Study On Study On Context-aware Modeling And Cluster Head Election Algorithm Of Cognitive Networks

As technology advances, networks are increasingly sophisticated and widely used, the capacity of wireless communication has also been an unprecedented growth, leading to increasingly complex communications network. Its complexity is mainly reflected in three aspects:The first aspect is that the network is heterogeneous; the second aspect is that the networking method is heterogeneous; last aspect is the heterogeneity of the terminal. Faced with such a complex heterogeneous environment, in order to achieve efficient integration of existing technology has clearly not enough, so the academic community proposed the cognitive network (Cognitive Network, CN) concept. Cognitive Network has been widely studied as the development direction of the future network. In this paper, we use the context-aware technology which is one of the key technologies in cognitive network to build a context-aware system model, and the model used marked model to express the context. XML language widely used because of the network, and has the inherent cross-platform advantages, so using XML as a mark model modeling language can effectively make the system has the versatility and cross-platform. This also bring a serious problem, that is, the upper application becomes more trouble when retrieving obtain contextual information, in order to solve this problem, the paper proposes CLISA algorithm, through the keywords semantic extracted in context information, it establishes retrieval, then using virtual context, that is context environment semantics of the keywords in the context information, and Deway encoding can be fast access to relevant information. In the three directions of network optimization based cognitive network research, this article choose the life cycle optimization to research and propose an energy balance of cluster head election based on the context algorithm,, it can be effective calculate the remaining energy on the basis of access to data in the use of CLISA, then use the remaining energy to select cluster head node. The experiments show that the algorithm can effectively balance the network node energy and prolong network lifetime.