Existing multi-hop clustered routing protocols for Intelligent Road Cone Ad-hoc Network （IRCAN） suffer from redundancy in network control overhead and the average number of hops for data packet transmission is not guaranteed to be minimal. To solve the above problems， combined with the link characteristics of the network topology， an efficient clustered routing protocol based on non-random retroverted clustering， called Retroverted-Clustering-based Hierarchy Routing （RCHR）， was proposed. Firstly， the retroverted clustering mechanism based on central extension and the cluster head selection algorithm based on overhearing， cross-layer sharing， and extending the adjacency matrix was proposed. Then， the proposed mechanism and the proposed algorithm were used to generate clusters with retroverted characteristics around sink nodes in sequence， and to select the optimal cluster heads for sink nodes at different directions without additional conditions. Thus， networking control overhead and time were decreased， and the formed network topology was profit for diminishing the average number of hops for data packet transmission. Theoretic analysis validated the effectiveness of the proposed protocol. The simulation experiment results show that compared with Ring-Based Multi-hop Clustering （RBMC） routing protocol and MODified Low Energy Adaptive Clustering Hierarchy （MOD-LEACH） protocol， the networking control overhead and the average number of hops for data packet transmission of the proposed protocol are reduced by 32.7% and 2.6% at least， respectively.

Aiming at the problem that the imbalance of datasets in credit risk assessment affects the prediction effect of the model， a credit risk prediction model based on Borderline Adaptive Synthetic Minority Oversampling TEchnique （BA-SMOTE） and Focal Loss-Light Gradient Boosting Machine （FLLightGBM） was proposed. Firstly， on the basis of Borderline Synthetic Minority Oversampling TEchnique （Borderline-SMOTE）， the adaptive idea and new interpolation method were introduced， so that different numbers of new samples were generated for each minority sample at the border， and the positions of the new samples were closer to the original minority sample， thereby balancing the dataset. Secondly， the Focal Loss function was used to improve the loss function of LightGBM （Light Gradient Boosting Machine） algorithm， and the improved algorithm was used to train a new dataset to obtain the final BA-SMOTE-FLLightGBM model constructed by BA-SMOTE method and FLLightGBM algorithm. Finally， on Lending Club dataset， the credit risk prediction was performed. Experimental results show that compared with other imbalanced classification algorithms RUSBoost （Random Under-Sampling with adaBoost）， CUSBoost （Cluster-based Under-Sampling with adaBoost）， KSMOTE-AdaBoost （K-means clustering SMOTE with AdaBoost）， and AK-SMOTE-Catboost （AllKnn-SMOTE-Catboost）， the constructed model has a significant improvement on two evaluation indicators G-mean and AUC （Area Under Curve） with 9.0%-31.3% and 5.0%-14.1% respectively. The above results verify that the proposed model has a better default prediction effect in credit risk assessment.

Nutch crawling performance was optimized by tunning Nutch MapReduce job configurations. In order to optimize Nutch performance, firstly Nutch crawling processes were studied from the view of Hadoop. And based on that, the characters of Nutch jobs workflows were analyzed in detail. Then tunned job configurations were generated by profiling Nutch crawling process. The tunned configurations were set before the next job running of the same type. The appropriate profiling interval was selected to consider the balance between cluster environmental error and profiling load, which improved optimization result. The experimental results show that it is indeed more efficient than the original programs by 5% to 14%. The interval value of 5 makes the best optimization result.

Negative Selection (NS) algorithm of artificial immunology has been successfully applied to anomaly detection on some lowdimension data, but the performance becomes unfavorable on highdimension data. Realvalued negative selection algorithm with variablesized detectors (VRNS) was applied to network intrusion detection and a variation of it (IVRNS) was proposed to improve the performance on highdimension data. In the improved algorithm, the detectors were used to control the coverage of them according to the overlap among the detectors. Experimental results prove the effectiveness of this novel algorithm on high-dimension data and a high detection rate with a lower false alarm rate in network intrusion.