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.

A multi-objective particle swarm optimization with decomposition for network community discovery was proposed and the multi-objective optimization model of community discovery was constructed through comparing the optimization objectives of different community discovery algorithms in social network. The proposed algorithm adopted the Chebyshev method to decompose the multi-objective optimization problem into a number of single-objective optimization sub-problems and used Particle Swarm Optimization (PSO) to discover the community structure. Moreover, a new local search based mutation strategy was put forward to improve the search efficiency and speed up convergence. The proposed algorithm overcame the defects of single objective optimization methods. The experimental results on synthetic networks and real-world networks show that the proposed algorithm can discover the community structure rapidly and accurately and reveal the hierarchical community structure.

Concerning the key problems of selecting threshold function in wavelet threshold denoising, in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients, a continuous adaptive threshold function in the whole wavelet domain was proposed. It fully considered the characteristics of different sub-band coefficients in different scales, and set the energy of sub-band coefficients in different scales as threshold function's initial weights. Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method, so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients. The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image, also achieve higher Peak Signal-to-Noise Ratio (PSNR) under different noise standard deviations.

In system call trace of a process, there are two kinds of invariability, program behavior invariability and user behavior invariability, of which the former can be further subdivided into temporal order invariability and frequency invariability. The existing researches on system call based intrusion detection techniques focus on program behavior invariability only, ignoring user behavior invariability. Based on frequency invariability embedded in process traces, the existence and description of user behavior invariability were studied, on which the least entropy length was proposed to measure the invariability. The experiment on Sendmail datasets shows that, least entropy length excellently describes user behavior invariability and significantly improves the performance of system call anomaly detection with the help of program behavior invariability.

A fault diagnosis model based on Bayesian network to identify the most likely problematic services in a Web service composition process was proposed. The Bayesian network topology construction method and the parameters configuration method were specified respectively in detail. A fault diagnosis algorithm based on the Bayesian network was proposed and the algorithm was analyzed. The experimental simulation show that the model can rule out the root cause of the problems in the Web service composition process effectively and efficiently.