Intimacy Relationship Quantification Method among Nodes Based on Social Attributes in Crowd Sensing

A new method, called social-based nodes intimacy quantification method (SNIQ), is proposed to solve the problem of unclear hierarchy and rough associated attribute description in nodes social relationships quantification. A formal representation of social attributes is presented by means of static semantic hierarchy tree and spatial index coding to analyze multi-dimension factors that influence nodes social relationship. The asymmetry of social relations of nodes is reasonably evaluated by introducing the interactive information entropy. Furthermore, analytic hierarchy process (AHP) is used to achieve two-level judgment and effective aggregation of node dynamic attributes, and a fusion degree of nodes is introduced to dynamically modify intimacy relationships of nodes. Experimental results show that SNIQ is better than the traditional algorithms in accuracy and recall rate in the process of getting nodes intimacy relationships, and effectively improves the success rate of nodes selection in crowd sensing. Comparisons with the existing algorithms show that the proposed SNIQ achieves 14.67% improvement in prediction accuracy.