Study On Information Quality Framework And QoS Classification Algorithm In Cognitive Network

Context is a capability can be used to describe any information that capable of characterizing some certain aspect of an entity environment. An entity can be a person, a place, and interacted objects thought to be used between users and applications, including users and applications themselves. If a system could use context to adapt its behavior towards users’transactions, then this system is considered to be context-aware. Cognitive network refers to network that is able to sense environment and to adjust inside network implementation actively according to the understanding and study of the outside environment, so as to adapt to the changing outside environment intelligently. The main points of cognitive network include environment analysis and cognition, environment self-adaption theory and network organization implementation. It can be concluded that cognitive network has the attribute of context awareness.Current study of context-aware information mainly focuses on uniform calculation based on neutral network and fuzzy algorithm by uniting the variables. However, the completeness and accuracy of information with this method still lack of prove. Traditional networks establish their standard of Qos classification according to different parameter requirements respectively. However, due to the highly-heterogeneous and highly-complex cognitive networks environment, information sensed and collected could be of multiple kinds and a major quantity, resulting in an unpredictable situation for Qos classification. It’s necessary to classify Qos information adapting to changing networks environment dynamically, so as to optimize the Qos of cognitive networks. Based on the proposal of the issues of cognitive network above, this paper makes a deep study on information quality and QoS classification. This paper proposes an efficient information quality framework to analyze the timing of information processing as well as proposes a dynamic QoS classification algorithm for cognitive network, which is proved with mathematic simulation, so as to provide more reasonable and efficient methods for improving information quality and QoS in cognitive network.