idea was that the recommendation and prediction problem were cast into the framework of transfer learning, then the users' demand for recommend questions were treated as target domain, while similar users who had applicable historic information were employed as auxiliary domain to help training target classifiers. The experimental results on both question recommendation platform and popular open datasets show that the accuracy of the proposed algorithm is 10%-20% higher than CF, and 5%-10% higher than single Bagging algorithm. The method solves cold start-up and sparse data problem in question recommendation field, and can be generalized into production recommendation on E-commerce platform.

In order to deal with the problems that decision information is intuitionistic fuzzy and attribute weights are unknown, a decisionmaking method based on intuitionistic fuzzy entropy and score function was proposed. Firstly, a new concept of intuitionistic fuzzy entropy was presented to measure the intuitionism and fuzziness of intuitionistic fuzzy sets, and relevant properties were also discussed. Secondly, to decrease decision effects of uncertain information, a programming model was constructed to determine attribute weights combined with intuitionistic fuzzy entropy. Meanwhile, in the view of membership, nonmembership and hesitancy degree, correlation coefficients between objects of the universe and the ideal object were constructed, and according to decision makers attitude, the optimal decision was obtained by defining the score function. Finally, the article proposed a multiple attribute decision making method on intuitionistic fuzzy information, and the feasibility and effectiveness of the method are verified through a case study of candidates evaluation.

To meet the need of instruction detection system used in complex natural environment, such as coastal mudflats, an improved barrier coverage model, a multi-line barrier coverage scheduling protocol named k-MLBCSP, a coverage layout algorithm and a coverage adjustment algorithm were proposed. The k-MLBCSP protocol divided the network lifetime into three phases. In the initialization phase, the coverage layout algorithm guaranteed reasonable network settings. In the adjustment phase, the coverage adjustment algorithm provided an effective way for the sink and alive senosrs to further negotiate coverage layout strategies. The theoretical analysis and simulations show that compared with LBCP and RIS, k-MLBCSP increases the sensor network's coverage probability and lifetime. Furthermore, k-MLBCSP reduces the time complexity and the network load.