关键词: 群智感知, 特征因子, 最优生成树, 节点合并, 社区调整

Abstract:

The sensing tasks are mainly distributed through the community division in the crowd sensing application. However, the community discovery algorithm in the existing crowd sensing application lacks the quantification of social relations and the characteristic factors of the discovery community are single. In order to resolve these problems, a community detection algorithm based on multi-dimensional social relationship feature is proposed. By calculating the optimal spanning tree, the node merging factor and the community adjustment factor between mobile users, the social relations of the mobile nodes are specifically quantified and the users are divided into different communities. The experimental results show that compared with the existing methods, the algorithm has better dynamic adaptability, validity and prediction accuracy in different data sets and the accuracy is 97.3% on average, which is higher than the contrast algorithm by 15.2%.

Key words: crowd sensing, feature factor, optimal path tree, node merging, community adjustment