Cognitive Radio Network (CRN) plays a critical role in achieving better wireless bandwidth utilization and improving the quality of wireless applications. Multicasting in CRN is a challenging problem due to the dynamic nature of spectrum opportunities available to the secondary users. Researchers have proposed a variety of schemes for efficient multicast in cognitive radio networks, including schemes based on optimization theory, network coding, machine learning, and game theory. The algorithms and techniques for solving the multicast problem effectively were summarized, and a comprehensive survey of protocols was given. Finally, future research directions were identified.

In view of the problem that how to select the network dynamically in heterogeneous wireless network environment, an network selection and handover strategy based on context-awareness was proposed. A dynamic network solution and a fuzzy logic handover decision were proposed. Based on them, the strategy chose a selection index to filter those access networks that did not satisfied the requirements and designed a network score function for network ranking calculation. The simulation results show that the proposed handover strategy can select a suitable access network, and effectively use the resources.

To solve the problems of slow convergence speed and easily getting into local optimal value for Artificial Bee Colony (ABC) algorithm, a new quantum optimization algorithm was proposed by combining quantum theory and artificial colony algorithm. This algorithm expanded the quantity of the global optimal solution and improved the probability of achieving the global optimal solution by using Bloch coordinates of quantum bit encoding food sources in the artificial colony algorithm; then food sources were updated by quantum rotation gate. This paper put forward a new method for determining the relationship between the two rotation phases in the quantum rotation gate. When the ABC algorithm searched as the equal area on the Bloch sphere, it was proved that the size of the two rotation phases in the quantum rotation gate approximated to the inverse proportion. This avoided blind arbitrary rotation and made the search regular when approaching the optimal solutions. The experiments of two typical optimization issues show that the algorithm is superior to the common Quantum Artificial Bee Colony (QABC) and the simple ABC in both search capability and optimization efficiency.

Combining the Web services with the workflow, WSBPMS which supported dynamical compounding of Web services was designed and realized. General architecture of WSBPMS and its critical techniques were introduced, including dynamical binding Web services to the workflow, supporting automatic and half-automatic execution of process and optimizing choices of Web services by QoS.