Bingyang Li

Cognitive Networks and Its Layered Cognitive Architecture

Cognitive networks become hotspot in the field of network research recently. It is considered a valid solution for heterogeneous networks. In this paper, we propose a new architecture of cognitive networks and divide its cognitive capabilities into three sub-layers named access cognitive layer, network cognitive layer and service cognitive layer. The aim of the design is to implement cognitive capabilities easily and pertinency. Works in this paper indicates directions of cognitive networks research for the future.

Optimal transmission for scalable video coded streaming in cellular wireless networks with the cooperation of local peer-to-peer network

We study the problem of transmitting scalable video coded streams in a hybrid scenario including a global cellular network and a local peer-to-peer network. Given desirable channel condition in cellular downlink, some cellular user equipments can fetch the video chunks via the cellular base station directly, and also provide relay service via the local peer-to-peer network for other user equipments unable to meet their basic video quality. To take the advantage of channel diversity gains, the method of random linear coding is adopted to generate linear combinations of the video chunks for transmission in both the cellular and peer-to-peer networks. To optimize the cellular and peer-to-peer transmission arrangement, we first formulate it as a mixed integer nonlinear programming problem, which becomes more complicated when the number of user equipments increases. Then, we convert it to a quasi-convex optimization problem by approximating the indicator function with a continuous step function, and it can be solved by a centralized approach. Furthermore, a primal-dual decomposition approach is developed and a distributed algorithm is proposed accordingly. Simulation results show that the proposed approach achieves a near-optimal solution.

Dynamic Self-configuration of User QoS for Next Generation Network

Traditional network configuration managements for users’ QoS can achieve well performance to some extent, but all of them could not be applied to the next generation network (NGN) directly, such as integrated services (Intserv) and differentiated services (Diffserv) typically. To solve this issue, we propose a framework of dynamic QoS self-configuration (FDQS) in this paper. Utility function and interruption mechanism are integrated into FDQS where utility function is utilized to express the priorities of users’ QoS and interruption mechanism to dynamically correct those priorities during transmission of data packets. Simulation results show that the proposed FDQS holds the same QoS level with Diffserv in well network state, however when network congestion happens, the level of QoS of FDQS is higher than Diffserv almost by 10%.

Experiment Research of Automatic Deception Model Based on Autonomic Computing

Aiming at the fact that the network deception technology needs manual work and can’t ensure deception efficiency and accuracy, an automatic deception system with autonomic intrusion tolerance based on autonomic computing is proposed. The theory model of the system is researched and the formal description is denoted which includes service transplant module and decoy subnet module. Service transplant module completes the retargeting of suspicious information and recovering of legal access, implementing the security isolation for object system and inputting deception targets to decoy subnet. Trapping subnet realizes automatic deception, feature analysis and autonomic associative study on attack information. The simulation results show the service availability, even response time, average delay, even transmission speed and resource efficiency of the servers with automatic deception are much better than those of servers without automatic deception.