A channel assignment algorithm with awareness of link traffic is proposed in multi-radio multi-channel wireless mesh networks. First, the physical interference model based on the signal-to-interference-plus-noise rati...
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A channel assignment algorithm with awareness of link traffic is proposed in multi-radio multi-channel wireless mesh networks. First, the physical interference model based on the signal-to-interference-plus-noise ratio and successful transmission condition is described. The model is more suitable for a wireless communication environment than other existing models. Secondly, a pure integer quadratic programming （PIQP） model is used to solve the channel assignment problem and improve the capacity of wireless mesh networks. Consequently, a traffic- aware static channel assignment algorithm（TASC） is designed. The algorithm adopts some network parameters, including the network connectivity, the limitation of the number of radios and the successful transmission conditions in wireless communications. The TASC algorithm can diminish network interference and increase the efficiency of channel assignment while keeping the connectivity of the network. Finally, the feasibility and effectivity of the channel assignment solution are illustrated by the simulation results. Compared witb similar algorithms, the proposed algorithm can increase the capacity of WMNs.
To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach co...
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To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.
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