Abstract: Mobile device camera sensing is one of the main forms of mobile crowdsensing. The context information clustering of photos in advance can reduce the similarity calculation of image features and improve the efficiency of redundant judgment of photos. In order to improve the accuracy of context information clustering, this paper proposes a clustering dynamic search algorithm, which solves the problem of dynamic clustering near edge similarity. Firstly, according to whether the PTree clustering algorithm clusters to the existing interval, it is divided into real branches and virtual branches. The data of real branches and leaves are uploaded directly, and the virtual branches and leaves are further dynamically searched for the best similarity matching interval; then, based on the idea of using local expansion and then dynamic reduction, the average distance between dynamic clustering data points is reduced; finally, the redundant photos are removed by clustering to the same interval image set. By collecting photo data with context information through the designed APP, the results show that, compared with the existing schemes, the number of photos to be uploaded can be reduced and the effect of redundancy can be improved.

Key words: mobile crowdsensing, clustering algorithm, heterogeneous feature, data quality