Network selection algorithm based on AHP and similarity

In heterogeneous wireless networks, there are various kinds of service demands from the users. A network selection algorithm based onthe analytic hierarchy process （AHP） and Similarity is proposed to solve this problem. The services are divided into three classes： Conversational Class, Streaming Class and Interactive Class. According to the characteristics of each service, a different judgment matrix is assigned and then the AHP method is used to calculate the network attribute weights. Taking the dynamic changes in user demands and network environment into account, a formula based on Lance distance for computing the attributes similarity is derived to evaluate the degree of conformity between user requirements and network attributes, from which the similarity between the user requirements and network attributes is calculated and then the total similarity by weighting. The network with the largest total similarity is the best choice. Simulation results demonstrate the effectiveness of the proposed scheme in improving the quality of service （QoS） according to the user requirements under three kinds of services.

Local attribute-similarity weighting regression algorithm for interpolating soil property values

Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance(SD).However,considering that properties of the neighbors spatially close to the unmeasured point may not be similar,the estimation of properties at the unmeasured one may not be accurate.The present study proposed a local attribute-similarity weighted regression(LASWR)algorithm,which characterized the similarity among spatial points based on non-spatial attributes(NSA)better than on SD.The real soil datasets were used in the validation.Mean absolute error(MAE)and root mean square error(RMSE)were used to compare the performance of LASWR with inverse distance weighting(IDW),ordinary kriging(OK)and geographically weighted regression(GWR).Cross-validation showed that LASWR generally resulted in more accurate predictions than IDW and OK and produced a finer-grained characterization of the spatial relationships between SOC and environmental variables relative to GWR.The present research results suggest that LASWR can play a vital role in improving prediction accuracy and characterizing the influence patterns of environmental variables on response variable.